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SigMapGrounded context AI can trust. Deterministic. Verifiable.

The deterministic, verifiable grounding layer for AI code work. Proof — 80.4% hit@5 · 95.2% token reduction · zero deps, fully offline.

Release: v8.85.1·New — the CI that builds the package runs pinned code with least-privilege tokens: all 53 workflow actions are commit SHAs, every default token is read-only and write scopes sit on the job that needs them, Dependabot moves the pins by pull request, and CHANGELOG.md no longer ships in every download (257 KB smaller to fetch). No runtime change.
Benchmark: sigmap-v8.85-main·80.4% hit@5 · 95.2% token reduction · 2026-10-27

Who is this for? ​

I am…Go to
New to SigMapQuick start
Using it dailyask · validate · judge
Setting up a team / CIConfig · Strategies
Using open-source agents (OpenCode, Aider, Cline)Open-source agents guide
Running local LLMs (Ollama, llama.cpp, vLLM)Local LLMs guide — zero cost, full privacy
Integrating with MCP, Claude, or CursorMCP setup
Evaluating for a monorepoStrategies · Generalization
Comparing against embeddings or RAGCompare alternatives

30-second start ​

Step 1: Generate context for your project

bash
npx sigmap

Step 2: Ask for relevant files (query-specific context)

bash
sigmap ask "explain the auth flow"
# Outputs: ranked file list + .context/query-context.md (ready to paste)

Step 3: Copy context to your AI assistant

  • Open .context/query-context.md
  • Paste the content into Claude, Copilot, ChatGPT, or your IDE's AI chat
  • Ask: "Explain the auth flow"

Step 4: Save the AI response

bash
# Copy the AI's answer into a file
echo "Paste AI response here..." > response.txt

Step 5: Validate coverage (optional)

bash
sigmap validate --query "auth login token"
# Check if coverage is high enough to trust the response

Step 6: Judge groundedness

bash
sigmap judge --response response.txt --context .context/query-context.md
# Score: shows if the answer is grounded in your code

That flow gives you: a compact signature map · a focused query context · a coverage sanity check · a groundedness score for the answer.

The workflow ​

SigMap is no longer just "shrink the context file." Every step has a purpose:

  • Generate a compact signature map once
  • Ask for the files that matter to the current task
  • Validate whether coverage is high enough to trust the context
  • Judge whether an answer is grounded in the supplied code
  • Learn from good and bad results locally, inside the repo

See the full end-to-end walkthrough to watch this in action on a real repo.

Latest saved benchmark snapshot ​

MetricWithout SigMapWith SigMap
Task success proxy— (proxy, modeled from retrieval tiers)61.9%
Prompts per task2.841.58
Retrieval hit@5 (retrieval corpus)—80.4%
Honest corpus hit@5 (125 tasks)40.0% (single-shot grep)92.8% (2.32× lift)
Overall token reduction—95.2%
GPT-4o overflow repos14/210/21

Latest saved benchmark run: 2026-10-28 (v8.85.1).

Benchmark proof, by question ​

If you want to prove...Open
SigMap reduces token load dramaticallyToken benchmark
SigMap finds the right file more oftenRetrieval benchmark
SigMap reduces retries and wrong-context answersTask benchmark
SigMap keeps large repos inside model limitsQuality benchmark

Where to go next ​

MIT License