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rolandtolnayrolandtolnay/llm-toolkit/skills/ripple-check/SKILL.md

ripple-check

Skill1 starsChanged 3 months agoMITView on GitHub

After a fix or improvement, explore the codebase for other places where the same learning might apply. Use when checking whether a bug pattern, wrong assumption, or better approach should ripple to similar code.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Before searching, state the abstract pattern behind the fix: what assumption was wrong, what convention was misunderstood, what was …

    line 13
  2. Distinctive:

    Look for code with shared lineage: same author, same assumption, same seam.

    line 17

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

A fix teaches you something the codebase did not know it had wrong: an assumption that does not hold, a convention that was misread, a better approach a reference revealed. Code written under the same assumption elsewhere carries the same latent problem. This session has already paid to learn the symptom and its cause, so checking the rest of the codebase now is cheap, and finding the same bug later in production is not. The flow that was just fixed is boy-scout's territory; this pass covers everywhere else.

Invoking this skill asks for the check, not for findings. "Checked X, Y, Z; the pattern does not transfer because..." is a complete answer, and forcing findings where there are none is the failure. The report goes to the user, who decides what to act on, so write it so they can judge each candidate without opening the code.

Name the learning

Before searching, state the abstract pattern behind the fix: what assumption was wrong, what convention was misunderstood, what was duplicated with a subtle variation, or what the reference implementation did better. The search is for this pattern, not for the literal code that was changed.

Find and judge candidates

Look for code with shared lineage: same author, same assumption, same seam. Use subagents when the search space spans several areas.

A candidate earns a place in the report only when you can name the specific mechanism it shares with the original issue. The same shape of code does not always carry the same bug; when it does not, say why, briefly.

Report

For each candidate that passes: where it lives, why it is the same problem, and the proposed fix. When nothing passes: say so, summarize what you checked, and explain why the pattern does not transfer.

Make no changes until the user picks, unless the invocation asked you to apply them; then implement the picked items.

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Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.