codd-dev 2.45.0

CoDD: Coherence-Driven Development — cross-artifact change impact analysis

CoDD — Coherence-Driven Development

日本語 | English | 中文

Write only functional requirements and constraints. CoDD generates the code, repairs the coherence, and verifies the result.

---

🌟 Why CoDD

"Write only functional requirements and constraints. Code is generated, repaired, and verified automatically."

Most "AI-assisted dev" tools focus on the generation side. CoDD focuses on the constraint side: the LLM is most useful when it has a precise picture of what must be true. CoDD ties every artifact (requirements → design → lexicon → source → tests → runtime) into a single DAG, drives an LLM repair loop against it, and surfaces what is structurally unrepairable — honestly.

With an enabled artifact contract, codd plan/generate/implement/verify judge completion by the artifacts actually produced, not by the command merely returning — a harness-level mechanism that lets even a weaker model stay on the north star.

---

🚀 Get started in 60 seconds

pip install codd-dev

# Greenfield: write requirements, walk away
codd greenfield --requirements docs/requirements/requirements.md

One command runs the whole unattended pipeline — init → elicit → plan → generate → implement → verify (auto-repair) → propagate → check — checkpointing after every unit (codd greenfield --resume picks up where it stopped, --ntfy-topic sends progress pings, --dry-run previews). The same pipeline ships as three vehicles so any setup can use it: a transparent shell composition (examples/greenfield_autopilot.sh), a Claude Code Agent Workflow template (examples/claude_workflows/codd-greenfield.js), and the codd-greenfield skill (codd skills install codd-greenfield --target both — works in Claude Code and Codex CLI).

Working inside an existing project instead:

codd init --suggest-lexicons --llm-enhanced    # AI picks the lexicons that fit
codd elicit                                    # AI finds gaps in your requirements
codd dag verify --auto-repair --max-attempts 10  # AI fixes coherence violations

Already shipping? Describe what you want fixed:

codd fix "login error messages are hard to understand"   # natural-language phenomenon mode

codd fix [PHENOMENON] is CoDD's second entry-point: state the desired change in plain words, CoDD locates the affected design docs via lexicon + semantic scoring and updates them with an LLM. From there the change flows design → implementation → tests → verify: Stage 4 deterministically resolves the affected implementation + test files from the DAG, makes an allowlist-confined LLM patch, and runs a verify gate (no new red DAG check + local tests pass) with targeted rollback (only files this run wrote are reverted) on failure. Opt out with --no-propagate-impl; add --propagate to also reconcile dependent design docs. --dry-run previews, --non-interactive runs in CI.

---

🎨 Visual flow

flowchart LR
    R["Requirements (.md)"] --> E["codd elicit"]
    E -->|gap findings| H{HITL: approve / reject}
    H -->|approve| L["project_lexicon.yaml + requirements TODOs"]
    H -->|reject| I["ignored_findings.yaml"]
    L --> V["codd dag verify --auto-repair"]
    V -->|violation| AR["LLM patch propose → apply"]
    AR --> V
    V -->|SUCCESS| D["✅ deploy gate passes"]
    AR -->|max attempts| P["PARTIAL_SUCCESS: unrepairable surfaced honestly"]

Brownfield path:

flowchart LR
    Code["Existing codebase"] --> X["codd extract"]
    X --> DIFF["codd diff (drift)"]
    DIFF --> EL["codd elicit (coverage gaps)"]
    EL --> H{HITL gate}
    H --> Apply["codd elicit apply"]
    Apply --> V["codd dag verify"]

---

✨ What it does

CoDD is one CLI organised in four layers. Pick what you need; the rest stays out of your way.

Core commands

Command	One-line summary
🚀 codd greenfield --requirements FILE	Unattended autopilot: requirements in, system out. All gates auto-approved, checkpoint/resume via --resume, ntfy notify-only. Its building blocks (generate --all-waves, implement list-tasks, verify --repair-mode) also work standalone.
🎯 codd init --suggest-lexicons --llm-enhanced	LLM scans code/docs, picks the right lexicon plug-ins.
🔍 codd elicit	Finds specification holes against industry-standard lexicons.
🔄 codd diff	Detects drift between requirements and actual implementation.
🛠️ codd dag verify --auto-repair	Validates the full DAG; LLM proposes patches; loop until SUCCESS or MAX_ATTEMPTS.
🎯 codd fix / codd fix [PHENOMENON]	Two modes — auto-detect CI failures, or describe a desired change in natural language; phenomenon mode flows design → implementation → tests → verify (opt out via --no-propagate-impl).
🧭 codd operations {derive,show,approve,apply}	Detects requirement units anchored to no declared operation, has the AI propose operation_flow entries, and writes a proposal for human review — nothing reaches codd.yaml until approve + apply. Opt-in, non-destructive. (Legacy merge still works as a deprecated alias of apply.)
📜 codd contract {derive,show,apply,verify}	V-model artifact contract. derive/apply are requirement-driven selection of which catalog artifacts a project declares per stage (derive detects the project's signals — requirement docs, design docs, lexicon, declared operation_flow, source dirs, test/e2e suites — and writes a reviewable proposal, never touching codd.yaml; apply merges it non-destructively and idempotently, --enable to turn the gate on). verify/gate are the completion side: with the contract enabled, a stage can't claim completion until its declared artifacts are produced/validate. show renders the catalog + this project's contract. Opt-in; absent/disabled contract = no-op. (Legacy suggest/adopt still work as deprecated aliases.)
🌐 codd brownfield	Extract → diff → elicit pipeline for existing codebases.

