Skill Analyzer

Classify every step of a target skill as SCRIPT (deterministic) or LLM (judgment), then propose a script interface and a simplified SKILL.md.

Deliver the analysis in the response. Write the script and simplified SKILL.md to disk only when the user asks to apply the changes.

Process

1. Inventory the steps

Read the target skill and list every discrete action, instruction, or decision point as a numbered step - including implicit steps the skill assumes (e.g. "read the file" before "edit the file"). Done when no instruction in the skill is left unaccounted for.

2. Classify each step

A step is blind when a script can perform it for every valid input without reading or understanding anything.

• Blind → SCRIPT: CLI commands with known arguments and file I/O on known paths. Regex or template transforms and git operations with known names also qualify.
• Not blind → LLM: interpretation of intent and choices between valid outputs. Quality judgments, prose generation, and ambiguous-reference resolution also require the LLM.

When in doubt, classify LLM - judgment kept in the SKILL.md beats a brittle script. For borderline patterns (filler-word removal, conditional templates, formatters) and worked end-to-end examples, see references/patterns.md.

Output one row per step:

| # | Step | Category | Rationale |

Done when every inventoried step has a category and a rationale.

3. Propose the script

Collect the SCRIPT steps into one shell or Python script, full source in a fenced block with a usage comment header. The script takes explicit inputs (paths, flags), produces deterministic outputs (files, stdout, exit codes), reports invalid input and exits nonzero, and is invocable from one SKILL.md instruction like "Run ./scripts/prepare.sh <input>". Done when every SCRIPT row in the classification table is covered by the script.

4. Propose the simplified SKILL.md

Rewrite the target SKILL.md: keep the frontmatter and every LLM step; replace the SCRIPT steps with the one instruction that invokes the script. Done when every LLM step survives and no SCRIPT step remains outside that one instruction.

5. Summarize the split

Report the step counts and SCRIPT/LLM percentages, then a verdict: is the split worth it? Name each step that is only blind for the inputs assumed, and the risk if inputs vary beyond them.