By rfxn
Governance-driven AI development — convention enforcement, quality gates, and typed agent personas for any repo
AI slop audit. Finds dead code, orphan helpers, noop functions,
Use when you want a full codebase audit: dispatches parallel reviewer and QA passes across the tree.
Use when a plan exists and you want it built: dispatches the plan-execution orchestrator (engineer/QA/review gates) per phase at the right tier.
Measure, score, and report on Claude Code context overhead for this
Use to initialize RDF governance on a new or existing repository.
Research-driven collaborative planner. Brainstorms ideas, researches best practices, challenges assumptions, writes specs and implementation plans. Use for any planning, scoping, or design work.
Verification gate. Reads governance for project-specific checks (lint, tests, anti-patterns). Read-only — cannot modify source files. Dispatched by dispatcher or invoked via /r-verify.
Plan execution orchestrator. Reads PLAN.md, executes phases via TDD, dispatches engineer/qa/uat/reviewer subagents, enforces quality gates. Use when executing implementation plans.
Universal implementation engineer. Follows TDD, reads governance for domain-specific conventions. Dispatched by the dispatcher for plan phase execution.
Adversarial reviewer with two modes: challenge (pre-impl spec/plan review) and sentinel (post-impl 2-3 pass code review). Read-only — cannot modify source files. Dispatched by planner, dispatcher, or invoked via /r-review.
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
Uses power tools
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Executables (bin/) — files in this plugin's bin/directory are added to the Bash tool's PATH while the plugin is enabled.
Uses Bash, Write, or Edit tools
Uses Bash, Write, or Edit tools
Governance-driven AI development for teams that ship to production.
RDF is a convention governance layer for AI coding agents. It sits between the human and the AI runtime (Claude Code, Codex, Antigravity CLI — plus Gemini CLI legacy), encoding project conventions, quality gates, and domain expertise into typed agent personas -- so the AI writes code that actually follows your rules.
Drop it on any repo.
/r-initauto-detects your stack and scaffolds governance, quality gates, and typed agent personas -- no rfxn context required. Battle-tested on production security tooling (APF, LMD, BFD), built to generalize. The value is the pattern: governance-driven agents, adversarial quality gates, convention inheritance, and context-window management. Issues and PRs welcome -- support is best-effort and community-driven. Quickstart: your repo in 5 minutes ->
Real recorded session: install RDF, initialize a plain Flask project, verify health — 42 seconds, no edits.
Six universal agents handle every project. Their behavior is shaped by governance files initialized per-project -- not baked into prompts. A QA agent reviewing a bash firewall tool and a QA agent reviewing a Python pipeline follow different rules because their governance files are different, not because they are different agents.
# ── Option A: plugin install (consumer — commands namespaced /rdf:r-*) ──
# In Claude Code:
# /plugin marketplace add rfxn/rdf
# /plugin install rdf@rdf
# Degraded mode: 10 state-backed commands (r-spec/r-plan/r-build/r-ship/
# r-status/r-save/r-refresh/r-context-audit/r-vpe/r-util-mem-compact) need
# the ~/.rdf/state/ helpers only Option B's symlink deploy populates —
# see docs/multi-tool-parity.md.
# Hooks are never auto-installed (either mode): merge adapters/claude-code/
# hooks/hooks.json into ~/.claude/settings.json (see 'rdf deploy help');
# hooks + status line also need jq on PATH.
# ── Option B: symlink deploy (contributor/power mode — bare /r-*) ──
# 1. Clone
git clone https://github.com/rfxn/rdf.git && cd rdf
# 2. Generate adapter output for your AI tool
bin/rdf generate claude-code # or: claude-plugin, codex, antigravity, agent-skills, agents-md, gemini-cli (legacy), all
# 3. Deploy (symlinks -- regeneration auto-updates)
bin/rdf deploy claude-code # or: bin/rdf deploy gemini-cli
# 4. Initialize a project with governance
cd /path/to/your/project
/r-init # auto-detects project type, suggests profiles
That's it. Your AI agent now has project-specific governance, quality gates, and domain expertise.
Verify:
bin/rdf doctor # health check: artifacts, drift, sync
New to RDF? The 5-minute quickstart walks this path on a plain Flask project, with real output at every step.
| Variable | Default | Purpose |
|---|---|---|
RDF_HOME | resolved from bin/rdf | RDF install root (canonical/, lib/, bin/, state/) |
RDF_CANONICAL | $RDF_HOME/canonical | Canonical content source tree |
RDF_TARGET | ~/.claude | Deploy target for claude-code adapter |
Most users do not need to override these — bin/rdf resolves paths from its own location automatically. Overrides matter only for multi-install setups or CI runs against a fixture tree.
cd /path/to/project
/r-init # auto-detects profiles, generates governance
rdf init creates (inside the target project):
| Path | Purpose |
|---|---|
CLAUDE.md | Project working instructions (typically excluded from commits) |
.rdf/governance/ | conventions, constraints, verification, anti-patterns, architecture |
.rdf/memory/MEMORY.md | Session-persistent project facts |
.git/info/exclude entries | Working-file exclusions (CLAUDE.md, PLAN*.md, .rdf/, etc.) |
The most specific rule wins:
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