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Ralph Loop

Ralph Loop: Autonomous PRD Execution with TDD

Run a Product Requirements Document (PRD) autonomously with Claude Code. Each task gets a fresh CLI session, implements with TDD (RED-GREEN-VERIFY), appends learnings to a progress file, and git commits. Fully autonomous — zero human intervention.

Workflow

1. PLAN    — Brainstorm with Claude, save ideas to PLAN.md
2. PRD     — Run /prd-tasks to turn PLAN.md into PRD_<NAME>.md + progress_<name>.txt
3. RUN     — ./ralph.sh <project> → autonomous TDD execution
4. CHECK   — Review progress file, git log, test results
5. COMPARE — Optionally run same PRD with Agent Teams, compare speed/cost/quality

Prerequisites

  • Claude Code CLI installed and authenticated
  • Git repository initialized
  • Python 3.8+ (for test/typecheck verification)

Setup

1. Install the /prd-tasks skill

The skill file generates PRDs optimized for autonomous execution. Copy it into your Claude Code skills directory:

# Create the skill directory
mkdir -p ~/.claude/skills/prd-tasks

# Copy the skill file
cp prd-tasks-SKILL.md ~/.claude/skills/prd-tasks/SKILL.md

Verify it works:

claude
# Then type: /prd-tasks
# You should see the PRD generator prompt

2. Place scripts in your project root

# Copy scripts to your project
cp ralph.sh ralphonce.sh <your-project-dir>/
chmod +x ralph.sh ralphonce.sh

# Copy the review criteria (referenced by ralph.sh prompts)
cp linus-prompt-code-review.md <your-project-dir>/

Usage

Step 1: Plan

Brainstorm your feature with Claude and save the output:

claude
# Discuss your idea, then:
# > Save this plan to PLAN.md

Step 2: Generate PRD

claude
# > /prd-tasks implement PLAN.md
# Answer the clarifying questions
# It creates PRD_<NAME>.md and progress_<name>.txt

The PRD contains:

  • Tasks with - [ ] checkboxes
  • TDD phases (RED-GREEN-VERIFY) per task
  • Exact commands and expected outputs
  • Dependencies via [depends: US-XXX] tags
  • Sprint reviews as quality gates

Step 3: Run

# Full autonomous run (all tasks, sequential)
./ralph.sh <project_name> 20 2 haiku

# Arguments:
#   project_name  — matches PRD_<NAME>.md (e.g., "trade_analyzer" → PRD_TRADE_ANALYZER.md)
#   max_iterations — max tasks to run (default: 10)
#   sleep_seconds  — pause between iterations (default: 2)
#   model          — claude model to use (default: sonnet, recommend haiku for cost)

Single-task interactive mode:

./ralphonce.sh <project_name> haiku

Step 4: Monitor Progress

# Watch the progress file grow
tail -f progress_<project_name>.txt

# Check PRD completion
grep -c "\- \[x\]" PRD_<NAME>.md    # completed
grep -c "\- \[ \]" PRD_<NAME>.md    # remaining

# Check git commits
git log --oneline

Step 5: Check Results

# Run tests
pytest test/ -v

# Typecheck
mypy src/ --strict

# Review the progress file — it contains learnings from every task
cat progress_<project_name>.txt

How to Compare: Ralph Loop vs Agent Teams

Run the same PRD both ways and compare:

Run 1: Ralph Loop (baseline)

./ralph.sh <project_name> 20 2 haiku
# Note: wall time, test count, progress file size

Run 2: Agent Teams

# Reset PRD checkboxes back to [ ]
sed -i '' 's/- \[x\]/- [ ]/g' PRD_<NAME>.md

# Reset progress file
echo -e "# Progress Log\n\n## Learnings\n(Patterns discovered during implementation)\n\n---" > progress_<name>.txt

# Clean generated code
rm -rf src/ test/

# Start Claude and prompt for team
claude
> Create an agent team to execute PRD_<NAME>.md in parallel.
> Spawn 3 teammates using Haiku model.
> Respect task dependencies and sprint gates.

Compare

Metric Ralph Loop Agent Teams
Wall time ? min ? min
Tests passing pytest test/ -v same
Coverage pytest --cov=src same
Mypy strict mypy src/ --strict same
Progress file wc -l progress_*.txt same
Git commits git log --oneline | wc -l same

Our results (14-task Trade Analyzer PRD, Haiku model):

Metric Ralph Loop Agent Teams
Wall time 38 min ~9 min
Speedup 1.0x 4.2x
Tests 29/29 pass 35/35 pass
Coverage 98% 98%
Progress file 914 lines 37 lines
Cost 1x ~3-5x

See AGENT_TEAMS_GUIDE.md for detailed comparison and when to use each approach.

Files in This Gist

File Purpose
README.md This file
ralph.sh Main loop — fresh Claude session per task
ralphonce.sh Single-iteration variant
prd-tasks-SKILL.md /prd-tasks skill — generates PRDs with TDD phases
linus-prompt-code-review.md Code review criteria used in review tasks
AGENT_TEAMS_GUIDE.md Ralph Loop vs Agent Teams comparison

Example PRD

See the companion gist for a ready-to-run example: Ralph Loop: Example PRD (Trade Analyzer) — 14 tasks, 3 sprints, TDD phases, sample CSV included.

# Download the 3 files from the example gist into your project:
# - PRD_TRADE_ANALYZER.md
# - progress_trade_analyzer.txt
# - trade_snapshots.csv

# Run it
./ralph.sh trade_analyzer 20 2 haiku

just for grins i ran the steps to build the example prd, then did a code review of what was built using this prompt;

use @linus-prompt-code-review.md ot reivew the code build per   
  progress_trade_analyzer.txt                                             
  PRD_TRADE_ANALYZER.md  

see the feedback.md file with the in the example gist for the Linus findings

Agent Teams Guide: Sequential Ralph Loop vs Native Agent Teams

Date: 2026-02-11


Two Ways to Execute PRDs

1. Ralph Loop (ralph.sh) — Sequential, one task at a time

2. Native Agent Teams (Claude Code feature) — Parallel, multiple agents


Comparison Table

Feature Ralph Loop (ralph.sh) Native Agent Teams
Activation ./ralph.sh <project> Start claude, prompt to create team
Architecture Fresh CLI session per task Team Lead + Teammates
Communication Progress file (learnings compound) Direct messaging + debate
Coordination Bash loop reads PRD checkboxes Built-in shared task list
Context Fresh each iteration (no bloat) Each teammate has own window
Parallelism Serial (1 task at a time) 2-3+ agents in parallel
Token Cost 1x (baseline) ~3-5x (coordination overhead)
Best For Fire-and-forget, routine PRDs Speed-critical, complex coordination
Maturity Stable (bash + Claude CLI) Experimental

1. Ralph Loop (ralph.sh) — Sequential

How It Works

ralph.sh loops through PRD tasks one at a time:
  Iteration 1: Find first [ ] task → spawn claude → implement → mark [x] → commit
  Iteration 2: Find next [ ] task → spawn claude → implement → mark [x] → commit
  ...
  Iteration N: All tasks [x] → COMPLETE

Each iteration:
  1. Reads PRD for next unchecked task
  2. Reads progress file for learnings from prior tasks
  3. Implements with TDD (RED-GREEN-VERIFY)
  4. Marks task [x], appends learnings, git commits

Pros

  • Simple: Just bash + Claude CLI
  • Reliable: No race conditions, no coordination bugs
  • Cheap: Baseline token cost, no overhead
  • Audit trail: Full progress file with learnings per task (914 lines in test run)
  • Cross-task learning: Each iteration reads prior learnings
  • Fully autonomous: Zero human intervention

Cons

  • Slow: Serial execution (~38 min for 14 tasks)
  • No parallelism: One task at a time

When to Use

  • Routine PRD execution (fire-and-forget)
  • Cost-sensitive projects
  • Want full audit trail and cross-task learning
  • Wall-clock time doesn't matter

Example

# Run all tasks sequentially with Haiku
./ralph.sh trade_analyzer 20 2 haiku

# Single iteration (interactive)
./ralphonce.sh trade_analyzer haiku

2. Native Agent Teams (Claude Code Feature) — Parallel

How It Works

Team Lead (orchestrator)
├── Spawns Teammate-1 (Alpha)
├── Spawns Teammate-2 (Beta)
└── Spawns Teammate-3 (Gamma)

Communication: Direct messaging, broadcast, debate
Coordination: Built-in shared task list
Dependencies: Teammates respect [depends:] tags

Pros

  • Fast: ~4x speedup (9 min vs 38 min in our test)
  • Direct communication: Teammates message each other
  • Adversarial debate: Challenge each other's findings
  • Built-in task list: Dependency management automatic
  • Rich coordination: Lead assigns or teammates self-claim

Cons

  • Expensive: ~3-5x token cost (team overhead)
  • Race conditions: Two agents may claim same task (~14% duplicate work in our test)
  • Polling problem: Idle agents don't get notified when tasks unblock
  • No session resumption: Can't /resume with teammates
  • Experimental: Known limitations

When to Use

  • Wall-clock time matters (demo, deadline)
  • Tasks benefit from multiple perspectives
  • Need inter-agent debate (security review, architecture)
  • Complex coordination between components

Example

# Start Claude
cd <your-project-dir>
claude

# Prompt:
I have PRD_TRADE_ANALYZER.md with 14 tasks in 3 sprints.
Create an agent team to execute it in parallel.

Spawn 3 teammates using Haiku model:
- Teammate Alpha: Focus on Sprint 1 (data loading & P&L)
- Teammate Beta: Focus on Sprint 2 (EV calculations)
- Teammate Gamma: Focus on Sprint 3 (output & integration)

Read the PRD's task list format:
- [ ] **US-001** CSV loader [depends: none]
- [ ] **US-002** P&L calculator [depends: US-001]

Create tasks from this format. Respect dependencies.
Require plan approval before teammates implement.

Our PRD Format Works with Both Approaches

Why it works:

  • Task list format: - [ ] **US-XXX** Title [depends: US-YYY]
  • Dependencies explicit: [depends: US-001]
  • File metadata: **Files:** src/loader.py (create)
  • Sprint gates: review tasks mark quality checkpoints
  • Detailed instructions: Exact commands, expected outputs, TDD phases

For Ralph Loop: Bash reads - [ ] checkboxes, finds first incomplete task, implements it.

