skills/optimise-skill/SKILL.md
Analyzes SKILL.md and supporting files, then produces an optimized rewrite that is clearer, more concise, and gives the agent more freedom to adapt.
npx skillsauth add laitszkin/apollo-toolkit optimise-skillInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Analyze a skill's full directory and produce an optimized rewrite. The goal is not to change what the skill does — it's to make the skill easier for an LLM to understand and execute effectively, by removing redundancy, separating concerns, and replacing rigid instructions with guiding principles.
A well-structured skill separates three distinct concerns:
| Layer | What it contains | Where it lives | |---|---|---| | Behavioral | How to think, what to check, what principles to follow | SKILL.md | | Format | What the output structure looks like | Template files | | Tool | CLI flags, API params, external commands | Reference files |
The most common problem in unoptimized skills is mixing these layers — templates that tell the agent what to do, SKILL.md that describes output formats, references that contain behavioral rules. Your job is to untangle them.
Read the full skill directory — SKILL.md, templates, references. Before you can optimize, understand:
Go through every section across all files and classify it as behavioral, format, or tool guidance. When you find content in the wrong layer, move it:
The key question: "If I removed this file, what would the agent lose?" If the answer is "behavioral guidance," that content belongs in SKILL.md. If it's "structure to fill," it belongs in a template. "CLI flags to look up" belongs in references.
For each field, section, and table in the skill's output (template), ask who actually reads it downstream. Prune what nobody consumes:
Depends on fields)Compare closely related rules across files. Contradictions confuse agents. Look for:
After untangling, restructure SKILL.md so it guides the agent's thinking rather than scripting every keystroke:
Good: "Define error recovery rules that fit the specific task. Think through retry limits, escalation paths, and what happens mid-batch. The ALWAYS / ASK FIRST / NEVER framework is one useful structure — adapt it as needed."
Avoid: "Error Recovery: Worker fails → retry once. Fails again → pause. Merge conflict → coordinator resolves."
The former teaches the agent a thought process. The latter gives it text to copy-paste, which is brittle and task-specific.
Principles for writing guidance:
Before delivering, confirm:
references/example_skill.md — Example of optimized skill structure (optional reference)development
Review a pull request — interactive PR selection via `gh`, 4-dimension code review (hallucinated code, architecture, performance, test validity), then post severity-graded comments with fix suggestions on the PR. Not for spec-based review — use `review` instead.
development
Read a user-specified PDF that marks the week's key financial events, deeply research each marked event with current sources, capture any additional breaking financial developments, and produce a concise Chinese-capable PDF briefing that explains what happened and why it matters.
documentation
Generate long-form videos (more than 10 minutes) by following user instructions and invoking related skills only when needed (`openai-text-to-image-storyboard`, `docs-to-voice`, `remotion-best-practices`). For text inputs, extract a complete long-form story arc, generate fresh storyboard images (no reuse of previously generated pictures), and render a 16:9 animated long-form video.
tools
協助完成自動化版本發佈。同步文檔、更新版本號、推送 tag 並建立 GitHub Release。