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SigMapBetter context. More grounded answers.

Zero-dependency AI context engine. 78.9% hit@5 · 97.9% token reduction · Ask → Validate → Judge → Learn.

Release: v6.10.10·Docs & roadmap update
Benchmark: sigmap-v6.10-main·78.9% hit@5 · 78.9% graph-boosted · 2026-05-12

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 proxy10%52.2%
Prompts per task2.841.66
Retrieval hit@513.6%78.9% (78.9% graph-boosted)
Overall token reduction97.9%
GPT-4o overflow repos16/210/21

Latest saved benchmark run: 2026-05-11 (v6.10.6).

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