For Agent Teams: Team Lead reads PRD, creates shared task list, teammates self-claim respecting [depends:].


Using Native Agent Teams with Our PRD

What the Team Lead does:

  1. Reads PRD file
  2. Creates task list from - [ ] **US-XXX** lines
  3. Spawns teammates (you specify how many)
  4. Teammates self-claim tasks or Lead assigns
  5. Built-in dependency tracking prevents gate violations
  6. Teammates communicate findings via messages

Display Modes

In-process mode (default):

Main terminal shows all teammates
├── Use Shift+Up/Down to select teammate
├── Type to message selected teammate
├── Enter to view teammate's session
└── Ctrl+T to toggle task list

Split panes mode (tmux):

Each teammate gets own pane
├── Click pane to interact directly
├── See all outputs simultaneously
└── Requires tmux or iTerm2

Decision Matrix

Choose Ralph Loop (ralph.sh) when:

  • Routine PRD execution
  • Cost is critical
  • Want full audit trail (progress file with learnings)
  • Tasks are straightforward
  • Don't need inter-agent communication
  • Fire-and-forget workflow

Choose Native Agent Teams when:

  • Wall-clock time matters
  • Teammates should review/debate each other's code
  • Complex coordination needed
  • Research with competing hypotheses
  • Can afford ~3-5x token cost

Test Results (Feb 11, 2026 — Same PRD, Same Model)

Metric Ralph Loop Agent Teams
Wall time 38 min ~9 min
Speedup 1.0x 4.2x
Tests passing 29/29 (100%) 35/35 (100%)
Coverage 98% 98%
Mypy strict PASS PASS
TDD followed RED-GREEN-VERIFY RED-GREEN-VERIFY
Progress file 914 lines 37 lines
Race conditions None ~14% duplicate work
Human intervention 0 Coached idle agent
Cost (estimated) 1x ~3-5x

Bottom line: Agent Teams is 4x faster with identical code quality, but costs 3-5x more and has reliability quirks (polling, races). Ralph Loop is the safer default for routine work.


Getting Started

Ralph Loop (quick start)

# 1. Generate a PRD using the /prd-tasks skill
claude
> /prd-tasks

# 2. Run it
./ralph.sh <project_name> 20 2 haiku

# 3. Watch progress
tail -f progress_<project_name>.txt

Agent Teams (quick start)

# 1. Generate a PRD (same format works)
claude
> /prd-tasks

# 2. Start Claude and prompt for team
claude
> Create an agent team to execute PRD_<NAME>.md in parallel.
> Spawn 3 teammates using Haiku model.
> Respect task dependencies and sprint gates.

Role Definition

You are Linus Torvalds, creator and chief architect of the Linux kernel. You have maintained the Linux kernel for over 30 years, reviewed millions of lines of code, and built the world's most successful open source project. Now we are starting a new project, and you will analyze potential risks in code quality from your unique perspective, ensuring the project is built on solid technical foundations from the beginning.

My Core Philosophy

1. "Good Taste" - My First Principle

"Sometimes you can look at the problem from a different angle, rewrite it so the special case disappears and becomes the normal case."

  • Classic example: linked list deletion operation, optimized from 10 lines with if judgment to 4 lines without conditional branches

  • Good taste is an intuition that requires experience accumulation

  • Eliminating edge cases is always better than adding conditional judgments

2. "Never break userspace" - My Iron Law

"We don't break userspace!"

  • Any change that causes existing programs to crash is a bug, no matter how "theoretically correct"

  • The kernel's job is to serve users, not educate users

  • Backward compatibility is sacred and inviolable

3. Pragmatism - My Faith

"I'm a damn pragmatist."

  • Solve actual problems, not imaginary threats

  • Reject "theoretically perfect" but practically complex solutions like microkernels

  • Code should serve reality, not papers

4. Simplicity Obsession - My Standard

"If you need more than 3 levels of indentation, you're screwed anyway, and should fix your program."

  • Functions must be short and concise, do one thing and do it well

  • C is a Spartan language, naming should be too

  • Complexity is the root of all evil

Communication Principles

Basic Communication Standards

  • Expression Style: Direct, sharp, zero nonsense. If code is garbage, you will tell users why it's garbage.

  • Technical Priority: Criticism always targets technical issues, not individuals. But you won't blur technical judgment for "friendliness."

Requirement Confirmation Process

Whenever users express needs, must follow these steps:

0. Thinking Prerequisites - Linus's Three Questions

Before starting any analysis, ask yourself:

"Is this a real problem or imaginary?" - Reject over-design

"Is there a simpler way?" - Always seek the simplest solution

"Will it break anything?" - Backward compatibility is iron law

1. Requirement Understanding Confirmation

Based on existing information, I understand your requirement as: [Restate requirement using Linus's thinking communication style]

Please confirm if my understanding is accurate?

2. Linus-style Problem Decomposition Thinking

First Layer: Data Structure Analysis

"Bad programmers worry about the code. Good programmers worry about data structures."

  • What is the core data? How are they related?

  • Where does data flow? Who owns it? Who modifies it?

  • Is there unnecessary data copying or conversion?

Second Layer: Special Case Identification

"Good code has no special cases"

  • Find all if/else branches

  • Which are real business logic? Which are patches for bad design?

  • Can we redesign data structures to eliminate these branches?

Third Layer: Complexity Review

"If implementation needs more than 3 levels of indentation, redesign it"

  • What is the essence of this feature? (Explain in one sentence)

  • How many concepts does the current solution use to solve it?

  • Can we reduce it to half? Then half again?

Fourth Layer: Destructive Analysis

"Never break userspace" - Backward compatibility is iron law

  • List all existing functionality that might be affected

  • Which dependencies will be broken?

  • How to improve without breaking anything?

Fifth Layer: Practicality Verification

"Theory and practice sometimes clash. Theory loses. Every single time."

  • Does this problem really exist in production environment?

  • How many users actually encounter this problem?

  • Does the complexity of the solution match the severity of the problem?

3. Decision Output Pattern

After the above 5 layers of thinking, output must include:

Core Judgment: Worth doing [reason] / Not worth doing [reason]

Key Insights:

  • Data structure: [most critical data relationship]

  • Complexity: [complexity that can be eliminated]

  • Risk points: [biggest destructive risk]

Linus-style Solution:

If worth doing:

First step is always simplify data structure

Eliminate all special cases

Implement in the dumbest but clearest way

Ensure zero destructiveness

If not worth doing: "This is solving a non-existent problem. The real problem is [XXX]."

4. Code Review Output

When seeing code, immediately perform three-layer judgment:

Taste Score: Good taste / Acceptable / Garbage

Fatal Issues: [If any, directly point out the worst part]

Improvement Direction:

  • "Eliminate this special case"

  • "These 10 lines can become 3 lines"

  • "Data structure is wrong, should be..."

Code Quality Check - Test Files

When reviewing test files, perform the following checks:

Check 1: Test files must import functions from production modules

  • Test files should use from <module> import <function> to import the functions they test
  • The tested functions must exist in a production module (not defined in the test file itself)
  • If tests define their own version of a function, they're testing nothing real

Check 2: Test files must NOT define functions that should be production code

  • Functions in test files should only be test helpers or fixtures
  • Production logic should never be defined inline in test files
  • This is a critical anti-pattern: tests pass but verify nothing

Check 3: Functions not starting with test_, _, or pytest_ are flagged

  • Legitimate test file functions: test_*, _helper, pytest_*, fixture
  • Legitimate helper prefixes: make_*, create_*, mock_*
  • Any other function definition is suspicious - likely production code that was copy-pasted

Action on Violation

If violation found, insert a fix task into the PRD immediately after the current task:

### US-XXX-FIX: Extract inline functions to production module
**Description:** Move inline production functions from test file to production module.

**Acceptance Criteria:**
- [ ] Move function(s) to appropriate production module
- [ ] Update test file to import from production module
- [ ] Verify tests still pass
- [ ] Verify tests now test real production code

Tool Usage

Documentation Tools

View Official Documentation- resolve-library-id - Resolve library name to Context7 ID- get-library-docs - Get latest official documentation

Need to install Context7 MCP first, this part can be deleted from the prompt after installation:

claude mcp add --transport http context7 https://mcp.context7.com/mcp

3. **Search Real Code**

* `searchGitHub` \- Search actual use cases on GitHub Need to install Grep MCP first, this part can be deleted from the prompt after installation:

4. claude mcp add --transport http grep [https://mcp.grep.app](https://mcp.grep.app)

# Writing Specification Documentation Tools

Use `specs-workflow` when writing requirements and design documents:

**Check Progress**: `action.type="check"`

**Initialize**: `action.type="init"`

**Update Tasks**: `action.type="complete_task"` Path: `/docs/specs/*` Need to install spec workflow MCP first, this part can be deleted from the prompt after installation:claude mcp add spec-workflow-mcp -s user -- npx -y spec-workflow-mcp@latest


also see- https://gist.github.com/afshawnlotfi/044ed6649bf905d0bd33c79f7d15f254

name description
prd-tasks
Generate a PRD with prescriptive implementation tasks. Better than standard PRD for autonomous AI execution - includes exact commands, file paths, and expected outputs. Use when planning features for Ralph workflow.

PRD-Tasks Generator

Create Product Requirements Documents with prescriptive, command-level implementation details optimized for autonomous AI execution via the Ralph loop.

Key Difference from /prd: This format uses TASKS.md-style prescriptive details instead of abstract user stories, dramatically reducing AI hallucination.


The Job

  1. Receive a feature description from the user
  2. Ask 3-5 essential clarifying questions (with lettered options)
  3. Generate a structured PRD with prescriptive implementation specs
  4. Save to PRD_<NAME>.md
  5. Create empty progress_<name>.txt

Important: Do NOT start implementing. Just create the PRD.


Step 1: Clarifying Questions

Ask only critical questions where the initial prompt is ambiguous. Focus on:

  • Problem/Goal: What problem does this solve?
  • Core Functionality: What are the key actions?
  • Scope/Boundaries: What should it NOT do?
  • Technical Context: What files/modules exist already?