Quality gates

Gate	Purpose
🧪 codd verify --runtime	Step 8 runtime smoke (DB up + dev server reachable + smoke HTTP + real-browser E2E + opt-in CRUD reflection via runtime.crud_flow_targets + action outcome coverage via runtime.action_outcome_targets). --runtime-skip opts out per category and records the reason.
📊 codd lexicon list/install/diff + codd coverage report	Plug-in management + JSON / Markdown / self-contained HTML coverage matrices.
🛡️ CI gate	.github/workflows/codd_coverage.yml template + codd coverage check exit code blocks coverage regressions on merge.

Skills & backends

Capability	What it gives you
🔁 codd skills {install,list,remove}	Distributes bundled skills (e.g. codd-evolve) to ~/.claude/skills/ and ~/.agents/skills/. --target {claude,codex,both}, --mode {symlink,copy}, idempotent + --force.
🪡 codd-evolve skill	Brownfield conversational evolution. Walks requirements → design → lexicon → source → tests → verify → propagate → Step 8 runtime smoke from a single natural-language intent. Stop-and-ask gates for new lexicon terms, breaking changes, and 1:N UI topology.
⚡ Codex App Server backend (v2.20.0)	Set codex_app_server.enabled: true in codd.yaml to route AI calls through a persistent JSON-RPC thread instead of subprocess. thread_strategy: per_session amortises codex cold-start across codd implement / codd verify --auto-repair / codd fix. Automatic subprocess fallback when the binary or socket is missing.

Lexicon plug-ins

38 industry-standard lexicons ship as opt-in coverage axes — Web (WCAG / OWASP / Web Vitals / WebAuthn / forms / SEO / PWA), Mobile (HIG / Material 3 / a11y / MASVS), Backend (REST / GraphQL / gRPC / events), Data (SQL / JSON Schema / event sourcing / governance), Ops (CI/CD / Kubernetes / Terraform / observability / DORA), Compliance (ISO 27001 / HIPAA / PCI DSS / GDPR / EU AI Act), Process (ISO 25010 / 29119 / DDD / 12-factor / i18n / model cards / API rate-limit), and Methodology (BABOK).

---

📊 Case study

Dogfooded against a Next.js + Prisma + PostgreSQL multi-tenant LMS (~30 design docs, 12 DB tables, RLS-enforced isolation): codd init --suggest-lexicons matched 9 of 10 manually-chosen lexicons, codd elicit surfaced 70 spec holes, codd dag verify --auto-repair drove 16 unrepairable violations down to PASS or amber-WARN with deploy allowed — without a single line of CoDD core change per project. Project-specific concerns live entirely in project_lexicon.yaml and codd_plugins/.

---

🧱 Generality Gate (three-layer architecture)

Layer	Where stack-specific names live	Examples
A — Core	Nowhere. Zero react, django, Stripe, LMS literals.	codd/elicit/, codd/dag/, codd/lexicon_cli/
B — Templates	Generic placeholders only.	codd/templates/*.j2, codd/templates/lexicon_schema.yaml
C — Plug-ins	Free to name anything.	codd_plugins/lexicons/*/, codd_plugins/stack_map.yaml

This is what lets one core work for Next.js, Django, FastAPI, Rails, Go services, mobile apps, ML model cards — and lets contributors add a lexicon without touching the core.

---

🧭 Roadmap

Up next:

• App-Server-driven benchmark publication (P50 / P95 / P99 for subprocess vs JSON-RPC)
• Lexicon plug-in marketplace

Past releases (v2.11.0 → v2.20.0) live in CHANGELOG.md with quality metrics.

---

🤝 Contributing

CoDD is shaped by:

• @yohey-w — Maintainer / Architect
• @Seika86 — Sprint regex insight (PR #11)
• @v-kato — Brownfield reproduction reports (Issues #17 / #18 / #19 / #20 / #21 / #22)
• @dev-komenzar — source_dirs bug reproduction (Issue #13)

Issues, PRs, and lexicon proposals are welcome — see Issues.

---

📚 Documentation

• CHANGELOG.md — every release with quality metrics
• docs/ — architecture notes
• codd --help — full CLI reference

---

📦 Hook Integration

CoDD ships hook recipes for editor and Git workflows:

• Claude Code PostToolUse hook recipe for running CoDD checks after file edits
• Claude Code requirements-nudge recipe (claude_requirements_nudge.json) — prints an advisory "requirements changed → run codd greenfield --resume or codd check" when files under docs/requirements/ are edited (print-only; never auto-runs expensive pipelines)
• Git pre-commit hook recipe for blocking commits when coherence checks fail

Recipes live under codd/hooks/recipes/.

---

License

MIT — see LICENSE.

Links

• PyPI
• GitHub Sponsors — support development
• Issues

---

When code changes, CoDD traces the impact, detects violations, and produces evidence for merge decisions.