The deterministic, verifiable grounding layer for AI code work. Proof — 80.4% hit@5 · 95.7% token reduction · zero deps, fully offline.
log_session_usage, and sigmap --report --sessions shows the latest sessions with their provenance, a cost floor and totals per provenance class — never one blended total. What changed →| I am… | Go to |
|---|---|
| New to SigMap | Quick start |
| Using it daily | ask · validate · judge |
| Setting up a team / CI | Config · 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 Cursor | MCP setup |
| Evaluating for a monorepo | Strategies · Generalization |
| Comparing against embeddings or RAG | Compare alternatives |
Step 1: Generate context for your project
npx sigmapStep 2: Ask for relevant files (query-specific context)
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
.context/query-context.mdStep 4: Save the AI response
# Copy the AI's answer into a file
echo "Paste AI response here..." > response.txtStep 5: Validate coverage (optional)
sigmap validate --query "auth login token"
# Check if coverage is high enough to trust the responseStep 6: Judge groundedness
sigmap judge --response response.txt --context .context/query-context.md
# Score: shows if the answer is grounded in your codeThat flow gives you: a compact signature map · a focused query context · a coverage sanity check · a groundedness score for the answer.
SigMap is no longer just "shrink the context file." Every step has a purpose:
See the full end-to-end walkthrough to watch this in action on a real repo.
| Metric | Without SigMap | With SigMap |
|---|---|---|
| Task success proxy | — (proxy, modeled from retrieval tiers) | 61.9% |
| Prompts per task | 2.84 | 1.58 |
| 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 | — | 95.7% |
| GPT-4o overflow repos | 14/21 | 0/21 |
Latest saved benchmark run: 2026-10-12 (v8.76.0).
| If you want to prove... | Open |
|---|---|
| SigMap reduces token load dramatically | Token benchmark |
| SigMap finds the right file more often | Retrieval benchmark |
| SigMap reduces retries and wrong-context answers | Task benchmark |
| SigMap keeps large repos inside model limits | Quality benchmark |