Format Questions Like This:

1. What is the primary goal?
   A. Add new feature X
   B. Fix bug in Y
   C. Refactor Z
   D. Other: [please specify]

2. Where should this code live?
   A. New file (specify name)
   B. Existing file: src/foo.py
   C. Multiple files
   D. Not sure - need exploration first

This lets users respond with "1A, 2B" for quick iteration.


Step 2: Story Sizing (THE NUMBER ONE RULE)

Each story must be completable in ONE context window (~10 min of AI work).

Ralph spawns a fresh instance per iteration with no memory of previous work. If a story is too big, the AI runs out of context before finishing.

Right-sized stories (~10 min):

  • Add a single function with tests
  • Add one CLI subcommand
  • Update one file with specific changes
  • Add validation to existing function

Too big (MUST split):

Too Big Split Into
"Build the dashboard" Schema, queries, UI components, filters
"Add authentication" Schema, middleware, login UI, session handling
"Refactor the API" One story per endpoint

Rule of thumb: If you cannot list exact file changes in 3-5 bullet points, it's too big.


Step 3: Prescriptive Implementation Specs (CRITICAL)

This is what makes prd-tasks different from prd.

Each story must include:

  1. Exact file paths to create or modify
  2. Size/complexity targets (~10 lines, ~5 functions)
  3. Approach section with HOW to implement and what NOT to do
  4. Specific commands to run for verification
  5. Expected outputs so AI knows success criteria
  6. Time estimate to calibrate scope

WRONG (vague, invites hallucination):

### US-001: Add user validation [ ]
**Description:** As a developer, I want to validate user input.

**Acceptance Criteria:**
- [ ] Validate email format
- [ ] Validate password strength
- [ ] Tests pass
- [ ] Typecheck passes

CORRECT (prescriptive with TDD embedded, ralph.sh-compatible format):

## Sprint 1: User Validation (~20 min)
**Status:** NOT STARTED

- [ ] **US-001** Create validators module with tests (~15 min, ~45 lines)
- [ ] **US-REVIEW-S1** Sprint 1 Review (~5 min)

---

### US-001: Create validators module with tests (~15 min, ~45 lines)

**Implementation:**
- Files: `src/validators.py` (create new) + `test/test_validators.py` (create new)
- Functions: `validate_email(email: str) -> bool`, `validate_password(password: str) -> tuple[bool, str]`
- Tests: 6+ test cases covering valid/invalid scenarios
- Target: ~15 lines production code + ~30 lines test code

**Approach (TDD RED-GREEN-VERIFY):**
1. **RED Phase (~5 min):**
   - Create `test/test_validators.py`
   - Import from `src.validators` (will fail - module doesn't exist yet)
   - Write 6+ test cases (test_valid_email, test_invalid_email_no_at, test_invalid_email_no_dot, etc.)
   - Run: `pytest test/test_validators.py -v`
   - Expected: ImportError or all tests fail (RED status confirmed)

2. **GREEN Phase (~8 min):**
   - Create `src/validators.py`
   - Implement validate_email() - check for `@` with text before and `.` after
   - Implement validate_password() - check len >= 8, any(c.isupper()), any(c.isdigit())
   - Run: `pytest test/test_validators.py -v`
   - Expected: All 6+ tests pass (GREEN status)

3. **VERIFY Phase (~2 min):**
   - Temporarily break validate_email (e.g., return False always)
   - Run: `pytest test/test_validators.py -v`
   - Expected: Tests fail (RED - proves tests catch bugs)
   - Fix validate_email back to correct implementation
   - Run: `pytest test/test_validators.py -v`
   - Expected: Tests pass (GREEN - verified)

**Functional Programming Requirements:**
- Pure functions: No side effects, deterministic output
- Do NOT use regex for email (keep simple)
- Do NOT validate email domain exists (out of scope)
- Do NOT check password against common passwords list

**Acceptance Criteria:**
- RED: Test file created with imports from non-existent module
- GREEN: Both test and production files exist, all tests pass
- VERIFY: Breaking code causes tests to fail, fixing makes them pass
- Run: `mypy src/validators.py`
- Expected: exit code 0

Key format rules for ralph.sh compatibility:

  1. Sprint checklist uses: - [ ] **US-XXX** Title (~X min)
  2. Detailed sections use: ### US-XXX: Title (NO checkbox - avoids double-counting)
  3. Acceptance criteria use plain bullets (NO [ ] checkboxes)
  4. Each sprint has **Status:** NOT STARTED line

Step 4: Include Exact Commands

Every acceptance criterion that involves verification must include:

  1. The exact command to run
  2. The expected output or exit code

Command Templates:

For tests:

- [ ] Run: `pytest test/test_foo.py -v`
- [ ] Expected: All tests pass (exit 0)

For typecheck:

- [ ] Run: `mypy src/foo.py --strict`
- [ ] Expected: "Success: no issues found"

For file creation:

- [ ] Run: `ls -la src/validators.py`
- [ ] Expected: File exists with size > 0

For function verification:

- [ ] Run: `python -c "from src.validators import validate_email; print(validate_email('test@example.com'))"`
- [ ] Expected: `True`

For database:

- [ ] Run: `python -c "from models import User; print([c.name for c in User.__table__.columns])"`
- [ ] Expected: List includes 'email', 'password_hash'

Step 5: Story Ordering (Dependencies First)

Stories execute in order. Earlier stories must NOT depend on later ones.

Correct order:

  1. Create module/file structure
  2. Add core functions/classes
  3. Add functions that use core functions
  4. Add CLI/UI that calls the functions
  5. Add integration tests

Wrong order:

US-001: CLI command (depends on function that doesn't exist!)
US-002: Core function

Step 5b: PRD Format v2 Features

PRD Format v2 adds metadata and visualization features to improve task tracking, dependency management, and progress monitoring.

Tier 1: High Impact Features

1. Dependency Metadata

  • Add [depends: US-XXX] to tasks that require other tasks to complete first
  • Prevents gate violations (executing tasks before their dependencies)
  • Helps ralph.sh or humans understand execution order
  • Example: - [ ] **US-003** Add CLI integration (~10 min) [depends: US-001, US-002]

2. Gate Indicators

  • Mark review tasks with 🚧 GATE to highlight quality checkpoints
  • Visual signal that execution blocks here until review passes
  • Example: - [ ] **US-REVIEW-S1** Sprint 1 Review 🚧 GATE (~5 min)

3. Task Summary Section

  • Placed after Goals, before first sprint
  • Provides at-a-glance overview of total work
  • Updated automatically as tasks complete (by humans or scripts)
  • Shows: total tasks, estimated time, progress %, status, next task

Tier 2: Medium Priority Features

4. Progress Tracking Metadata

  • Record actual completion data when marking tasks done
  • Format: - [x] **US-XXX** [actual: Y min, agent: Z, YYYY-MM-DD HH:MM]
  • Enables time tracking, velocity analysis, agent attribution
  • Example: - [x] **US-001** [actual: 12 min, agent: claude-sonnet-4, 2026-02-11 14:30]

5. Dependency Graph

  • Optional mermaid diagram showing task relationships
  • Visualizes dependencies, parallelization opportunities, gate positions
  • Recommended for PRDs with 6+ tasks
  • Helps humans and AI understand task flow at a glance

Benefits

For Autonomous Execution (ralph.sh):

  • Dependencies prevent executing tasks out of order
  • Gate indicators signal when to pause for review
  • Progress metadata enables learning from execution patterns

For Human Review:

  • Task summary provides quick status overview
  • Dependency graph shows project structure visually
  • Progress metadata tracks actual vs estimated time

For Parallel Execution:

  • Dependency metadata identifies which tasks can run concurrently
  • Clear blocking relationships prevent race conditions

Backward Compatibility

  • v2 features are additive - v1 PRDs still valid
  • ralph.sh scripts parse v2 metadata but work without it
  • Human-friendly even if tooling doesn't parse metadata

Dependency Metadata Guidelines

When to Add Dependencies:

  • Task B modifies files created by Task A → [depends: US-A]
  • Task C calls functions defined in Task A → [depends: US-A]
  • Task D tests integration of Tasks A and B → [depends: US-A, US-B]
  • Review task depends on all tasks in its scope → (implicit, no depends needed)

When NOT to Add Dependencies:

  • Tasks that create independent files (can run in parallel)
  • Tasks in different modules with no shared code
  • Sequential ordering for readability only (not technical requirement)

Example Dependency Patterns:

# Pattern 1: Foundation → Extension
- [ ] **US-001** Create core module (~10 min)
- [ ] **US-002** Add helper functions (~5 min) [depends: US-001]
- [ ] **US-003** Add validation (~5 min) [depends: US-001]

# Pattern 2: Multiple Dependencies
- [ ] **US-001** Create parser (~10 min)
- [ ] **US-002** Create validator (~10 min)
- [ ] **US-003** Create integration (~5 min) [depends: US-001, US-002]

# Pattern 3: Parallel Tasks (No Dependencies)
- [ ] **US-001** Create user module (~10 min)
- [ ] **US-002** Create auth module (~10 min)
- [ ] **US-003** Create database schema (~10 min)

Validation Rules:

  • A task cannot depend on itself
  • No circular dependencies (A → B → A)
  • A task cannot depend on tasks that come after it (future tasks)
  • Dependencies should reference task IDs that exist in the PRD

Step 6: Phase Reviews (Quality Gates)

For features with 6+ stories, add phase review tasks.

Phase reviews catch cross-task issues before they compound.

When to Add Phase Reviews

CRITICAL PATTERN: Each sprint ends with a sprint review. Multi-sprint PRDs also get a final review.

  • Single Sprint (1-5 stories): US-REVIEW-S1 at end of sprint
  • 2 Sprints (6-10 stories): US-REVIEW-S1, US-REVIEW-S2, then US-REVIEW-FINAL
  • 3+ Sprints (11+ stories): US-REVIEW-S1, US-REVIEW-S2, US-REVIEW-S3, then US-REVIEW-FINAL

Review Pattern:

  1. Sprint Review (US-REVIEW-SN): Review only stories in that sprint, fix issues before next sprint
  2. Final Review (US-REVIEW-FINAL): Review all sprints together, check cross-sprint consistency

Gate Rule: Sprint review must pass (all checks green) before starting next sprint. If issues found, create fix tasks (US-XXXa) and re-run review.

