openlore-analyze-codebase

name	openlore-analyze-codebase
description	Run a full static OpenLore analysis and summarize architecture, call graph, refactoring issues, and duplicate code. Use when asked to analyze, map, or assess a codebase without LLM inference.

openlore: Analyze Codebase

When to use this skill

Trigger this skill whenever the user asks to analyze a codebase with openlore, with phrasings like:

• "analyze my project / my code"
• "give me a code quality report"
• "what are the structural issues in my codebase"
• "find duplicates in my code"
• explicit command /openlore-analyze-codebase

This skill is read-only — it modifies no files. It produces a report and suggests next steps.

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Step 1 — Confirm the project directory

Ask the user which project to analyze, or confirm the current workspace root.

Which project directory should I analyze?
Options: current workspace root | enter a different path

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Step 2 — Run static analysis

Call the openlore MCP tool analyze_codebase with {"directory": "$DIRECTORY"}.

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Step 3 — Summarize the results

Present a concise summary:

• Project type and detected frameworks
• File count, function count, internal call count
• Top 5 refactoring issues (function name, file, issue type, priority score)
• Detected domains

Also report stack inventory (read directly from .openlore/analysis/ — no extra MCP call needed):

• HTTP routes: N routes across M files — if route-inventory.json exists
• ORM tables: N tables — if schema-inventory.json exists
• Env vars: N total, X required without default — if env-inventory.json exists
• UI components: N components — if ui-inventory.json exists

If none of these files exist, skip this section and suggest running openlore analyze --force.

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Step 4 — Show the call graph

Call the openlore MCP tool get_call_graph with {"directory": "$DIRECTORY"}.

Highlight:

• Hub functions (fanIn ≥ 8) — over-solicited functions, high coupling risk
• Layer violations detected (e.g. a UI layer calling the database directly)

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Step 5 — Show duplicate code report

Call the openlore MCP tool get_duplicate_report with {"directory": "$DIRECTORY"}.

Present a concise summary:

• Overall duplication ratio (e.g. "12% of functions are duplicated")
• Top 3 clone groups sorted by impact (instances × line count):
  • Clone type (exact / structural / near) and similarity score
  • List of instances (file + function name + line range)
• If no duplicates found, note this as a positive signal

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Step 6 — Suggest next steps

Based on the analysis, guide the user through the natural next actions in order:

1. Call get_minimal_context on the highest-priority function — returns callers, callees, body, and test coverage in one call (~300 tokens). Use instead of get_subgraph + get_signatures separately.
2. Call get_cluster on any function to see its full community (tightly coupled neighbors across directories).
3. Call detect_changes to rank recently changed functions by blast radius — spot riskiest commits before reviewing.
4. If significant duplication was found, suggest consolidating clone groups before refactoring
5. Suggest running /openlore-plan-refactor once the user has enough context to act, then /openlore-execute-refactor to apply the plan
6. If the project has OpenSpec specs, call list_spec_domains then search_specs to enable spec-first reasoning (question → requirements → linked source files). To activate search_specs, run openlore analyze --embed or openlore analyze --reindex-specs.

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Absolute constraints

• No code modifications in this workflow
• Never skip the duplication step — it determines the order of subsequent actions
• Always present call graph and duplicate report results even if numbers are low
• Next steps (Step 6) are suggestions, not automatic actions