ask
01Command
Ask a codebase question and get a cited, synthesised answer with a confidence rating (costs an LLM call).
Codebase intelligence for AI and humans: code health scores, auto-generated docs, git analytics, dead code detection, and architectural decisions via MCP.
Command
Ask a codebase question and get a cited, synthesised answer with a confidence rating (costs an LLM call).
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Triage card for files, modules, or symbols — layer, hotspot, fix history, freshness (relationships, not source bytes).
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Ingest or inspect test-coverage reports — LCOV, Cobertura/Clover, or coverage.py .coverage (builds the per-test map when contexts are present).
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Report unreachable files, unused exports, and zombie packages, tiered by confidence.
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Work with architectural decisions — list, inspect health, add, or confirm auto-proposed decisions.
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Diagnose the Repowise setup — install, API keys, index/store drift — and optionally repair it.
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Export the wiki (or architecture model) to markdown, HTML, JSON, or Structurizr DSL.
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Show Repowise code-health — KPIs, lowest-scoring files, refactoring targets, trends, or per-file markers.
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Print the tests whose coverage intersects a change's changed lines — for a commit, base..head range, or staged diff.
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Set up Repowise for this codebase. Installs if needed, asks about your preferences, and runs the indexing.
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Rebuild the Repowise vector store by re-embedding all wiki pages. No LLM calls — only embedding API calls.
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Rank a live change for review using a repo-relative percentile and an auditable supporting diff-shape score.
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Search the Repowise wiki using natural language, full-text, or symbol search.
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Scan for security signals — working-tree scanning already runs during init/update; use --history to walk full git history for leaked secrets.
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Check the health of your Repowise index — sync state, page counts, provider, and token usage.
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Read one symbol's body with live-verified line bounds (path::Name, live range, or distill omission ref).
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Trigger an incremental Repowise update to sync documentation with recent code changes.
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Why the code is shaped this way — decisions, rationale, and git archaeology (question, path, or decision-health dashboard).