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CodeTrace

One PRD in. Architecture, code, tests, and acceptance evidence out — then a self-converging loop hardens it until it clears a measurable quality bar.

CodeTrace is an AI-first delivery pipeline: a reusable, version-controlled system of agents, skills, commands, rules, and memory under .claude/. To prove the pipeline works, it generates its own payload — a lightweight, local-first, zero-dependency Python function tracer under src/.

The pipeline is the deliverable. The library is the proof.

Flow

PRD → Intent → Architecture → Build Spec → Task Slices → Coding → Verify → Accept → ⟳ Converge

Every stage is three files: a command (entry), a skill (how), an agent (who), wrapped in non-negotiable rules and resumable memory.

/seechen --run      # run the full pipeline from docs/PRD.md
/converge --run     # harden a green milestone until it converges

Proven run

The pipeline generated the library end to end, then the convergence loop hardened it across three rounds:

77/77 tests · 99% line coverage · 77.3% mutation · ruff clean · mypy 0 errors · complexity ≤ B → CONVERGED

The loop caught what a green suite hid — a type-safety hole, a complexity hotspot, and a coverage illusion: 99% of lines executed, but only 64.5% of the logic was actually asserted. Hardening lifted mutation to 77.3%, past the gate, and the loop stopped.

Case Study — the full narrative · Audit evidence — per-round reports

Documentation

Document What it covers
PRD Product source of truth
Workflow Stage-by-stage definitions
Pipeline Components Every agent and skill in detail — inputs, outputs, boundaries
Case Study The end-to-end run, round by round
Convergence Loop Rubric, gates, stop conditions, driver
Diagrams Flow + sequence diagrams, rendered inline (PlantUML source in docs/diagrams/)

Structure

CodeTrace/
├── .claude/          # the pipeline: agents, skills, commands, rules, memory, docs
├── docs/             # PRD, workflow, component reference, case study, diagrams
├── specs/            # generated outputs: intent, architecture, build, acceptance, audit
├── src/ tests/       # the generated library and its test suite
└── CLAUDE.md         # repository entry point

Contributing

Follow the rules in .claude/rules/: task-appropriate branches, Conventional Commits, English for reusable constraint files, and update .claude/memory/pipeline-state.md after each completed stage.

License

MIT — see LICENSE.

About

An AI-first delivery pipeline — agents, skills, commands, rules, and memory that turn one PRD into architecture, build specs, code, tests, and acceptance evidence, then run a self-converging audit/hardening loop. Proven by generating CodeTrace, a zero-dependency Python function tracer.

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