SigMap v8.9: Deterministic Context Layer Cuts Token Use 97% for AI Coding Agents
SigMap v8.9 is a deterministic, verifiable grounding layer for AI code work. It claims a 97.0% token reduction, 87.8% hit@5 retrieval accuracy (vs 13.6% random), and 49.2% fewer prompts per task (1.44 vs 2.84). The tool is zero-dependency, fully offline, and works with TypeScript, Python, Go, Rust, Java, Kotlin, Ruby, PHP, Swift, C#, C++, Dart, Scala, Vue, Svelte, GraphQL, SQL, Terraform, R, GDScript, and more.
Core workflow: ask, validate, judge, learn
The workflow goes beyond simple context shrinking:
- Generate a compact signature map once with
npx sigmap. - Ask for files specific to the current task:
sigmap ask "explain the auth flow". This outputs a ranked file list and.context/query-context.mdready to paste. - Validate coverage:
sigmap validate --query "auth login token"checks if context is sufficient. - Judge grounding:
sigmap judge --response response.txt --context .context/query-context.mdscores whether the answer is grounded in the code.
MCP and IDE integration
SigMap is MCP-ready and works with Copilot, Claude Code, Cursor, Windsurf, Codex, OpenCode, and Gemini CLI. The v8.9.1 release adds a squeeze_output MCP tool and squeeze --response CLI flag to compress noisy stack traces, CI logs, or JSON payloads deterministically mid-session — the 19th MCP tool.
Benchmark results
Latest saved benchmark (v8.9.1, July 2026):
| Metric | Without SigMap | With SigMap |
|---|---|---|
| Task success proxy | 10% | 67.8% |
| Prompts per task | 2.84 | 1.44 |
| Retrieval hit@5 | 13.6% | 88% |
| Overall token reduction | — | 97.0% |
| GPT-4o overflow repos | 16/21 | 0/21 |
Performance spans 21 repos and 90 real coding tasks.
Quick start
npx sigmap
sigmap ask "explain the auth flow"
# Outputs ranked file list + .context/query-context.md
# Paste context into AI assistantFor teams or CI, SigMap offers configurable strategies and generalization for monorepos. It also has a dedicated guide for open-source agents (OpenCode, Aider, Cline) and local LLMs (Ollama, llama.cpp, vLLM) — zero cost, full privacy.
📖 Read the full source: HN LLM Tools
👀 See Also
Rever UI Cloner: AI-Optimized Alternative to HTML Scraping for UI Replication
Rever UI Cloner is an API endpoint that provides AI agents with clean design blueprints instead of raw HTML, avoiding context window bloat and UI hallucination issues. It uses the x402 payment protocol requiring a 1 USDC micro-transaction on Base network for agent-to-agent payments.
Canopy: Terminal Dashboard for Managing Multiple Claude Code Agents
Canopy is an open source terminal UI that provides a single dashboard view for tracking multiple AI coding agents running across git worktrees. It shows agent states (running, idle, waiting for input, done, errored) and lets you jump into sessions or send input without fully switching.
Claude Watch: Open Source Tool Visualizes AI-Generated Code Logic
Claude Watch is an open source tool that provides graphical semantic visualization for projects built with AI coding agents like Claude Code. It analyzes code in a nested way and includes AI-powered search to answer questions about project logic.
Definable AI adds self-hosted observability dashboard with single flag
Definable AI, an open-source Python framework for building AI agents, now includes a built-in observability dashboard that can be enabled with one flag. The dashboard provides real-time event streaming, token accounting, latency metrics, and run replay without external dependencies.