A Prompt Pipeline Demonstrates Meta-Programming Properties

A developer has documented a prompt pipeline used to build an Electron app (Claude Code CLI, React, TypeScript, SQLite) that demonstrates the structural properties of a meta-programming language, including typed inputs/outputs, control flow, loops, state machines, and module interfaces.
Pipeline Architecture
The system operates in four distinct stages with typed contracts between them.
Stage 1: Static Analysis
The repo-eval.md prompt performs a full codebase scan. It reads every source file, traces dependency graphs, maps IPC flows end-to-end, and emits a structured findings report to issues.md. Each finding includes:
- Section ID
- Title
- Severity (Critical/High/Medium/Low)
- Category
- File paths with line ranges
- Evidence
- Impact assessment
- Recommended fix
Stage 2: Compilation
The address-issues.md prompt parses the output of Stage 1. It groups findings by file affinity, assigns prompt numbers (FIX-01, FIX-02, ...), determines inter-prompt dependencies, and sorts by severity. Output is a directory (prompts/arch/r{NNN}/) containing:
FIX-NN.md— Self-contained fix prompts specifying objective, findings addressed, files to modify, implementation steps, and verification criteria.STATE.md— Execution state tracker with status per prompt (pending/in-progress/done/blocked), dependency graph, completion dates, and handoff notes.MASTER.md— Execution loop specification.
Stage 3: Execution
MASTER.md serves as the runtime loop. On each iteration:
- Read
STATE.md - Select next pending prompt whose dependencies are satisfied
- Read the prompt
- Read all affected source files
- Execute implementation steps
- Run verification (
tsc --noEmit,grepfor removed patterns, line count checks) - Update
STATE.md - Append changelog entry, update architecture docs
- Loop to step 1
Stage 4: Packaging
The release-notes.md prompt reads the changelog, runs git log and git diff --stat against the last tag, categorizes changes, and emits formatted release notes with version bump recommendation.
Key Properties
Typed Contracts: The output schema of repo-eval is the input contract for address-issues. The output schema of address-issues is the input contract for MASTER. The FIX prompts consume the exact format that address-issues produces. STATE.md has a defined schema that MASTER reads and writes.
Automatic Documentation: A system prompt (agents.md) attached to every Claude Code context window includes a post-execution protocol: append a changelog entry, update affected architecture docs, update state tracking. Documentation is emitted as a side effect of prompt execution, eliminating context decay.
Prompts as ADRs: Each FIX-NN.md functions as a design doc, work order, and architectural decision record, capturing problem, rationale, implementation plan, and verification criteria before execution.
Output
One day of running this pipeline produced:
- 2 full repo evaluations
- 17 bug fixes across 2 revision rounds (race conditions, stream architecture, concurrent state management)
- Service decomposition: 1,218 lines → 403 lines + 5 extracted services
- 3 additional crash fixes
- README rewrite verified against source
- 6-page GitHub Pages website
- 21 changelog entries
📖 Read the full source: r/ClaudeAI
👀 See Also

OpenClaw's Bub AI agent struggles with delegation, burns $20 in 15 minutes during mobile site optimization
During QA for Driftwatch V3, the OpenClaw bot Bub burned $20 in 15 minutes by failing to delegate tasks properly. The developer discovered detailed spec templates reduce costs, while mobile retrofitting added unexpected time and expense.

Non-Coder Builds Multiplayer Game on Steam Using Claude AI — 60k Lines, 5 Factions, 87 Abilities
A Reddit user with zero coding experience built a full multiplayer game (60k lines, 5 factions, 87 abilities) using Claude AI and got it approved on Steam. Early Access June 1.

Rebuilding an Automated Video Production Pipeline with OpenClaw
A developer rebuilt an automated video production pipeline using OpenClaw, yt-dlp, ffmpeg, and ElevenLabs. The new version analyzes scripts to find relevant footage, syncs clips to voiceover timing, and caps clip length to prevent visible looping.

Using Claude Opus 4 for AI Orchestration on Limited Hardware
Exploring Claude Opus 4 as a reasoning engine on a 2014 Mac Mini, leveraging the Claude API for handling complex orchestration tasks.