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.md ready 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.md scores 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 assistant

For 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.