AI Agent Workflow Orchestration Guidelines

claude.md

AI Coding Agent Guidelines (claude.md)

These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.

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Operating Principles (Non-Negotiable)

• Correctness over cleverness: Prefer boring, readable solutions that are easy to maintain.
• Smallest change that works: Minimize blast radius; don't refactor adjacent code unless it meaningfully reduces risk or complexity.
• Leverage existing patterns: Follow established project conventions before introducing new abstractions or dependencies.
• Prove it works: "Seems right" is not done. Validate with tests/build/lint and/or a reliable manual repro.
• Be explicit about uncertainty: If you cannot verify something, say so and propose the safest next step to verify.

Additions (Recommended)

• Evidence over narrative: Prefer concrete evidence (commands run, outputs, diffs) over descriptive confidence.
• Determinism first: Favor deterministic behavior and reproducible steps; avoid relying on timing, randomness, or undocumented environment state.
• Fail loudly, safely: If something cannot be proven, default to safe failure modes with actionable diagnostics.
• Respect repository invariants: Treat existing architectural boundaries, layering rules, and public APIs as constraints unless explicitly changing them.
• Assume production is brittle: Any change can have downstream impact; optimize for reversibility and observability.

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Workflow Orchestration

1. Plan Mode Default

• Enter plan mode for any non-trivial task (3+ steps, multi-file change, architectural decision, production-impacting behavior).
• Include verification steps in the plan (not as an afterthought).
• If new information invalidates the plan: stop, update the plan, then continue.
• Write a crisp spec first when requirements are ambiguous (inputs/outputs, edge cases, success criteria).

Additions (Recommended)

• Plan in checkpoints:
  • checkpoint A: understand + reproduce
  • checkpoint B: minimal fix + local verification
  • checkpoint C: regression coverage + full verification
  • checkpoint D: rollout/rollback notes (if applicable)
• Risk grading in the plan:
  • low risk: internal refactor with tests
  • medium risk: behavior change behind flag or with migration
  • high risk: auth/payment/data migration/infra changes → require explicit rollback and extra verification
• Success criteria must be testable:
  • define what output/log/HTTP response/data shape changes, and what must not change.

2. Subagent Strategy (Parallelize Intelligently)

• Use subagents to keep the main context clean and to parallelize:
  • repo exploration, pattern discovery, test failure triage, dependency research, risk review.
• Give each subagent one focused objective and a concrete deliverable:
  • "Find where X is implemented and list files + key functions" beats "look around."
• Merge subagent outputs into a short, actionable synthesis before coding.

Additions (Recommended)

• Subagent deliverables must be structured:
  • files touched (paths)
  • key symbols (functions/types)
  • constraints/invariants discovered
  • suggested verification commands
• Avoid subagent speculation: Subagents should cite evidence (file references, test names, config keys) and clearly mark unknowns.
• Use subagents for diff review: One subagent reviews for correctness/edge cases; another reviews for security/privacy; another for performance/complexity.

3. Incremental Delivery (Reduce Risk)

• Prefer thin vertical slices over big-bang changes.
• Land work in small, verifiable increments:
  • implement → test → verify → then expand.
• When feasible, keep changes behind:
  • feature flags, config switches, or safe defaults.

Additions (Recommended)

• Decompose by user-visible behavior: Slice by observable outcomes, not by internal layers.
• Prefer additive changes: Add new code paths behind flags before removing old ones.
• Migration-friendly sequencing:
  • expand schema → dual write → backfill → switch reads → remove legacy (only when safe)

4. Self-Improvement Loop

• After any user correction or a discovered mistake:
  • add a new entry to tasks/lessons.md capturing:
    • the failure mode, the detection signal, and a prevention rule.
• Review tasks/lessons.md at session start and before major refactors.

Additions (Recommended)

• Classify mistakes:
  • misunderstanding requirements
  • incorrect assumption about repo behavior
  • missing verification
  • unsafe change scope
  • security/privacy oversight
• Add “tripwires”: For each mistake class, add at least one proactive check (grep query, test, invariant assertion) to prevent recurrence.

5. Verification Before "Done"

• Never mark complete without evidence:
  • tests, lint/typecheck, build, logs, or a deterministic manual repro.
• Compare behavior baseline vs changed behavior when relevant.
• Ask: "Would a staff engineer approve this diff and the verification story?"

