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Skill slimming strategy

Skill ChrisLamDev/hermes-core-skills/skills/skill-slimming-strategy

25 executable AI agent skills for debugging, planning, token efficiency, and security

Install
npx -y skills add ChrisLamDev/hermes-core-skills --skill skill-slimming-strategy

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

  • 10 stars10 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

Use when a skill SKILL.md is over 2000 words and needs trimming to the Superpowers standard of under 500 words.

SKILL.md

3.6 KB, as published. Nobody here has run it

Skill Slimming Strategy

When to Use

When a SKILL.md file exceeds 2000 words and needs to be made more token-efficient per Superpowers standards.

Classification First

Before slimming, classify the skill into one of these types. Each gets a different treatment:

Type A: Auto-generated API Reference Doc

Signs: Language like "Created On: Jun 06, 2025 | Last Updated On:..." or "This skill auto-generated from official documentation" Action: Replace entirely. Keep only the frontmatter + a 200-word summary with links to official docs. The original content is stale anyway — official docs are more current.

Example:

# Before: 22,733 words of copied PyTorch API docs
# After: 219 words — frontmatter + 3 bullet core concepts + 3 common tasks + links

Type B: Hand-Authored Pipeline / Flow Doc

Signs: Has step-by-step phases, checklists, decision trees. Written by a human for a specific project. Action: Extract details to reference files. Keep the decision tree / flow diagram + one-sentence-per-phase in SKILL.md. Move detailed checklists, commands, and troubleshooting to references/ files.

Example:

# Before: 14,058 words — full paper writing pipeline with all 7 phases detailed
# After: 429 words — flow diagram + phase summary table + links to reference files

Type C: External Package / Vendored Skill

Signs: Has homepage: or repository: pointing to an external GitHub project. Contains vendored Python scripts. Action: Don't touch. These are maintained by external authors. The scripts are the real content, not the SKILL.md prose.

Example: last30days — 19,231 words of scripts + instructions from mvanhorn.

Type D: Project-Specific Debug Flow

Signs: Contains real-world debugging steps learned through painful experience. Lots of "quick fix" ordering. Action: Abstract to decision tree. Keep the symptom diagnosis (Case A vs Case B) + quick-fix order in SKILL.md. Move deep-dive explanations and repair procedures to references/.

Example:

# Before: 8,022 words — full Cocos gray screen debug flow with 5 case analyses
# After: 209 words — symptom check + quick-fix order + reference links

The Process

Step 1: Read first 30 lines + last 10 lines

Understand the structure: is it a flow, a reference, or an external package?

Step 2: Decide the action

Use the classification above.

Step 3: Slim

  • Type A: Replace entire body with summary
  • Type B: Copy detailed sections to references/ files, keep outline only
  • Type C: Skip (add a note "External package, see source for details")
  • Type D: Abstract to decision tree

Step 4: Verify word count

wc -w skills/path/SKILL.md  # Target: <500 words

Pitfalls

  1. External packages (Type C) look like Type A or B — always check for homepage: or repository: in frontmatter before slimming.
  2. Don't delete reference content entirely — save it to references/ files so it's still accessible via the Read tool when needed.
  3. Don't slim skills the user actively usesclaude-code, hermes-agent, and project-specific debugs are frequently loaded. Slimming them requires care.
  4. Batch approach is better — use a Python script for multiple skills (see batch-skill-description-standardization for the batch approach pattern).

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.