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SigMapGrounded context AI can trust. Deterministic. Verifiable.

The deterministic, verifiable grounding layer for AI code work. Proof — 78.9% hit@5 · 96.8% token reduction · zero deps, fully offline.

Release: v8.32.1·New — a retrieval gate you can trust: the benchmark now regenerates every index it scores, so a stale artifact can no longer read as a regression. A new 61-task JVM corpus mined from spring-petclinic and akka scores against other repos — outside the feedback loop that shifts scores whenever SigMap's own source changes — and it immediately caught a real one-task regression the previous gate would have missed. Separately, every extractor now discloses what a ceiling dropped instead of truncating silently. Zero dependencies, offline, deterministic.
Benchmark: sigmap-v8.32-main·78.9% hit@5 · 96.8% token reduction · 2026-09-12

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)62.9%
Prompts per task2.841.58
Retrieval hit@544.0% (single-shot grep)78.9% (1.79× honest lift)
Overall token reduction96.8%
GPT-4o overflow repos16/210/21

Latest saved benchmark run: 2026-09-12 (v8.32.1).

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