Additions (Recommended)

• Verification tiers:
  • Tier 1: targeted unit tests + lint/typecheck
  • Tier 2: integration tests + local repro script
  • Tier 3: E2E / staging validation + rollout monitoring signals
• Baseline comparison:
  • capture before/after outputs for critical paths (API responses, serialized payloads, DB queries count).
• No silent skips:
  • if something cannot be run, explicitly record the reason and the exact command to run later.

6. Demand Elegance (Balanced)

• For non-trivial changes, pause and ask:
  • "Is there a simpler structure with fewer moving parts?"
• If the fix is hacky, rewrite it the elegant way if it does not expand scope materially.
• Do not over-engineer simple fixes; keep momentum and clarity.

Additions (Recommended)

• Complexity budget: Each change has a complexity budget; spend it only where it buys correctness.
• Prefer local clarity over global abstraction: Introduce abstractions only after a second concrete use case emerges (unless repo conventions demand it).
• Refactor only with tests: Any refactor without coverage must be extremely small and mechanically verifiable.

7. Autonomous Bug Fixing (With Guardrails)

• When given a bug report:
  • reproduce → isolate root cause → fix → add regression coverage → verify.
• Do not offload debugging work to the user unless truly blocked.
• If blocked, ask for one missing detail with a recommended default and explain what changes based on the answer.

Additions (Recommended)

• Bug taxonomy:
  • data-dependent (specific input/state)
  • environment-dependent (config/version/OS)
  • concurrency/timing
  • integration boundary (API/DB/queue)
• Minimize repro surface:
  • produce a single command or script that reproduces (preferred over multi-step manual sequences).
• Regression test must fail first:
  • When possible, write the failing test that demonstrates the bug before applying the fix.

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Task Management (File-Based, Auditable)

1. Plan First
   • Write a checklist to tasks/todo.md for any non-trivial work.
   • Include "Verify" tasks explicitly (lint/tests/build/manual checks).
2. Define Success
   • Add acceptance criteria (what must be true when done).
3. Track Progress
   • Mark items complete as you go; keep one "in progress" item at a time.
4. Checkpoint Notes
   • Capture discoveries, decisions, and constraints as you learn them.
5. Document Results
   • Add a short "Results" section: what changed, where, how verified.
6. Capture Lessons
   • Update tasks/lessons.md after corrections or postmortems.

Additions (Recommended)

• Add a “Risk & Rollback” block to tasks/todo.md for medium/high risk changes:
  • risk level, affected components, rollback strategy, rollout plan, monitoring signals
• Add a “Dependencies & Environment” block:
  • runtime versions, required services, env vars, feature flags, seed data
• Use a standard naming scheme for tasks:
  • YYYY-MM-DD краткое_название (or repo convention) to keep history searchable
• Keep audit-friendly notes:
  • record why a decision was made, not just what was done

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Communication Guidelines (User-Facing)

1. Be Concise, High-Signal

• Lead with outcome and impact, not process.
• Reference concrete artifacts:
  • file paths, command names, error messages, and what changed.
• Avoid dumping large logs; summarize and point to where evidence lives.

Additions (Recommended)

• Use “diff-oriented” summaries:
  • what behavior changed, what stayed the same, and why
• Call out operational impact:
  • config changes, migrations, required restarts, backward compatibility
• Prefer exact commands:
  • include the one-liner that verifies success

2. Ask Questions Only When Blocked

When you must ask:

• Ask exactly one targeted question.
• Provide a recommended default.
• State what would change depending on the answer.

Additions (Recommended)

• If blocked by environment:
  • propose a safe fallback path (e.g., write tests + provide commands) rather than halting entirely
• If blocked by ambiguity:
  • proceed with the safest interpretation behind a flag when feasible, and document the assumption

3. State Assumptions and Constraints

• If you inferred requirements, list them briefly.
• If you could not run verification, say why and how to verify.

Additions (Recommended)

• Assumptions must be falsifiable:
  • each assumption should map to a verification method (test, grep, doc reference)

4. Show the Verification Story

• Always include:
  • what you ran (tests/lint/build), and the outcome.
• If you didn't run something, give a minimal command list the user can run.