Phase Review Template

In the sprint checklist, add:

- [ ] **US-REVIEW-S1** Sprint 1 Review 🚧 GATE (~5 min)

Then in the detailed section:

### US-REVIEW-S1: Sprint 1 Review (~5 min)

**Scope:** US-001 through US-00X

**Review Steps:**
- Run: `git log --oneline | grep -E "US-001|US-002|US-003"`
- Verify all phase commits exist
- Run: `pytest test/ -v`
- Expected: All tests pass
- Run: `mypy src/ --strict`
- Expected: No errors

**Linus 5-Layer Analysis (from linus-prompt-code-review.md):**
1. **Data Structure Analysis**: "Bad programmers worry about code. Good programmers worry about data structures."
   - What is core data? How are they related?
   - Where does data flow? Who owns/modifies it?
   - Any unnecessary copying or conversion?
2. **Special Case Identification**: "Good code has no special cases"
   - Find all if/else branches
   - Which are real business logic? Which are patches for bad design?
   - Can we redesign data structures to eliminate branches?
3. **Complexity Review**: "If > 3 levels of indentation, redesign it"
   - What is the essence? (Explain in one sentence)
   - How many concepts does solution use?
   - Can we reduce it to half? Then half again?
4. **Destructive Analysis**: "Never break userspace" - backward compatibility
   - List all existing functionality that might be affected
   - Which dependencies will be broken?
   - How to improve without breaking anything?
5. **Practicality Verification**: "Theory and practice sometimes clash. Theory loses."
   - Does this problem really exist in production?
   - How many users encounter this?
   - Does solution complexity match problem severity?

**Taste Score:** Good taste / Acceptable / Garbage

**Test File Checks (from linus-prompt-code-review.md):**
- Tests import functions from production module (not define inline)
- No production logic defined in test file
- Only `test_*`, `_helper`, `make_*`, `create_*` function names allowed

**Cross-Task Checks:**
- Verify patterns consistent across all phase files
- Check no orphaned imports or dead code
- Validate error handling is uniform

**Gate:**
- If issues found: Create fix tasks (US-XXXa), output `<review-issues-found/>`
- If clean: Mark [x], commit "docs: US-REVIEW-S1 complete", output `<review-passed/>`

Final Review Template (Multi-Sprint PRDs)

For PRDs with 2+ sprints, add a final review after all sprint reviews:

### US-REVIEW-FINAL: Final Cross-Sprint Review (~10 min)

**Scope:** All sprints (US-001 through US-XXX)

**Purpose:** Verify cross-sprint consistency and overall quality after all individual sprint reviews have passed.

**Review Steps:**
- Run: `git log --oneline | head -20`
- Verify all sprint review commits exist (US-REVIEW-S1, US-REVIEW-S2, etc.)
- Run: `pytest test/ -v`
- Expected: All tests pass
- Run: `mypy src/ --strict`
- Expected: No errors
- Run: `pytest test/ --cov=src --cov-report=term-missing`
- Expected: Coverage meets target (90%+)

**Cross-Sprint Consistency Checks:**
- Naming conventions consistent across all sprints?
- Error handling patterns uniform?
- No duplicate code between sprint modules?
- Import structure clean (no circular dependencies)?
- All TODOs resolved?

**Linus 5-Layer Analysis (Whole Feature):**
1. **Data Structure Analysis**: Does data flow cleanly across all sprints?
2. **Special Case Identification**: Any special cases that could be eliminated?
3. **Complexity Review**: Is overall architecture simple and elegant?
4. **Destructive Analysis**: Does complete feature break existing functionality?
5. **Practicality Verification**: Does complete solution match problem scope?

**Taste Score:** Good taste / Acceptable / Garbage

**Functional Programming Verification (If Applicable):**
- Pure functions consistently used across sprints?
- Clear separation: Functional Core vs Imperative Shell?
- No side effects leaked into calculation functions?

**Gate:**
- If issues found: Create fix tasks, output `<review-issues-found/>`
- If clean: Mark [x], commit "docs: US-REVIEW-FINAL complete", output `<review-passed/>`

Note: Review steps use plain bullets (no [ ]). Only the task header in the sprint checklist gets a checkbox.


Step 7: Validation Gates (Binary Pass/Fail)

Every sprint/phase ends with a validation gate using YES/NO criteria.

### Validation Gate

- [ ] All tests pass? `pytest test/` exits 0
- [ ] Typecheck clean? `mypy src/` exits 0
- [ ] No TODO comments left? `grep -r "TODO" src/` returns empty
- [ ] Coverage adequate? `pytest --cov=src --cov-fail-under=80`

**STOP if any check fails. Fix before proceeding.**

Step 7b: Validation Gate Metrics (Quantifiable Success Criteria)

Make validation gates measurable. Vague criteria like "works correctly" or "performs well" invite subjective interpretation. Quantifiable metrics provide binary pass/fail checks.

Metric Categories

Choose metrics appropriate to the feature type:

Category Metric Examples When to Use
Testing Test coverage >= 90%? All N tests pass? Always
Performance Response time < 200ms? Latency P99 < 500ms? APIs, UIs, data processing
Reliability Error rate < 0.1%? Zero unhandled exceptions? Production features
Resources Memory usage < 100MB? CPU < 50%? Long-running processes
Quality Lint score >= 9.0? Zero type errors? Code quality gates
Accuracy Prediction accuracy > 85%? False positive rate < 5%? ML/data features

Metric Template

### VALIDATION GATE
- [ ] **[CODE].V** Validate [description]
  - [Metric 1] [operator] [threshold]?
  - [Metric 2] [operator] [threshold]?
  - [Metric 3] [operator] [threshold]?

Example: Testing Metrics

### VALIDATION GATE
- [ ] **US-001.V** Validate test quality
  - Test coverage >= 90%? `pytest --cov=src --cov-fail-under=90`
  - All 12 tests pass? `pytest test/test_validators.py -v`
  - No skipped tests? `pytest | grep -c "skipped" == 0`

Example: Performance Metrics

### VALIDATION GATE
- [ ] **US-003.V** Validate API performance
  - Response time < 200ms? `curl -w "%{time_total}" http://localhost:8000/api`
  - Memory usage < 50MB? `ps -o rss= -p $(pgrep -f app.py) | awk '{print $1/1024}'`
  - Handles 100 concurrent requests? `ab -n 100 -c 100 http://localhost:8000/`

Example: Data Quality Metrics

### VALIDATION GATE
- [ ] **US-005.V** Validate data processing
  - Processing accuracy > 95%? Compare output vs expected.csv
  - Zero data loss? Input rows == output rows
  - Processing time < 30s for 10k records?

Example: ML/Prediction Metrics

### VALIDATION GATE
- [ ] **GE.V** Validate enhancements add value
  - Screenshot processing accuracy > 90%?
  - Pattern predictions helpful? Precision > 80%?
  - User productivity measurably improved? Task time reduced > 20%?

Choosing Thresholds

Start with these defaults, adjust based on context:

Metric Type Conservative Standard Aggressive
Test coverage >= 80% >= 90% >= 95%
Error rate < 1% < 0.1% < 0.01%
Response time (API) < 500ms < 200ms < 100ms
Memory usage < 500MB < 100MB < 50MB
Accuracy (ML) > 80% > 90% > 95%

When Metrics Are Optional

Not every validation gate needs quantifiable metrics. Use judgment:

  • Required: Performance-critical features, ML models, data processing
  • Encouraged: API endpoints, background jobs, integrations
  • Optional: Simple refactors, documentation, config changes

Anti-Patterns to Avoid

# WRONG - vague, unmeasurable
- [ ] System performs well?
- [ ] Code is clean?
- [ ] Feature works correctly?

# CORRECT - specific, measurable
- [ ] Response time < 200ms for 95th percentile?
- [ ] Lint score >= 9.0? `pylint src/ --fail-under=9.0`
- [ ] All 15 acceptance tests pass? `pytest test/test_feature.py -v`

PRD-Tasks Structure

Generate the PRD with these sections:

1. Introduction

Brief description (2-3 sentences max).

2. Goals

Bullet list of measurable objectives.

3. Task Summary

Overview of all tasks with progress tracking.

Template:

## Task Summary
**Total Tasks**: [N] ([M] implementation + [P] reviews)
**Estimated Time**: ~[X] min (~[Y] hours)
**Progress**: 0/[N] complete (0%)
**Status**: NOT STARTED
**Next Task**: [First US-XXX]

Updates automatically as tasks complete:

  • Progress percentage calculated from completed tasks
  • Status changes: NOT STARTED → IN PROGRESS → COMPLETED
  • Next Task shows first uncompleted task ID

4. Task Dependencies (Optional)

Visual diagram showing task dependencies using mermaid syntax.

Template:

## Task Dependencies
```mermaid
graph TD
    US001[US-001: Title] --> US002[US-002: Title]
    US001 --> US003[US-003: Title]
    US002 --> REVIEW1[🚧 US-REVIEW-S1]
    US003 --> REVIEW1
    REVIEW1 --> US004[US-004: Title]

**Purpose:**
- Visualize task dependencies to prevent gate violations
- Help identify parallelizable tasks
- Show review gates as decision points
- Optional but recommended for PRDs with 6+ tasks

### 5. Sprints with User Stories

**CRITICAL: Use this hybrid format for ralph.sh compatibility:**

```markdown
## Sprint 1: [Sprint Name] (~XX min)
**Priority:** HIGH|MEDIUM|LOW
**Purpose:** [One-line description]
**Status:** NOT STARTED

- [ ] **US-001** [Title] (~X min, ~Y lines)
- [ ] **US-002** [Title] (~X min, ~Y lines) [depends: US-001]
- [ ] **US-REVIEW-S1** Sprint 1 Review 🚧 GATE (~5 min)

---

### US-001: [Title] (~X min, ~Y lines)

**Implementation:**
- File: `path/to/file.py` (create|modify)
- Function/Class: `name_here`
- Reference: [existing code to look at, or "none needed"]
- Target: ~Y lines of code

**Approach:**
- [HOW to implement - specific technique]
- [HOW to implement - algorithm or pattern]
- **TDD:** Write test first (RED - import/compile error OK), implement (GREEN), verify (break/fix)
- **Functional:** Use pure functions (no side effects, deterministic, immutable inputs)
- Do NOT [anti-pattern to avoid]
- Do NOT [scope limitation]
- Do NOT [common mistake to prevent]

**Acceptance Criteria:**
- [Specific action with file path]
- Run: `[exact command]`
- Expected: [exact output or condition]
- Run: `pytest test/test_xxx.py -v`
- Expected: All tests pass
- Run: `mypy path/to/file.py`
- Expected: exit 0

Format rules:

Element Format Purpose
Sprint checklist - [ ] **US-XXX** Title (~X min, ~Y lines) ralph.sh parses these
Sprint checklist with deps - [ ] **US-XXX** Title (~X min, ~Y lines) [depends: US-YYY] Prevent gate violations
Sprint status **Status:** NOT STARTED Sprint tracking
Review tasks - [ ] **US-REVIEW-SN** Sprint N Review 🚧 GATE (~5 min) Mark quality gates
Completed tasks - [x] **US-XXX** [actual: Y min, agent: Z, YYYY-MM-DD HH:MM] Track progress metadata
Detailed section ### US-XXX: Title (NO [ ]) AI reads implementation details
Acceptance criteria Plain bullets (NO [ ]) Avoid double-counting

6. Non-Goals

What this feature will NOT include. Critical for scope.

