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SigMap

SigMap is a command-line tool that builds a deterministic signature-and-evidence map of a code repository and retrieves focused context for AI coding tasks. Its documented operations include context generation, coverage validation, groundedness scoring and local learning from useful or misleading files. The website describes offline processing, no native dependencies, support for 38 languages and MCP-based lookups.

Explore the practical guide ↓

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Understand the project

Practical overview

When it fits

Use SigMap when preparing repository context for an AI coding task, especially when you want to inspect coverage before asking for an answer or assess the answer against the context provided.

Who it is for

  • Developers who want coding assistants to work from focused repository context.
  • Teams using MCP-compatible coding clients that need on-demand repository lookups.
  • Developers who want to check context coverage or assess whether an AI answer is supported by the code provided.

Documented uses

  • Generate a focused context file for a repository question.
  • Check whether retrieved context covers a coding task.
  • Score an AI answer against its supplied code context.
  • Improve local retrieval weights by marking files helpful or misleading.
Before you choose

Requirements & limits

The documented quick start uses `npx sigmap`; it also gives a global npm installation option. The site describes the workflow as offline.

  • The quick-start workflow includes a manual copy-and-paste step between the context file and an AI assistant.
  • SigMap documents MCP lookups for dynamic retrieval as a separate integration path.
  • Coverage validation and groundedness scoring are distinct operations: one checks context coverage, while the other assesses an answer against context.
  • The site reports support for 38 languages; benchmark results should be read as reported measurements, not guarantees for a particular repository.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare and validate focused repository context

Create context for a code question and check its coverage before passing it to an AI assistant.

Before you start

  • A code repository and a command-line environment with npm/npx.
  • An AI assistant to receive the copied context.

Steps

  1. Run the command below to generate the default context file and signature index.

    Documented command
    npx sigmap

  2. Run sigmap ask "explain the auth flow" to create focused context for the task.

  3. Run sigmap validate --query "auth login token" to check coverage; address low coverage before relying on the context.

  4. Copy .context/query-context.md into the AI assistant and ask the question with the context included.

Expected outcome

A task-focused context file that has been checked for query coverage and can be supplied to an AI assistant.

Guide limitations

  • Coverage validation checks whether context covers a query; it does not establish that an AI response is correct.
  • The guide's example query and paths are illustrative; repository-specific results can differ.
Documentation 1 ↗
Source-based practical guide

Score an AI answer against SigMap context

Assess whether an AI-generated answer is grounded in the context used for the task.

Before you start

  • The focused context file created for the task.
  • The AI assistant's response saved as response.txt.

Steps

  1. Save the assistant's answer to response.txt.

  2. Run sigmap judge --response response.txt --context .context/query-context.md.

  3. Review the reported score and PASS/FAIL indication.

Expected outcome

A score from 0.0 to 1.0 and a PASS/FAIL indication about whether the answer appears supported by the supplied code context.

Guide limitations

  • The score is an assessment of grounding against the supplied context, not a guarantee of factual correctness.
Documentation 1 ↗
Evidence and transparency

Sources & review

sigmap.io ↗Guide documentation ↗
Source record details
Source availability
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Sources checked
5 November 2026
Original website title
SigMap — the deterministic, verifiable grounding layer for AI code work | SigMap · Official homepage title · retained in its source language
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