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Refactor safely

Skill MiaoY0uShan/FP/.codebuddy/skills/refactor-safely

Finish with Proof: a portable execution protocol for coding agents—diagnose before patching, bound delegation, and verify real outcomes.

Install
npx -y skills add MiaoY0uShan/FP --skill refactor-safely

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Plan and execute safe refactoring using dependency analysis

SKILL.md

1.2 KB, as published. Nobody here has run it

Refactor Safely

Use the knowledge graph to plan and execute refactoring with confidence.

Steps

  1. Use refactor_tool with mode="suggest" for community-driven refactoring suggestions.
  2. Use refactor_tool with mode="dead_code" to find unreferenced code.
  3. For renames, use refactor_tool with mode="rename" to preview all affected locations.
  4. Use apply_refactor_tool with the refactor_id to apply renames.
  5. After changes, run detect_changes_tool to verify the refactoring impact.

Safety Checks

  • Always preview before applying (rename mode gives you an edit list).
  • Check get_impact_radius_tool before major refactors.
  • Use get_affected_flows_tool to ensure no critical paths are broken.
  • Run find_large_functions to identify decomposition targets.

Token Efficiency Rules

  • ALWAYS start with get_minimal_context(task="<your task>") before any other graph tool.
  • Use detail_level="minimal" on all calls. Only escalate to "standard" when minimal is insufficient.
  • Target: complete any review/debug/refactor task in ≤5 tool calls and ≤800 total output tokens.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.