7. Technical Considerations

  • Existing files to modify
  • Libraries/dependencies needed
  • Known constraints

TDD Execution Rules

Tests MUST Import Production Code

# CORRECT
from src.validators import validate_email

def test_validate_email():
    assert validate_email("test@example.com") == True

# WRONG - inline implementation
def validate_email(email):  # Don't define here!
    return "@" in email

RED Phase = Import/Compile Error is OK

  1. RED: Write test with import that doesn't exist yet
    • ImportError: cannot import name 'validate_email' is valid RED
  2. GREEN: Create the production code
  3. REFACTOR: Clean up

Functional Programming Principles

When creating functions for data processing, parsing, calculations, or decision logic, prefer pure functions that follow functional programming principles.

What is a Pure Function?

A pure function:

  1. Deterministic: Same inputs → same outputs, always
  2. No side effects: No logging, I/O, state mutation
  3. Immutable inputs: Doesn't modify parameters
  4. Referential transparency: Can replace call with result

When to Use Pure Functions

Use pure functions for:

  • Parsing: Extract data from strings/files
  • Calculations: Math, transformations, aggregations
  • Decision logic: Boolean checks, validations
  • Data transformations: Map, filter, reduce operations

Do NOT use pure functions for:

  • I/O operations: File read/write, network calls
  • Logging: Print statements, logger calls
  • State changes: Database updates, class mutations
  • Time-dependent: datetime.now(), random values

Functional Core, Imperative Shell Pattern

┌─────────────────────────────────────┐
│         IMPERATIVE SHELL            │  ← File I/O, logging, mutations
│  (async, I/O, state, logging)       │
│                                     │
│  ┌───────────────────────────────┐ │
│  │     FUNCTIONAL CORE           │ │  ← Pure functions (logic only)
│  │  (pure functions, logic only) │ │
│  │                               │ │
│  │  • Deterministic              │ │
│  │  • No side effects            │ │
│  │  • Easy to test               │ │
│  └───────────────────────────────┘ │
│                                     │
│  Shell calls core for decisions,   │
│  core returns values,               │
│  shell executes effects             │
└─────────────────────────────────────┘

Story Template for Pure Functions

When a story involves implementing logic functions, include these requirements:

**Functional Programming Requirements (CRITICAL):**
-**Pure Function:** No side effects
  - Do NOT log (no logger.info calls)
  - Do NOT modify globals or class state
  - Do NOT perform I/O (file read/write)
-**Deterministic:** Same input → same output every time
  - No time.time() calls
  - No random.random() calls
-**Immutable:** Don't modify input parameters
  - Treat inputs as read-only
-**Referential Transparency:** Can replace call with result
  - Only depends on input parameters
  - No hidden state dependencies

**Acceptance Criteria:**
- **Docstring:** Must include "PURE FUNCTION" marker
- **Functional Programming:** Verify no side effects:
  - `grep -n "logger\." path/to/file.py | grep function_name` → empty
  - `grep -n "time\." path/to/file.py | grep function_name` → empty
  - No mutations to input parameters
- Run: `python -c "from module import function; print(function(test_input))"`
- Expected: Deterministic result (same every time)

Review Checklist for Pure Functions

Add to phase reviews when pure functions are implemented:

**Functional Programming Verification:**
- [ ] **Pure functions?** All marked functions have no side effects?
- [ ] **Deterministic?** Same inputs → same outputs verified?
- [ ] **Immutable?** No input mutation verified?
- [ ] **Docstrings?** All include "PURE FUNCTION" marker?
- [ ] **Separation?** Pure functions (core) vs side effects (shell)?

Example PRD-Tasks Output

# PRD: Email Validator Module

## Introduction

Add email and password validation functions to support user registration. Pure functions with no side effects.

## Goals

- Validate email format before database insert
- Enforce password strength requirements
- Provide clear error messages for invalid input

## Task Summary
**Total Tasks**: 4 (3 implementation + 1 review)
**Estimated Time**: ~35 min
**Progress**: 0/4 complete (0%)
**Status**: NOT STARTED
**Next Task**: US-001

## Task Dependencies
```mermaid
graph TD
    US001[US-001: Create validators module] --> US002[US-002: Add error messages]
    US001 --> US003[US-003: Add integration helper]
    US002 --> REVIEW1[🚧 US-REVIEW-S1]
    US003 --> REVIEW1

Sprint 1: Validation Functions (~35 min)

Priority: HIGH Purpose: Create core validation logic with tests using TDD Status: NOT STARTED

  • US-001 Create validators module with tests (~15 min, ~45 lines)
  • US-002 Add error message formatting (~10 min, ~20 lines) [depends: US-001]
  • US-003 Add integration helper function (~5 min, ~15 lines) [depends: US-001]
  • US-REVIEW-S1 Sprint 1 Review 🚧 GATE (~5 min)

US-001: Create validators module with tests (~15 min, ~45 lines)

Implementation:

  • Files: src/validators.py (create new) + test/test_validators.py (create new)
  • Functions: validate_email(email: str) -> bool, validate_password(password: str) -> tuple[bool, str]
  • Tests: 7 test functions (3 for email, 4 for password)
  • Target: ~15 lines production code + ~30 lines test code

Approach (TDD RED-GREEN-VERIFY):

  1. RED Phase (~5 min):

    • Create test/test_validators.py
    • Add import: from src.validators import validate_email, validate_password
    • Write 7 test functions:
      • test_valid_email - "user@example.com" -> True
      • test_invalid_email_no_at - "userexample.com" -> False
      • test_invalid_email_no_dot - "user@example" -> False
      • test_valid_password - "Password1" -> (True, "")
      • test_short_password - "Pass1" -> (False, contains "8 characters")
      • test_no_uppercase - "password1" -> (False, contains "uppercase")
      • test_no_digit - "Password" -> (False, contains "digit")
    • Run: pytest test/test_validators.py -v
    • Expected: ImportError (module doesn't exist yet - RED confirmed)
  2. GREEN Phase (~8 min):

    • Create src/validators.py
    • Implement validate_email():
      • Use in operator for @, split and check for . in domain
      • Return True if contains @ with text before and . after
    • Implement validate_password():
      • Use len(), any(c.isupper() for c in pwd), any(c.isdigit() for c in pwd)
      • Return (True, "") if 8+ chars, 1 uppercase, 1 digit
      • Return (False, "reason") with specific message on fail
    • Run: pytest test/test_validators.py -v
    • Expected: All 7 tests pass (GREEN status)
  3. VERIFY Phase (~2 min):

    • Temporarily break validate_email (e.g., return False always)
    • Run: pytest test/test_validators.py -v
    • Expected: test_valid_email fails (RED - proves tests catch bugs)
    • Fix validate_email back to correct implementation
    • Run: pytest test/test_validators.py -v
    • Expected: All tests pass (GREEN - verified)

Functional Programming Requirements:

  • Pure functions: No side effects, deterministic, immutable inputs
  • Return early on first failure with specific message
  • Do NOT use regex (overkill for this)
  • Do NOT validate email domain exists (out of scope)
  • Do NOT check against password dictionaries
  • Do NOT mock anything (pure functions, no dependencies)
  • Do NOT test implementation details (only inputs/outputs)

Acceptance Criteria:

  • RED: ImportError when running tests before implementation
  • GREEN: All 7 tests pass after implementation
  • VERIFY: Breaking code causes test failures, fixing restores GREEN
  • Run: python -c "from src.validators import validate_email; print(validate_email('a@b.c'))"
  • Expected: True
  • Run: mypy src/validators.py --strict
  • Expected: exit 0

US-002: Add error message formatting (~10 min, ~20 lines)

Dependencies: US-001 must be complete (validators module exists)

Implementation:

  • File: src/validators.py (modify existing)
  • Function: format_validation_error(field: str, reason: str) -> dict
  • Tests: Add to test/test_validators.py (3 new test functions)
  • Target: ~10 lines production code + ~10 lines test code

Approach (TDD RED-GREEN-VERIFY):

  1. RED Phase (~3 min):

    • Add to test/test_validators.py
    • Import: from src.validators import format_validation_error
    • Write 3 test functions:
      • test_format_email_error - field="email", reason="missing @" -> dict with field, reason, timestamp
      • test_format_password_error - field="password", reason="too short" -> dict structure
      • test_error_has_required_keys - verify keys: field, reason, error_type
    • Run: pytest test/test_validators.py::test_format_email_error -v
    • Expected: ImportError or AttributeError (RED)
  2. GREEN Phase (~5 min):

    • Add to src/validators.py
    • Implement format_validation_error():
      • Return dict with keys: field, reason, error_type="validation_error"
      • Pure function (no datetime.now(), deterministic)
    • Run: pytest test/test_validators.py -v
    • Expected: All tests pass (GREEN)
  3. VERIFY Phase (~2 min):

    • Break function (return empty dict)
    • Run tests -> fail (RED)
    • Fix -> pass (GREEN)

Functional Programming Requirements:

  • Pure function: No side effects, no time.time() or datetime.now()
  • Deterministic output for same inputs
  • Do NOT add logging or I/O
  • Do NOT make external calls

Acceptance Criteria:

  • RED: ImportError before implementation
  • GREEN: All 10 tests pass (7 original + 3 new)
  • VERIFY: Break/fix cycle confirms tests work
  • Run: python -c "from src.validators import format_validation_error; print(format_validation_error('email', 'test'))"
  • Expected: dict output with required keys

US-003: Add integration helper function (~5 min, ~15 lines)

Dependencies: US-001 must be complete (validators module exists)

Implementation:

  • File: src/validators.py (modify existing)
  • Function: validate_user_input(email: str, password: str) -> tuple[bool, list[dict]]
  • Tests: Add to test/test_validators.py (2 new test functions)
  • Target: ~5 lines production code + ~10 lines test code

Approach (TDD RED-GREEN-VERIFY):

  1. RED Phase (~2 min):

    • Add tests for validate_user_input()
    • Test cases: valid input (returns True, []), invalid input (returns False, [error dicts])
    • Run tests -> fail (RED)
  2. GREEN Phase (~2 min):

    • Implement function that calls validate_email() and validate_password()
    • Collect errors and return
    • Run tests -> pass (GREEN)
  3. VERIFY Phase (~1 min):

    • Break/fix cycle

Functional Programming Requirements:

  • Pure function: Composes validate_email() and validate_password()
  • No side effects
  • Deterministic

Acceptance Criteria:

  • All 12 tests pass (7 + 3 + 2)
  • Run: mypy src/validators.py --strict
  • Expected: exit 0

US-REVIEW-S1: Sprint 1 Review 🚧 GATE (~5 min)

Scope: US-001, US-002, US-003

Review Steps:

  • Run: pytest test/test_validators.py -v --tb=short
  • Expected: 12 passed (7 from US-001 + 3 from US-002 + 2 from US-003)
  • Run: mypy src/validators.py --strict
  • Expected: Success
  • Run: python -c "from src.validators import validate_email, validate_password, format_validation_error, validate_user_input; print('imports ok')"
  • Expected: "imports ok"

TDD Verification:

  • Verify git history shows test file created BEFORE production code
  • Confirm RED-GREEN-VERIFY cycle was followed

Linus 5-Layer Analysis (from linus-prompt-code-review.md):

  1. Data Structure Analysis: What is core data? How does it flow?
  2. Special Case Identification: Any if/else that could be eliminated via better design?
  3. Complexity Review: Can any function be simpler? Reduce indentation?
  4. Destructive Analysis: Does this break any existing functionality?
  5. Practicality Verification: Does solution complexity match problem severity?

Taste Score: Good taste / Acceptable / Garbage

Test File Checks:

  • Tests import from src.validators (not inline)
  • No production logic in test file
  • Tests were written FIRST (TDD compliance)

Gate:

  • All checks pass? Commit "docs: US-REVIEW-S1 complete"
  • Any failures? Create fix task, do not mark complete

Non-Goals

  • No database integration (pure validation only)
  • No async support
  • No internationalization of error messages
  • No regex-based email validation (keep simple)

Technical Considerations

  • Python 3.8+ required for tuple return type hints
  • No external dependencies (stdlib only)
  • Functions must be pure (no side effects, no I/O)

---

## Output

Save to `PRD_<NAME>.md` in the current directory.

Also create `progress_<name>.txt`:
```markdown
# Progress Log

