By artk0de
Use git signals (ownership, churn, bug history) to drive code generation and analysis: reorder search results by code stability, find root causes of bugs, select battle-tested templates, and assess project health before making changes.
Developer explore, understand, explain, investigate code — "how does X work", "show me the architecture of Y", "what does Z do", "where is X used", "find all X", "antipatterns in X", "best example of X". Also pre-generation investigation — "before I code/change/modify/refactor X", "what should I know before touching X", "research before coding", "context for changing X", "risks before refactoring X". NOT for active bugs (use bug-hunt), NOT for standalone code health scan without specific area (use risk-assessment)
Agentic-only enrichment skill — surface battle-tested reference code from project as templates for generation / modification. Three-level locality cascade (target subdomain → domain → project), quality gate by overlay labels + project-wide proven rerank preset. Returns ranked list of reference chunks + locality annotation (L1 / L2 / L3 / none). Invoked by `tea-rags:data-driven-generation` Step 2 (TEMPLATE), `dinopowers:writing-plans` (per code-gen Task), `dinopowers:executing-plans` (per Task during execute). Skipped when no `positiveIds` / `positiveCode` and no `behaviorQuery` available.
Two ways to constrain a tea-rags search beyond `query` + `pathPattern`. Pick the
Force full re-index zero downtime. Builds new versioned collection in background while search continues on current. Alias switches atomically when done. Requires explicit user confirmation — NEVER invoke automatically.
Index/reindex codebase. First time — register alias + index in one command (index-codebase --name). Already indexed — incremental reindex (only changed files).
Executes bash commands
Hook triggers when Bash tool is used
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Trajectory Enrichment-Aware RAG for Coding Agents
Your coding agent copies the first code it finds — not the right one.
TeaRAGs is an MCP server for code search that enriches every retrieved chunk with git history: authorship, churn, bug-fix rate, ownership. Your agent stops learning from hotspots and starts learning from stable, owned, battle-tested code.
📖 Full documentation · 🏁 15-minute quickstart · 🧠 Core concepts
Every new developer pays in hours. Every fresh agent session pays in tokens. Naming conventions, domain logic, local idioms — all of it has to be rebuilt from scratch, every time.
Confusing names mean the agent reads more files. More files mean more tokens, slower responses, and a higher chance of picking the wrong example. Your codebase's technical debt is now your AI bill.
Standard code search ranks by embedding similarity alone. It doesn't know which function gets bug-fixed every sprint, which module hasn't been touched in two years, or whose name is on the commits. So the agent copies whatever looks similar — including the broken examples.
TeaRAGs gives your agent two things it can't get from vanilla code search.
Retrieved code comes with signals about who wrote it, how stable it is, how often it gets bug-fixed, and how impactful a change would be. Semantic similarity stops being the whole answer — it becomes the floor.
TeaRAGs ships agent skills — ready-made playbooks that tell your agent when and how to use the signals. No prompt engineering required:
explore — orient in an unfamiliar codebasedata-driven-generation — write code backed by stable, owned templatesrisk-assessment — know what you'd break before you break itrefactoring-scan · bug-hunt · pattern-search — and moreInstall the plugin, your agent learns the workflow. See all skills →
Bonus: dinopowers — a companion plugin with 10 wrappers over
superpowers:* skills (Jesse Vincent's
skills library for Claude Code) that inject tea-rags signals into brainstorming,
planning, debugging, TDD, review, and completion flows. Mean eval delta +71pp
across 136 cases.
Learn more →
Your agent writes new code backed by stable, canonical templates — modules
with a low bug-fix rate, long stability, and a clear owner. No more copying from
last sprint's hotspot. Skill: data-driven-generation ·
Why stable code is safer →
Find the 5% of code responsible for 80% of incidents. High churn + high
bug-fix rate + concentrated ownership = your next production issue — and your
next refactoring candidate. Skills: refactoring-scan, bug-hunt
npx claudepluginhub artk0de/tearags-mcp --plugin tea-ragsAutomated TeaRAGs installation wizard — Node.js, embedding providers, Qdrant, MCP configuration
Wrapper skills over superpowers:* that inject tea-rags signals (risk, ownership, churn, impact) into brainstorming, planning, execution, debugging, and review flows
Pre-merge AI code review — drive a GitHub-PR-style review of the worktree diff in VS Code (inline comments, threads, Ask-agent, approve/decline) and apply fixes until approved. No GitHub required.
Wrapper skills over superpowers:* that inject tea-rags signals (risk, ownership, churn, impact) into brainstorming, planning, execution, debugging, and review flows
AI-powered codebase understanding assistant. Learn design patterns, analyze impact, trace code flows, and understand any codebase through information theory principles. Includes 6 Agent Skills for automatic analysis triggering.
Personal Claude Code + Codex dev stack: security hooks, AI-first code conventions, /security-review, /repo-map, /stack-check, portable statusline. Designed to complement other skills-based plugins, not replace them.
Codebase exploration, refactoring, and quality analysis
Repository knowledge engine plugin. Bundles CLI-backed slash commands (ag-setup / ag-init / ag-refresh / ag-ask) plus the agent-repo-init skill. Works in both Claude Code and Codex CLI.
Analyze local repos for code health, complexity, test coverage gaps - multi-dimensional health analysis combining complexity + churn + coverage