agentleFS
mblaubergmblauberg/provenant/skills/refactor/SKILL.md

refactor

Skill1 starsChanged 4 days agoMITView on GitHub

Use for authorised behaviour-preserving refactors and large structural changes proved by a mechanical oracle. Not for new behaviour or read-only review; use test-design or code-review.

196 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use ascii-diagram, debug-fix and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • SethGammonSkill

    ascii-diagram

    Generate perfectly aligned ASCII diagrams — architecture, flow, sequence, box-and-arrow. Uses a programmatic character-grid approach so alignment is guaranteed by math, not token prediction. Includes post-render verification.

    Also in 4 projects that use this

  • zebbernSkill

    debug-fix

    Find and fix a bug or issue — from any source (GitHub issue, error message, user report, or observed behavior)

    Also in 4 projects that use this

  • parcadeiSkill

    agent-context-isolation

    Agent Context Isolation

    Also in 3 projects that use this

  • parcadeiSkill

    agentica-infrastructure

    Reference guide for Agentica multi-agent infrastructure APIs

    Also in 3 projects that use this

  • +8 more from parcadei

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 169 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use implement for a shared seam and batch for disjoint modules into one integration branch.

    line 18

“Common” is measured: kept by at least 6 in 10 of the 169 edited versions of this file we could read. “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

Refactor

Record the cost to remove, observable invariants and the before/after ownership and dependency shape. Choose an oracle before changing structure: the compiler for type-driven moves, a reviewed codemod for uniform changes across about 20 or more sites, a differential run, or characterisation tests. Record the baseline and commands so another agent can repeat the comparison.

Land the shared seam first when independent modules depend on it. Then make the large change under the same oracle, with disjoint write scopes and one writer per worktree. Delete the old path in the same change and rewrite tests coupled to its structure at the public boundary. For a codemod, review the transform and a sample of sites. Structural search can locate candidates, for example sg -p '$A.unwrap()'.

For dependent steps, stack the seam first and its consumers above it; see the stacked PR workflow.

Do not add compatibility shims, legacy readers or fallback paths. Use a temporary strangler only while live traffic or persisted data moves, or behind a weak oracle; give it an expiry and delete it when the move finishes. Bound retries per unit and route the long tail to a senior. Stop on an unexplained oracle failure or discovered behaviour change; use diagnose or scope as appropriate. Report the ownership and dependency delta, evidence and residual uncertainty for code-review.

How it runs

Use implement for a shared seam and batch for disjoint modules into one integration branch. Inspect provenant run --use batch --file work.toml --explain, with worktree and write_scope bindings; submit without --explain.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

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.