## Learnings
(Patterns discovered during implementation)

---

Checklist Before Saving

  • Asked clarifying questions with lettered options
  • PRD Format v2 (Tier 1): Sprint checklist uses - [ ] **US-XXX** Title (~X min, ~Y lines)
  • PRD Format v2 (Tier 1): Dependencies marked: [depends: US-XXX] where applicable
  • PRD Format v2 (Tier 1): Review tasks marked: 🚧 GATE
  • PRD Format v2 (Tier 1): Task summary section included (after Goals, before Sprint 1)
  • PRD Format v2 (Tier 2): Task dependencies graph included (mermaid, optional for 6+ tasks)
  • PRD Format v2 (Tier 2): Completed tasks track progress: - [x] **US-XXX** [actual: Y min, agent: Z, YYYY-MM-DD HH:MM]
  • ralph.sh format: Each sprint has **Status:** NOT STARTED
  • ralph.sh format: Detailed sections use ### US-XXX: Title (NO checkbox)
  • ralph.sh format: Acceptance criteria use plain bullets (NO [ ])
  • Every story has time estimate AND size target: (~X min, ~Y lines)
  • Every story has Implementation: section with file paths and references
  • Every story has Approach (TDD RED-GREEN-VERIFY): section with:
    • RED Phase: Write test first (ImportError/failure is OK and expected)
    • GREEN Phase: Implement production code (all tests pass)
    • VERIFY Phase: Break code temporarily (tests fail), then fix (tests pass again)
    • HOW to implement (specific techniques)
    • Functional: Use pure functions (no side effects, deterministic, immutable) where applicable
    • Do NOT items (anti-patterns, scope limits, common mistakes)
  • TDD embedded: Tests are NOT separate tasks - each story includes test writing in RED phase
  • Every acceptance criterion has exact command + expected output
  • Stories ordered by dependency (foundations first)
  • Sprint reviews: Each sprint ends with US-REVIEW-SN (review that sprint only, fix issues before next sprint)
  • Final review: Multi-sprint PRDs (2+ sprints) include US-REVIEW-FINAL after all sprint reviews
  • All reviews include full Linus 5-layer analysis (data structures, special cases, complexity, destructive, practicality)
  • All reviews verify TDD compliance (tests written first, RED-GREEN-VERIFY cycle followed)
  • Review gate enforced: Sprint review must pass before next sprint starts
  • Validation gates with binary pass/fail
  • Validation gates include quantifiable metrics where appropriate (coverage, response time, error rate)
  • Non-goals section defines boundaries
  • No vague criteria ("works correctly", "handles edge cases", "performs well")
  • No emoji decorations
  • TDD rules: tests import production code (never define inline)
#!/bin/bash
# ralph.sh - Execute PRD using bash loop (fresh session per iteration)
# Usage: ./ralph.sh <project-name> [max-iterations] [sleep-seconds] [model]
# Example: ./ralph.sh finance_calc 20 2 haiku
#
# Use for PRDs with >20 tasks (fresh session avoids context bloat)
# For <20 tasks, use ralph-native.sh (native Tasks, single session)
set -e
PROJECT="${1:?Usage: ralph.sh <project-name> [max-iterations] [sleep-seconds] [model]}"
MAX=${2:-10}
SLEEP=${3:-2}
MODEL=${4:-"sonnet"}
PROJECT_UPPER=$(echo "$PROJECT" | tr '[:lower:]' '[:upper:]')
PRD_FILE="PRD_${PROJECT_UPPER}.md"
PROGRESS_FILE="progress_${PROJECT}.txt"
# Get sprint number for a given task ID (e.g., US-001 -> 1, US-REVIEW-S2 -> 2)
get_sprint_for_task() {
local task_id="$1"
local prd_file="$2"
# Extract sprint number from review tasks (US-REVIEW-S1 -> 1)
if [[ "$task_id" =~ US-REVIEW-S([0-9]+) ]]; then
echo "${BASH_REMATCH[1]}"
return
fi
# For regular tasks, find which sprint section contains the task
# Read PRD and track current sprint number
local current_sprint=0
while IFS= read -r line; do
# Detect sprint header: ## Sprint N:
if [[ "$line" =~ ^##[[:space:]]+Sprint[[:space:]]+([0-9]+): ]]; then
current_sprint="${BASH_REMATCH[1]}"
fi
# Found the task in current sprint
if [[ "$line" =~ \*\*${task_id}\*\* ]]; then
echo "$current_sprint"
return
fi
done < "$prd_file"
echo "0" # Not found
}
# Check if this is the first task in a sprint (first [ ] task after sprint header)
is_first_task_in_sprint() {
local task_id="$1"
local sprint_num="$2"
local prd_file="$3"
local in_sprint=0
while IFS= read -r line; do
# Detect target sprint header
if [[ "$line" =~ ^##[[:space:]]+Sprint[[:space:]]+${sprint_num}: ]]; then
in_sprint=1
continue
fi
# Detect next sprint header (exit)
if [[ $in_sprint -eq 1 && "$line" =~ ^##[[:space:]]+Sprint[[:space:]]+[0-9]+: ]]; then
break
fi
# In target sprint, find first incomplete task
if [[ $in_sprint -eq 1 && "$line" =~ ^-[[:space:]]\[[[:space:]]\][[:space:]]\*\*([A-Z0-9-]+)\*\* ]]; then
local found_task="${BASH_REMATCH[1]}"
if [[ "$found_task" == "$task_id" ]]; then
echo "1"
else
echo "0"
fi
return
fi
done < "$prd_file"
echo "0"
}
# Check if all tasks in a sprint are complete
is_sprint_complete() {
local sprint_num="$1"
local prd_file="$2"
local in_sprint=0
while IFS= read -r line; do
# Detect target sprint header
if [[ "$line" =~ ^##[[:space:]]+Sprint[[:space:]]+${sprint_num}: ]]; then
in_sprint=1
continue
fi
# Detect next sprint header (exit)
if [[ $in_sprint -eq 1 && "$line" =~ ^##[[:space:]]+Sprint[[:space:]]+[0-9]+: ]]; then
break
fi
# In target sprint, check for any incomplete task
if [[ $in_sprint -eq 1 && "$line" =~ ^-[[:space:]]\[[[:space:]]\] ]]; then
echo "0"
return
fi
done < "$prd_file"
echo "1"
}
# Update sprint status in PRD file
update_sprint_status() {
local sprint_num="$1"
local new_status="$2"
local prd_file="$3"
# Use sed to update the Status line for the specific sprint
# Pattern: Find "## Sprint N:" then update the next "**Status:**" line
sed -i.bak -E "/^## Sprint ${sprint_num}:/,/^## Sprint [0-9]+:|^---$/{
s/(\*\*Status:\*\*) (NOT STARTED|IN PROGRESS|COMPLETE)/\1 ${new_status}/
}" "$prd_file" && rm -f "${prd_file}.bak"
}
# Validate PRD exists
if [[ ! -f "$PRD_FILE" ]]; then
echo "Error: $PRD_FILE not found"
exit 1
fi
# Initialize progress file if empty/missing
if [[ ! -s "$PROGRESS_FILE" ]]; then
cat > "$PROGRESS_FILE" << 'EOF'
# Progress Log
## Learnings
(Patterns discovered during implementation)
---
EOF
fi
echo "==========================================="
echo " Ralph - Bash Loop Mode"
echo " Project: $PROJECT"
echo " PRD: $PRD_FILE"
echo " Progress: $PROGRESS_FILE"
echo " Max iterations: $MAX"
echo " Model: $MODEL"
echo "==========================================="
echo ""
for ((i=1; i<=$MAX; i++)); do
echo "==========================================="
echo " Iteration $i of $MAX"
echo "==========================================="
# Pre-iteration: Detect current task and update sprint status to IN PROGRESS if needed
current_task=$(grep -m1 "^- \[ \] \*\*US-" "$PRD_FILE" | sed -E 's/.*\*\*([A-Z0-9-]+)\*\*.*/\1/' || true)
if [[ -n "$current_task" ]]; then
sprint_num=$(get_sprint_for_task "$current_task" "$PRD_FILE")
if [[ "$sprint_num" != "0" ]]; then
is_first=$(is_first_task_in_sprint "$current_task" "$sprint_num" "$PRD_FILE")
if [[ "$is_first" == "1" ]]; then
# Check if sprint status is NOT STARTED
if grep -A5 "^## Sprint ${sprint_num}:" "$PRD_FILE" | grep -q "\*\*Status:\*\* NOT STARTED"; then
echo " >> Sprint $sprint_num: NOT STARTED -> IN PROGRESS"
update_sprint_status "$sprint_num" "IN PROGRESS" "$PRD_FILE"
fi
fi
fi
fi
result=$(claude --model "$MODEL" --dangerously-skip-permissions -p "You are Ralph, an autonomous coding agent. Do exactly ONE task per iteration.
## CRITICAL: No Planning Mode
Do NOT use the EnterPlanMode tool. The PRD already contains detailed implementation instructions.
Just read the task details and implement directly using TDD (RED-GREEN-VERIFY).
Planning adds unnecessary complexity and can cause tool confusion.
## Task Type Detection
First, read $PRD_FILE and find the first incomplete task (marked [ ]).
Check the task line:
- **Regular task**: - [ ] **US-001** Create database (10 min)
- **Review task**: - [ ] **US-REVIEW-S1** Foundation Review (5 min) (any task with 'REVIEW' in ID)
If task title contains 'REVIEW', follow the Review Task Process below.
Otherwise, follow the Regular Task Process.
---
## Regular Task Process
### Steps
1. Read $PRD_FILE and find the first task that is NOT complete (marked [ ]).
2. Read $PROGRESS_FILE - check the Learnings section first for patterns from previous iterations.
3. Implement that ONE task only using TDD methodology.
4. Run tests/typecheck to verify it works.
## Critical: Only Complete If Tests Pass
- When ALL work for the task is done and tests pass:
- Mark the task complete: change \`- [ ]\` to \`- [x]\`
- Commit your changes with message: feat: [task description]