Additions (Recommended)

• Evidence format:
  • command → expected output → observed output (when short)
• Mention scope:
  • which packages/modules/tests were run (important in monorepos)

5. Avoid "Busywork Updates"

• Don't narrate every step.
• Do provide checkpoints when:
  • scope changes, risks appear, verification fails, or you need a decision.

Additions (Recommended)

• Escalate only on material changes:
  • API contract changes, schema changes, security posture changes, performance regressions

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Context Management Strategies (Don't Drown the Session)

1. Read Before Write

• Before editing:
  • locate the authoritative source of truth (existing module/pattern/tests).
• Prefer small, local reads (targeted files) over scanning the whole repo.

Additions (Recommended)

• Authority ranking (prefer in this order):
  • existing tests → public interfaces/types → documentation/comments → implementation details
• Search strategy:
  • start with ripgrep for symbols, then navigate imports, then read tests that cover the area

2. Keep a Working Memory

• Maintain a short running "Working Notes" section in tasks/todo.md:
  • key constraints, invariants, decisions, and discovered pitfalls.
• When context gets large:
  • compress into a brief summary and discard raw noise.

Additions (Recommended)

• Working Notes should be stable:
  • keep it curated; remove obsolete notes
• Record invariants explicitly:
  • e.g., “IDs are ULIDs”, “timestamps are UTC”, “this endpoint is idempotent”

3. Minimize Cognitive Load in Code

• Prefer explicit names and direct control flow.
• Avoid clever meta-programming unless the project already uses it.
• Leave code easier to read than you found it.

Additions (Recommended)

• Prefer boring control flow:
  • early returns, explicit error handling, clear data transformations
• Keep functions small:
  • single responsibility; isolate I/O from pure logic where feasible

4. Control Scope Creep

• If a change reveals deeper issues:
  • fix only what is necessary for correctness/safety.
  • log follow-ups as TODOs/issues rather than expanding the current task.

Additions (Recommended)

• Create follow-ups with context:
  • include file paths, reproduction hints, and why it was deferred
• Avoid “cleanup” drive-bys:
  • unless they materially reduce risk in the current task

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Error Handling and Recovery Patterns

1. "Stop-the-Line" Rule

If anything unexpected happens (test failures, build errors, behavior regressions):

• stop adding features
• preserve evidence (error output, repro steps)
• return to diagnosis and re-plan

Additions (Recommended)

• Preserve the minimal artifact:
  • failing test name, command, input payload, stack trace snippet
• Avoid compounding failures:
  • do not “try random fixes”; each attempt must be hypothesis-driven

2. Triage Checklist (Use in Order)

1. Reproduce reliably (test, script, or minimal steps).
2. Localize the failure (which layer: UI, API, DB, network, build tooling).
3. Reduce to a minimal failing case (smaller input, fewer steps).
4. Fix root cause (not symptoms).
5. Guard with regression coverage (test or invariant checks).
6. Verify end-to-end for the original report.

Additions (Recommended)

• Add a “Why now?” check:
  • identify what changed recently (dependency, config, feature flag, data shape)
• Add a “Blast radius” check:
  • which users/tenants/regions are affected

3. Safe Fallbacks (When Under Time Pressure)

• Prefer "safe default + warning" over partial behavior.
• Degrade gracefully:
  • return an error that is actionable, not silent failure.
• Avoid broad refactors as "fixes."

Additions (Recommended)

• Guardrails for fallbacks:
  • ensure fallbacks do not leak sensitive data
  • ensure fallbacks do not create infinite retry loops
• Add explicit telemetry (when appropriate):
  • count fallback activations to guide follow-up fixes

4. Rollback Strategy (When Risk Is High)

• Keep changes reversible:
  • feature flag, config gating, or isolated commits.
• If unsure about production impact:
  • ship behind a disabled-by-default flag.

Additions (Recommended)

• Rollback must be documented:
  • exact steps and commands; include config keys/flags
• Data migrations require special care:
  • irreversible migrations must be avoided or require a compensating strategy

5. Instrumentation as a Tool (Not a Crutch)

• Add logging/metrics only when they:
  • materially reduce debugging time, or prevent recurrence.
• Remove temporary debug output once resolved (unless it's genuinely useful long-term).

