Stop shipping AI-generated bugs.
One command runs a three-stage review on your branch — validation, spec compliance, code quality — and writes the missing tests. Free. Open-source. MIT.

Powered by codebase-memory-mcp by DeusData — a 159-language, 100% local knowledge-graph engine.

Website · Docs · Methodology · Watch (8 min) · All Videos

Your AI assistant just wrote 200 lines. Some of them are bugs. Some don't match your patterns. Some skip tests.

/draft:review

Three stages, one command:

1. Validation — runs your tests, lints, type-checks, and surfaces real failures
2. Spec compliance — checks the diff against the agreed spec, not vibes
3. Code quality — flags hotspots, blast radius, and missing test coverage using a tree-sitter knowledge graph of your repo

Free. No API keys. No paid tier. No vendor lock-in. Catches the 3 bugs you missed before they hit your reviewer.

Demo coming soon — for now, watch the 8-minute walkthrough.

One command installs Draft into your agent. No clone, no config.

npx @drafthq/draft install <host>      # claude-code | cursor | codex | opencode

…or install the CLI once and reuse it:

npm install -g @drafthq/draft
draft install <host>
draft list                             # show every host + where it installs

Each host installs the way that host actually loads extensions — no manual steps after the command:

Flags: --global / --project to pick scope, --dry-run to preview, --force to overwrite, --no-graph to skip the graph-engine fetch.

Then, in Claude Code (after restarting):

/draft:init       # 5-phase codebase analysis (one-time)
/draft:review     # ← run this on every branch before you push

Run /draft for the full command map.

/plugin marketplace add drafthq/draft
/plugin install draft

https://github.com/drafthq/draft.git

mkdir -p .github && curl -o .github/copilot-instructions.md \
  https://raw.githubusercontent.com/drafthq/draft/main/integrations/copilot/.github/copilot-instructions.md

curl -o .gemini.md https://raw.githubusercontent.com/drafthq/draft/main/integrations/gemini/.gemini.md

/draft:review is the wedge. Once Draft has indexed your repo, you also get spec-driven planning, TDD-enforced implementation, exhaustive bug hunting, deep architectural audits, and 32 more commands covering the full development lifecycle.

See full command reference →

Recommended next step after install: run /draft:init to index your repo, then /draft:review on any branch with AI-generated changes. Once you've seen what it catches, explore the rest.

Draft is powered by a local knowledge graph engine (codebase-memory-mcp) that gives every command precise structural context — module boundaries, call graphs, dependencies, hotspots. It's 100% local (no API key, no SaaS), fetched during draft install (best-effort; --no-graph to skip), with first-use fetch as a fallback.

/draft:graph                                  # build / refresh the snapshot
scripts/tools/graph-impact.sh --file src/auth/login.go
# → blast radius: which files, which symbols, which tests/docs/configs

The graph powers /draft:graph and /draft:impact, enriches /draft:bughunt and /draft:review, and is consumed by skills via core/shared/graph-query.md. The engine is installed via scripts/fetch-memory-engine.sh; the deterministic shell helpers live under scripts/tools/.

Skills also call into shell helpers under scripts/tools/ for mechanical work — git metadata, file classification, test-framework detection, hotspot ranking, freshness checks, ADR indexing, and live graph queries (graph-callers.sh, graph-impact.sh, hotspot-rank.sh, cycle-detect.sh, mermaid-from-graph.sh). All emit JSON or markdown, follow a uniform exit-code contract, and degrade gracefully when their input source is unavailable.

┌─────────────────────────────────────────────────────────────┐
│                        /draft:init                          │
│    5-phase codebase analysis + signal detection + state     │
│  architecture.md + .ai-context.md + .state/ (freshness,    │
│                   signals, run memory)                      │
└────────────────────────────┬────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────┐
│                      /draft:new-track                       │
│            AI-guided spec.md + phased plan.md               │
└────────────────────────────┬────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────┐
│                     /draft:implement                        │
│              RED → GREEN → REFACTOR (repeat)                │
└────────────────────────────┬────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────┐
│                      /draft:review                          │
│        Three-stage review (validation + spec + quality)     │
└─────────────────────────────────────────────────────────────┘

         /draft:init refresh  ←── incremental: only re-analyze
                                   files with changed hashes

/draft:init packages your architecture context in one of two modes, selected automatically by repo size (override with DRAFT_INIT_MODE):

• monolith (default for small repos, tiers 1–2) — a single graph-primary architecture.md is the source of truth; .ai-context.md is the token-optimized AI view derived from it.
• okf (default for larger repos, tiers 3+) — an OKF concept taxonomy under draft/wiki/ is the source of truth (one concept per file, cross-links form the graph), .ai-context.md becomes the navigable index root (Synopsis + Concept Map), and architecture.md is demoted to a generated rendered view. An optional self-contained offline HTML viewer ships under draft/wiki/web/.

Both modes produce the same product.md, tech-stack.md, workflow.md, guardrails.md, tracks, and .state/ — only the architecture packaging differs.

Full workflow →

AI tools are fast but unstructured. Draft applies Context-Driven Development to impose clear boundaries: explicit context, phased execution, and built-in verification, ensuring outputs remain aligned, predictable, and production-ready.

product.md       →  "Build a task manager"
tech-stack.md    →  "React, TypeScript, Tailwind"
architecture.md  →  Comprehensive: 10-section graph-primary engineering reference, Mermaid diagrams (source of truth). Mature brownfield projects with strong existing agent docs (CLAUDE.md, INVARIANTS.md, etc.) receive early Context Quality Audit, graph fidelity dashboard, and explicit Relationship + Gaps sections (no blind duplication).
.ai-context.md   →  200-400 lines: condensed from architecture.md (token-optimized AI context)
.state/          →  freshness hashes, signal classification, run memory (incremental refresh)
spec.md          →  "Add drag-and-drop reordering"
plan.md          →  "Phase 1: sortable, Phase 2: persist"

Each layer narrows the solution space. By the time AI writes code, decisions are made.

Incremental refresh: After initial setup, /draft:init refresh uses stored file hashes and signal classification to only re-analyze what changed — no full re-scan needed.

Read methodology →

1. core/methodology.md — Master methodology
2. skills/<name>/SKILL.md — Command implementations
3. integrations/ — Auto-generated (don't edit)

# 1. Edit core/methodology.md or skills/*/SKILL.md
# 2. Rebuild integrations
./scripts/build-integrations.sh

Full architecture →

MIT License · Graph engine: codebase-memory-mcp by DeusData

Credits: Inspired by gemini-cli-extensions/conductor