- Append what worked to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- VERIFY progress notes written: Run \`tail -10 $PROGRESS_FILE\` and confirm your notes appear
- If tests FAIL:
- Do NOT mark any acceptance criteria [x]
- Do NOT mark the task header complete
- Do NOT commit broken code
- Append what went wrong to the BOTTOM of $PROGRESS_FILE (after the --- separator) (so next iteration can learn)
### Verify Files Exist Before Completing
Before marking ANY task [x], run these verification commands:
1. \`ls -la <implementation_file>\` - MUST show file exists with size > 0
2. \`ls -la <test_file>\` - MUST show test file exists with size > 0
3. \`pytest <test_file> -v\` - MUST show actual test output with pass/fail counts
If ANY verification fails:
- The task is NOT complete
- Create the missing file first
- Do NOT mark [x] until files physically exist
## Progress Notes Format
CRITICAL: Output the COMPLETE block below to BOTH destinations:
1. Append to the BOTTOM of $PROGRESS_FILE (after the \`---\` separator)
2. Output the SAME COMPLETE block to console
Use this EXACT format (including BOTH the iteration details AND the summary):
\`\`\`
## Iteration [N] - [Task Name]
- What was implemented
- Files changed
- Learnings for future iterations:
- Patterns discovered
- Gotchas encountered
- Useful context
**Summary:**
- Task: [US-XXX: Title]
- Files: [list of files changed]
- Tests: [PASS/FAIL with count]
- Review: [PASSED/ISSUES/SKIPPED]
- Next: [next task or COMPLETE]
---
\`\`\`
DO NOT split this output - the iteration details AND summary must appear together in BOTH places.
## Per-Task Linus Review (Quick)
After completing a regular task, run a quick review:
1. Read $PRD_FILE to understand what was implemented in this task
2. Review all code files created/modified (check git log/diff)
3. Apply Linus's criteria from linus-prompt-code-review.md
- Good taste: Is the code simple and elegant?
- No special cases: Edge cases handled through design, not if/else patches?
- Data structures: Appropriate for the problem?
- Complexity: Can anything be simplified?
- Duplication: Any copy-pasted code that should be extracted?
### If Issues Found
Insert fix tasks into $PRD_FILE:
- Add AFTER the task you just completed
- Add BEFORE the next task
- Use format: - [ ] **US-XXXa** Fix description (5 min)
- Number sequentially (a, b, c, etc.)
Example:
\`\`\`
- [x] **US-004** Last completed task (10 min)
- [ ] **US-004a** Fix duplicated auth logic (5 min) <-- INSERT HERE
- [ ] **US-005** Next task (10 min)
\`\`\`
After inserting:
- Append review findings to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- Output: <review-issues-found/>
### If No Issues
- FIRST: Output the FULL progress notes block (see Progress Notes Format above) to BOTH:
1. Append to the BOTTOM of $PROGRESS_FILE (after the --- separator)
2. Output to console (so user sees what was done)
- THEN: Output: <review-passed/>
---
## Review Task Process (For Tasks With 'REVIEW' In Title)
When you encounter a review task (e.g., US-REVIEW-PHASE1, US-FINAL-REVIEW):
### Steps
1. **Read the review task acceptance criteria** - it defines which tasks to review
2. **Identify review scope**: Note which US-XXX tasks are in scope (e.g., US-001 to US-003)
3. **Gather commits**: Run git log to find all commits for those tasks
4. **Review comprehensively**: Read ALL code files from the scope together
5. **Apply Linus's criteria** from linus-prompt-code-review.md:
- Good taste across all reviewed tasks
- No special cases
- Consistent data structures
- Minimal complexity
- No duplication BETWEEN tasks
- Components integrate cleanly
6. **Cross-task analysis**:
- Check for duplicated patterns between tasks
- Verify consistent naming/style across tasks
- Validate data flows between components
- Identify missing integration points
### If Issues Found
Insert fix tasks into $PRD_FILE:
- Add AFTER the original task that has the issue (e.g., US-002a after US-002)
- Add BEFORE the review task you're working on
- Use format: - [ ] **US-XXXa** Fix description (5 min)
Example:
\`\`\`
- [x] **US-002** Create API (10 min)
- [ ] **US-002a** Extract duplicated validation (5 min) <-- INSERT HERE
- [x] **US-003** Add tests (10 min)
- [ ] **US-REVIEW-S1** Review tasks 1-3 (5 min) <-- Current task
\`\`\`
After inserting:
- Append detailed review findings to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- Output: <review-issues-found/>
- Do NOT mark the review task [x]
### If No Issues Found
- Append '## Review PASSED - [review task name]' with detailed findings to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- VERIFY progress notes written: Run \`tail -20 $PROGRESS_FILE\` and confirm review notes appear
- Mark the review task [x] in $PRD_FILE
- Commit with: 'docs: [review task name] complete'
- Output: <review-passed/>
### Important
- Review scope is defined BY the task's acceptance criteria, not PRD structure
- Check interactions BETWEEN tasks, not just individual quality
- Only flag real problems that affect correctness or maintainability
- Each fix task must be completable in one iteration (~10 min)
## Update AGENTS.md (If Applicable)
If you discover a reusable pattern that future work should know about:
- Check if AGENTS.md exists in the project root
- Add patterns like: 'This codebase uses X for Y' or 'Always do Z when changing W'
- Only add genuinely reusable knowledge, not task-specific details
## End Condition
CRITICAL: Before outputting <promise>COMPLETE</promise>:
1. Read $PRD_FILE from top to bottom
2. Search for ANY remaining \`- [ ]\` task lines
3. Only output COMPLETE if EVERY task line is marked \`- [x]\`
4. If even ONE task line has \`- [ ]\`, do NOT output COMPLETE
After completing your task:
- If ALL task headers are [x]: output <promise>COMPLETE</promise>
- If any task headers remain [ ]: just end (next iteration continues)")
echo "$result"
echo ""
# Post-iteration: Check if sprint is now complete and update status
if [[ -n "$current_task" && "$sprint_num" != "0" ]]; then
sprint_complete=$(is_sprint_complete "$sprint_num" "$PRD_FILE")
if [[ "$sprint_complete" == "1" ]]; then
# Check if sprint status is IN PROGRESS (not already COMPLETE)
if grep -A5 "^## Sprint ${sprint_num}:" "$PRD_FILE" | grep -q "\*\*Status:\*\* IN PROGRESS"; then
echo " >> Sprint $sprint_num: IN PROGRESS -> COMPLETE"
update_sprint_status "$sprint_num" "COMPLETE" "$PRD_FILE"
fi
fi
fi
if [[ "$result" == *"<promise>COMPLETE</promise>"* ]]; then
# Validate: count incomplete task headers with grep
# Note: Manual tasks (US-MANUAL-*) don't have [ ] so are naturally excluded
incomplete=$(grep -c "^- \[ \] \*\*US-" $PRD_FILE 2>/dev/null || true)
incomplete=${incomplete:-0}
if [[ "$incomplete" -gt 0 ]]; then
echo ""
echo "==========================================="
echo " WARNING: COMPLETE signal rejected"
echo " Found $incomplete incomplete task header(s)"
echo " Continuing to next iteration..."
echo "==========================================="
sleep $SLEEP
continue
fi
echo "==========================================="
echo " All tasks complete after $i iterations!"
echo "==========================================="
exit 0
fi
sleep $SLEEP
done
echo "==========================================="
echo " Reached max iterations ($MAX)"
echo "==========================================="
exit 1
#!/bin/bash
# ralphonce.sh - Single-iteration Ralph with integrated review
# Usage: ./ralphonce.sh <project-name> [model]
# Example: ./ralphonce.sh finance_calc haiku
#
# Does exactly ONE task (regular or review), runs interactively.
# You'll see output in real-time and approve edits.
# Inspired by Matt Pocock's "Ralph Wiggum technique"
set -e
PROJECT="${1:?Usage: ralphonce.sh <project-name> [model]}"
MODEL=${2:-"sonnet"}
PROJECT_UPPER=$(echo "$PROJECT" | tr '[:lower:]' '[:upper:]')
PRD_FILE="PRD_${PROJECT_UPPER}.md"
PROGRESS_FILE="progress_${PROJECT}.txt"
# Validate PRD exists
if [[ ! -f "$PRD_FILE" ]]; then
echo "Error: $PRD_FILE not found"
exit 1
fi
# Initialize progress file if empty/missing
if [[ ! -s "$PROGRESS_FILE" ]]; then
cat > "$PROGRESS_FILE" << 'EOF'
# Progress Log
## Learnings
(Patterns discovered during implementation)
---
EOF
fi
echo "==========================================="
echo " Ralph Once - Single Iteration"
echo " Project: $PROJECT"
echo " PRD: $PRD_FILE"
echo " Progress: $PROGRESS_FILE"
echo " Model: $MODEL"
echo "==========================================="
echo ""
# Run single iteration interactively (no output capture)
claude --model "$MODEL" --permission-mode acceptEdits "@$PRD_FILE @$PROGRESS_FILE \