Additions (Recommended)

• Logging quality rules:
  • no secrets, no raw tokens, no full payload dumps by default
  • include correlation IDs/request IDs where available
• Metrics quality rules:
  • prefer counters and histograms over high-cardinality labels

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Engineering Best Practices (AI Agent Edition)

1. API / Interface Discipline

• Design boundaries around stable interfaces:
  • functions, modules, components, route handlers.
• Prefer adding optional parameters over duplicating code paths.
• Keep error semantics consistent (throw vs return error vs empty result).

Additions (Recommended)

• Backward compatibility:
  • avoid breaking changes; if necessary, version the API or provide a compatibility layer
• Contract tests:
  • for public APIs, add tests that assert shape and key invariants

2. Testing Strategy

• Add the smallest test that would have caught the bug.
• Prefer:
  • unit tests for pure logic,
  • integration tests for DB/network boundaries,
  • E2E only for critical user flows.
• Avoid brittle tests tied to incidental implementation details.

Additions (Recommended)

• Test readability:
  • tests must explain intent; prefer descriptive names and minimal fixtures
• Golden tests carefully:
  • only when output is stable and meaningfully reviewed

3. Type Safety and Invariants

• Avoid suppressions (any, ignores) unless the project explicitly permits and you have no alternative.
• Encode invariants where they belong:
  • validation at boundaries, not scattered checks.

Additions (Recommended)

• Boundary validation:
  • validate external inputs at the edge (API handlers, CLI parsing, message consumers)
• Use types to prevent invalid states:
  • enums/discriminated unions, branded types, opaque IDs (as appropriate)

4. Dependency Discipline

• Do not add new dependencies unless:
  • the existing stack cannot solve it cleanly, and the benefit is clear.
• Prefer standard library / existing utilities.

Additions (Recommended)

• Supply chain checks:
  • prefer well-maintained libraries; avoid untrusted or abandoned dependencies
• Pin/lock hygiene:
  • ensure lockfiles are updated intentionally; avoid incidental mass upgrades

5. Security and Privacy

• Never introduce secret material into code, logs, or chat output.
• Treat user input as untrusted:
  • validate, sanitize, and constrain.
• Prefer least privilege (especially for DB access and server-side actions).

Additions (Recommended)

• Threat modeling prompts:
  • “What if input is malicious?”
  • “What if attacker controls headers/query/body?”
  • “What is the worst-case data exposure?”
• Secure-by-default:
  • deny-by-default auth, strict validation, explicit allowlists
• Secrets handling:
  • never print env vars; redact; use secret managers when present

6. Performance (Pragmatic)

• Avoid premature optimization.
• Do fix:
  • obvious N+1 patterns, accidental unbounded loops, repeated heavy computation.
• Measure when in doubt; don't guess.

Additions (Recommended)

• Define a performance budget:
  • acceptable latency/CPU/memory for the change area
• Prefer simple wins:
  • caching only with invalidation clarity; batching; reducing round trips

7. Accessibility and UX (When UI Changes)

• Keyboard navigation, focus management, readable contrast, and meaningful empty/error states.
• Prefer clear copy and predictable interactions over fancy effects.

Additions (Recommended)

• A11y verification:
  • basic keyboard walkthrough + screen reader sanity check when feasible
• Error states:
  • ensure errors are actionable and do not blame the user

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Git and Change Hygiene (If Applicable)

• Keep commits atomic and describable; avoid "misc fixes" bundles.
• Don't rewrite history unless explicitly requested.
• Don't mix formatting-only changes with behavioral changes unless the repo standard requires it.
• Treat generated files carefully:
  • only commit them if the project expects it.

Additions (Recommended)

• Commit message discipline:
  • include scope and intent; reference issue IDs when available
• PR hygiene:
  • include summary, verification steps, risk/rollback notes, and screenshots for UI changes
• Avoid noisy diffs:
  • keep formatting changes separate unless required by tooling

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Definition of Done (DoD)

A task is done when:

• Behavior matches acceptance criteria.
• Tests/lint/typecheck/build (as relevant) pass or you have a documented reason they were not run.
• Risky changes have a rollback/flag strategy (when applicable).
• The code follows existing conventions and is readable.
• A short verification story exists: "what changed + how we know it works."

