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Skill refactorer

Skill kpab/claude-fable-5-skills/skills/skill-refactorer

Audit and rewrite existing agent skills, system prompts, and CLAUDE.md files for Claude Fable 5. Use whenever the user mentions migrating, upgrading, porting, or "fixing" a skill or prompt for Fable 5, complains that a previously good skill now produces worse output, or asks why Fable 5 ignores or over-follows old instructions. Also trigger when reviewing any skill written before June 2026.From its SKILL.md

Install
npx -y skills add kpab/claude-fable-5-skills --skill skill-refactorer

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

  • 14 stars14 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.

SKILL.md

3.4 KB, 666 tokens by cl100k_base, as published. Nobody here has run it

Skill Refactorer for Claude Fable 5

Skills and prompts written for earlier Claude models often encode capability compensation: step-by-step hand-holding, exhaustive enumeration of edge cases, and rigid micro-workflows that existed because older models needed them. Fable 5 follows instructions strictly enough that this old scaffolding becomes a straitjacket — the model obeys the obsolete recipe instead of using its own (now better) judgment. The result is output that is worse than no skill at all.

Your job: separate what the instruction protects from how it micromanages, keep the former, delete the latter.

Workflow

  1. Inventory. List every imperative instruction in the target skill/prompt as a separate line item.
  2. Classify each item into exactly one bucket:
    • Guardrail — protects against a real, current risk (data loss, secrets, destructive commands, compliance). KEEP.
    • Preference — a genuine style/format choice the owner still wants (tone, language, naming conventions). KEEP, but compress to one sentence if it sprawls.
    • Compensation — exists only because an older model would otherwise fail (forced step sequences, "always re-read the file before editing", verbose output templates, repeated "do not forget" reminders, enumerations of failure modes the model no longer exhibits). DELETE or replace with a single statement of intent.
  3. Rewrite. For every deleted compensation, ask: "what outcome was this trying to guarantee?" If the outcome still matters, state the outcome once, not the procedure. Example:
    • Before: "Step 1: open the file. Step 2: locate the function. Step 3: copy it to a scratch buffer. Step 4: edit the copy. Step 5: diff against the original. Step 6: ..."
    • After: "Edits must be verifiable: keep a way to diff your change against the original before finalizing."
  4. Tighten the description field. Triggering logic stays in frontmatter description; the body should assume the skill already fired.
  5. Length check. A refactored skill is typically 30–60% shorter. If it isn't, re-run step 2 — you kept compensations.
  6. A/B test. Run one representative task with the old skill and one with the refactored skill. Compare outputs with the user before declaring victory.

Red flags that mark a line as compensation

  • Numbered procedures where the order doesn't actually matter
  • The same warning repeated in different words
  • Output templates that fix structure the user never asked for
  • "Think step by step", "be careful", "double-check" without a concrete check
  • Instructions to narrate or explain internal reasoning in the response (on Fable 5 this can trigger refusals — remove these entirely)

What NOT to touch

  • Domain facts, file paths, API contracts, schemas — these are knowledge, not scaffolding.
  • Safety and permission boundaries. When in doubt whether something is a guardrail or a compensation, ask the user; never silently drop a guardrail.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most context ai engineering skills give in 666 tokens

Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06

  • Dispatch a fresh subagent for each taskin 76 of 1328, across 59 files
  • Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
  • Dispatch a final code reviewer after all tasksin 38 of 1328, across 26 files
  • Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
  • Use the least powerful model capable of the taskin 33 of 1328, across 26 files
  • Create a TodoWrite list for all tasksin 32 of 1328, across 22 files
  • Perform a task review after each implementationin 31 of 1328, across 24 files
  • Extract all tasks and context from the planin 29 of 1328, across 20 files
  • Provide full task text to subagentsin 28 of 1328, across 20 files
  • Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
  • Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
  • Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files

Said here and by no other author read

  • List every imperative instruction in the target skill
  • Classify instructions as guardrails, preferences, or compensation
  • Keep guardrails and preferences
  • Delete instructions that compensate for older model limitations
  • Remove instructions to narrate internal reasoning
  • Run A/B tests comparing old and new skill outputs

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. 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.