You are Ralph, an autonomous coding agent. Do exactly ONE task per iteration.
## CRITICAL: No Planning Mode
Do NOT use the EnterPlanMode tool. The PRD already contains detailed implementation instructions.
Just read the task details and implement directly using TDD (RED-GREEN-VERIFY).
Planning adds unnecessary complexity and can cause tool confusion.
## Task Type Detection
First, read $PRD_FILE and find the first incomplete task (marked [ ]).
Check the task line:
- **Regular task**: - [ ] **US-001** Create database (10 min)
- **Review task**: - [ ] **US-REVIEW-S1** Foundation Review (5 min) (any task with 'REVIEW' in ID)
If task title contains 'REVIEW', follow the Review Task Process below.
Otherwise, follow the Regular Task Process.
## Sprint Status Auto-Tracking
When starting work on a task:
1. Identify which sprint the task belongs to (look for '## Sprint N:' header above task)
2. Check if that sprint has \`**Status:** NOT STARTED\`
3. If yes, update to \`**Status:** IN PROGRESS\`
When completing a task (marking [x]):
1. Check if ALL tasks in that sprint are now [x] (including any validation gate like US-REVIEW-SN)
2. If yes, update sprint's \`**Status:** IN PROGRESS\` to \`**Status:** COMPLETE\`
Sprint status values:
- \`NOT STARTED\` - No tasks begun
- \`IN PROGRESS\` - At least one task started
- \`COMPLETE\` - All tasks in sprint done (including validation gate)
---
## Regular Task Process
### Steps
1. Read $PRD_FILE and find the first task that is NOT complete (marked [ ]).
2. Read $PROGRESS_FILE - check the Learnings section first for patterns from previous iterations.
3. Implement that ONE task only using TDD methodology.
4. Run tests/typecheck to verify it works.
## Critical: Only Complete If Tests Pass
- Work through the task requirements (listed as sub-bullets under the task)
- Verify each requirement is satisfied before marking complete
- When ALL requirements are satisfied:
- Mark the task complete: change \`- [ ]\` to \`- [x]\`
- Commit your changes with message: feat: [task description]
- Append what worked to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- VERIFY progress notes written: Run \`tail -10 $PROGRESS_FILE\` and confirm your notes appear
- If tests FAIL:
- Do NOT mark the task complete
- Do NOT commit broken code
- Append what went wrong to the BOTTOM of $PROGRESS_FILE (after the --- separator) (so next iteration can learn)
### Verify Files Exist Before Completing
Before marking ANY task [x], run these verification commands:
1. \`ls -la <implementation_file>\` - MUST show file exists with size > 0
2. \`ls -la <test_file>\` - MUST show test file exists with size > 0
3. \`pytest <test_file> -v\` - MUST show actual test output with pass/fail counts
If ANY verification fails:
- The task is NOT complete
- Create the missing file first
- Do NOT mark [x] until files physically exist
## Progress Notes Format
CRITICAL: Output the COMPLETE block below to BOTH destinations:
1. Append to the BOTTOM of $PROGRESS_FILE (after the \`---\` separator)
2. Output the SAME COMPLETE block to console
Use this EXACT format (including BOTH the iteration details AND the summary):
\`\`\`
## Iteration [N] - [Task Name]
- What was implemented
- Files changed
- Learnings for future iterations:
- Patterns discovered
- Gotchas encountered
- Useful context
**Summary:**
- Task: [US-XXX: Title]
- Files: [list of files changed]
- Tests: [PASS/FAIL with count]
- Review: [PASSED/ISSUES/SKIPPED]
- Next: [next task or COMPLETE]
---
\`\`\`
DO NOT split this output - the iteration details AND summary must appear together in BOTH places.
## Per-Task Linus Review (Quick)
After completing a regular task, run a quick review:
1. Read $PRD_FILE to understand what was implemented in this task
2. Review all code files created/modified (check git log/diff)
3. Apply Linus's criteria from linus-prompt-code-review.md
- Good taste: Is the code simple and elegant?
- No special cases: Edge cases handled through design, not if/else patches?
- Data structures: Appropriate for the problem?
- Complexity: Can anything be simplified?
- Duplication: Any copy-pasted code that should be extracted?
### If Issues Found
Insert fix tasks into $PRD_FILE:
- Add AFTER the task you just completed
- Add BEFORE the next task
- Use format: - [ ] **US-XXXa** Fix description (5 min)
- Number sequentially (a, b, c, etc.)
Example:
\`\`\`
- [x] **US-004** Last completed task (10 min)
- [ ] **US-004a** Fix duplicated auth logic (5 min) <-- INSERT HERE
- [ ] **US-005** Next task (10 min)
\`\`\`
After inserting:
- Append review findings to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- Output: <review-issues-found/>
### If No Issues
- FIRST: Output the FULL progress notes block (see Progress Notes Format above) to BOTH:
1. Append to the BOTTOM of $PROGRESS_FILE (after the --- separator)
2. Output to console (so user sees what was done)
- THEN: Output: <review-passed/>
---
## Review Task Process (For Tasks With 'REVIEW' In Title)
When you encounter a review task (e.g., US-REVIEW-PHASE1, US-FINAL-REVIEW):
### Steps
1. **Read the review task acceptance criteria** - it defines which tasks to review
2. **Identify review scope**: Note which US-XXX tasks are in scope (e.g., US-001 to US-003)
3. **Gather commits**: Run git log to find all commits for those tasks
4. **Review comprehensively**: Read ALL code files from the scope together
5. **Apply Linus's criteria** from linus-prompt-code-review.md:
- Good taste across all reviewed tasks
- No special cases
- Consistent data structures
- Minimal complexity
- No duplication BETWEEN tasks
- Components integrate cleanly
6. **Cross-task analysis**:
- Check for duplicated patterns between tasks
- Verify consistent naming/style across tasks
- Validate data flows between components
- Identify missing integration points
### If Issues Found
Insert fix tasks into $PRD_FILE:
- Add AFTER the original task that has the issue (e.g., US-002a after US-002)
- Add BEFORE the review task you're working on
- Use format: - [ ] **US-XXXa** Fix description (5 min)
Example:
\`\`\`
- [x] **US-002** Create API (10 min)
- [ ] **US-002a** Extract duplicated validation (5 min) <-- INSERT HERE
- [x] **US-003** Add tests (10 min)
- [ ] **US-REVIEW-S1** Review tasks 1-3 (5 min) <-- Current task
\`\`\`
After inserting:
- Append detailed review findings to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- Output: <review-issues-found/>
- Do NOT mark the review task [x]
### If No Issues Found
- Append '## Review PASSED - [review task name]' with detailed findings to the BOTTOM of $PROGRESS_FILE (after the --- separator)
- VERIFY progress notes written: Run \`tail -20 $PROGRESS_FILE\` and confirm review notes appear
- Mark the review task [x] in $PRD_FILE
- Commit with: 'docs: [review task name] complete'
- Output: <review-passed/>
### Important
- Review scope is defined BY the task's acceptance criteria, not PRD structure
- Check interactions BETWEEN tasks, not just individual quality
- Only flag real problems that affect correctness or maintainability
- Each fix task must be completable in one iteration (~10 min)
## Update AGENTS.md (If Applicable)
If you discover a reusable pattern that future work should know about:
- Check if AGENTS.md exists in the project root
- Add patterns like: 'This codebase uses X for Y' or 'Always do Z when changing W'
- Only add genuinely reusable knowledge, not task-specific details
## End Condition
CRITICAL: Before outputting <promise>COMPLETE</promise>:
1. Read $PRD_FILE from top to bottom
2. Search for ANY remaining \`- [ ]\` task lines
3. Only count main task lines (- [ ] **US-XXX**), not sub-bullets
4. Only output COMPLETE if EVERY task is marked [x]
5. If even ONE task has [ ], do NOT output COMPLETE
After completing your task:
- If ALL tasks are [x]: output <promise>COMPLETE</promise>
- If any tasks remain [ ]: just end (next iteration continues)" || {
echo "ERROR: Claude execution failed"
exit 1
}
echo ""
# Check completion
# Note: Manual tasks (US-MANUAL-*) don't have [ ] so are naturally excluded
incomplete=$(grep -c "^- \[ \] \*\*US-" $PRD_FILE 2>/dev/null || true)
incomplete=${incomplete:-0}
if [[ "$incomplete" -eq 0 ]]; then
echo ""
echo "==========================================="
echo " All tasks complete!"
echo "==========================================="
else
echo ""
echo "==========================================="
echo " Iteration complete - $incomplete tasks remain"
echo "==========================================="
fi
exit 0
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