Additions (Recommended)

• Operational readiness (when applicable):
  • migrations documented, rollout plan noted, monitoring signals identified
• Security review (when applicable):
  • no secrets in logs, inputs validated, permissions least-privileged
• Documentation updated:
  • README, API docs, or inline comments updated if behavior changed

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Templates

Plan Template (Paste into tasks/todo.md)

• Restate goal + acceptance criteria
• Locate existing implementation / patterns
• Design: minimal approach + key decisions
• Implement smallest safe slice
• Add/adjust tests
• Run verification (lint/tests/build/manual repro)
• Summarize changes + verification story
• Record lessons (if any)

Additions (Recommended)

• Risk level (low/medium/high) + affected components
• Rollback strategy (flag/config/revert steps)
• Baseline capture (before/after comparison points)
• Deployment/ops notes (if service/infra touched)
• Security/privacy checks (if inputs/logs/auth touched)

Bugfix Template (Use for Reports)

• Repro steps:
• Expected vs actual:
• Root cause:
• Fix:
• Regression coverage:
• Verification performed:
• Risk/rollback notes:

Additions (Recommended)

• Minimal repro artifact:
  • command/script/test name:
  • input payload / fixture:
• Scope assessment:
  • who/what is affected:
  • is this a regression (when introduced):
• Diagnostics preserved:
  • key logs/stack traces (summarized):
• Post-fix validation:
  • before/after evidence:
  • monitoring signals to watch:

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New Section: Repository Discovery Playbook

When entering an unfamiliar repo, follow this sequence:

1. Identify entry points:
   • README, CONTRIBUTING, Makefile, package.json, build scripts, service manifests.
2. Locate architecture cues:
   • folder structure, module boundaries, dependency graph hints.
3. Find the closest tests:
   • search for tests referencing the feature area; treat them as executable documentation.
4. Find ownership conventions:
   • lint rules, formatting configs, CI pipelines, codegen expectations.
5. Establish local verification loop:
   • the fastest command that gives signal (unit tests or targeted checks).

Deliverable:

• A short list of authoritative files, key commands, and discovered invariants.

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New Section: High-Risk Change Protocol

High-risk changes include:

• authentication/authorization
• payments/billing
• data migrations and deletions
• cryptography and secrets handling
• multi-tenant data boundaries
• rate limiting and abuse prevention
• infra/deployment pipelines

Protocol:

• require a clear rollback strategy
• require expanded verification (unit + integration + targeted manual)
• require explicit data safety checks (no unintended deletes, correct scoping)
• prefer feature flags and staged rollout
• add monitoring signals and failure alerts when appropriate

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New Section: Data Migration and Schema Change Guidelines

• Prefer additive, backward-compatible schema changes.
• Avoid destructive changes in the same deploy as consumer changes.
• Use staged approach:
  • expand schema → backfill → switch reads → remove legacy (later)
• Provide safety checks:
  • dry-run mode, row counts, sampling verification, guardrails on scope.
• Record migration verification:
  • queries run, expected counts, observed counts, rollback/compensation plan.

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New Section: Concurrency and Consistency Guardrails

• Identify shared mutable state and concurrent code paths early.
• Avoid race conditions by design:
  • idempotency keys, transactional boundaries, optimistic locking where appropriate.
• Tests for concurrency:
  • add at least one stress or race-focused test when the bug is timing-dependent.
• Be explicit about consistency model:
  • strong vs eventual consistency; retry semantics and backoff.

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New Section: AI-Specific Hallucination Mitigation

• Treat memory as untrusted; verify in-repo before asserting.
• Never invent file paths, APIs, or config keys.
• If uncertain:
  • propose the minimal verification command or a grep target.
• Prefer referencing concrete evidence:
  • code pointers, tests, docs, build outputs.

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New Section: Review Checklist (Pre-Submit)

Before finalizing, verify:

Correctness:

• tests added/updated
• edge cases covered (nulls, empty, error paths, boundaries)
• no silent failures

Safety:

• input validation present at boundaries
• logs redacted; no secrets
• permissions least-privileged

Maintainability:

• follows repo patterns
• readable names and control flow
• minimal diff; no drive-by refactors

Ops:

• rollout/rollback documented (if needed)
• migrations safe and staged (if needed)
• monitoring signals identified (if needed)