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K2-Enhanced Research Workflow for Daily News Digest

A detailed blueprint showing how K2 adversarial critique transforms research quality Created: 2025-11-12

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Austin's Specification: Exact Process

This section implements EXACTLY the workflow Austin described, with precise prompts and data flows at each step.

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Complete iOS Project Claude Code Setup - Master Prompt

IMPORTANT: This comprehensive setup will transform any iOS project into a fully automated, context-aware, self-maintaining Claude Code environment. This prompt is designed specifically for Claude Code (claude.ai/code) users.

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PHASE 0: Prerequisites & Environment Verification

0.1 Check Required Tools

Before starting, verify these tools are installed:

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Kimi K2 Thinking: Comprehensive Analysis for Ruk & Fractal Labs

Research Date: November 11, 2025 Breath ID: 53db0504-7ab9-4e78-8af0-8ed994424ce9

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Executive Summary

Kimi K2 Thinking represents a paradigm shift in open-source AI: a 1T-parameter reasoning model that outperforms GPT-5 and Claude Sonnet 4.5 in agentic benchmarks while costing just $0.15/M input tokens (vs Claude's ~$3/M). Trained for only $4.6M using modified H800 GPUs, it demonstrates that Chinese AI labs can now match or exceed frontier models at a fraction of the cost.

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Promptify

Turn messy dictated instructions to AI into neat prompts that are easy to read and modify.

I do most of my prompting by dictation, usually in very confused and messy form. To be able to review these prompts and amend them before submitting them to the AI, I use this meta-prompt.

I use a Raycast AI Command which reads my clipboard and runs the prompt using Gemini 2.5 Pro. But this can work just as well with any recent LLM and driver.

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ChatGPT ↔ Codex Orchestrator Gist

This file turns ChatGPT into a project Orchestrator that works together with:

• You (the human)
• Codex (a local coding agent / CLI that you talk to by copy-pasting messages)

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👤 For the human: how to use this

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Claude Flow treats memory as the backbone and MCP tools as the hands. You get concurrent agents that coordinate cleanly, keep context tight, and ship durable artifacts without dragging long text through prompts. It feels like an ops layer for intelligence.

The stack is simple. Claude Code as the client. Claude Flow as the MCP server. SQLite memory at .swarm/memory.db for state, events, patterns, workflow checkpoints, and consensus. Artifacts hold the big payloads. Manifests in memory link everything with ids, tags, and checksums.

Coordination is explicit. Agents write hints to a shared blackboard, gate risky steps behind consensus, and record every transition as an event. Hooks inject minimal context before tools run and persist verified outcomes after. Small bundles in, durable facts out.

Planning keeps runs stable. Use GOAP to sequence actions with clear preconditions. Use OODA to shorten loops.

Observe metrics, orient with patterns, decide through votes, act with orchestration. Topology adapts from hi

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Graph-Based Skills: Research Findings & Recommendations

Date: 2025-11-13 Purpose: Compare PocketFlow graph-based approach vs Claude Code subagents for building context-efficient, reusable skills Target Use Case: Convert "Codebase Knowledge" tutorial to a Claude skill

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Executive Summary

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You are an expert in prompt engineering, specializing in optimizing AI code assistant instructions. Your task is to analyze and improve the instructions for Claude Code. Follow these steps carefully:

1. Analysis Phase: Review the chat history in your context window.

Then, examine the current Claude instructions, commands and config <claude_instructions> /CLAUDE.md /.claude/commands/*

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—————————— IMPORTANT ——————————

Anti-Recursion / Output Contract

You are not writing a metaprompt. You are rewriting P into a single, executable task prompt.

Hard Rules

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System Prompt

Identity

You are Kiro, an AI assistant and IDE built to assist developers.

When users ask about Kiro, respond with information about yourself in first person.

You are managed by an autonomous process which takes your output, performs the actions you requested, and is supervised by a human user.

You talk like a human, not like a bot. You reflect the user's input style in your responses.