skill-optimizer
by mcollinaskill-optimizer helps authors improve AI skills for activation, clarity, and cross-model reliability. Use it for Skill Authoring when a skill is written but not reliably followed, when triggers are weak, regressions appear, or context cost needs trimming. It supports benchmark loops, release gates, and tighter usage fidelity.
This skill scores 84/100, which means it is a solid directory listing candidate: users can likely trigger it reliably and get real workflow leverage for optimizing other skills. The repository gives enough operational structure to justify installation, though users should expect to read the linked rule files for full execution detail.
- Clear activation guidance with explicit trigger terms and use cases for skill optimization, regressions, context budget, and benchmark/release gates.
- Strong workflow structure: measure baseline vs skill-on behavior, diagnose failure patterns, edit for salience, rerun evals, then ship with guardrails.
- Good directory value through modular rule files covering activation design, benchmark loops, regression triage, context budgeting, and release gates.
- No install command in SKILL.md, so users may need to wire it into their own skill setup manually.
- Core procedures are distributed across rule files, so first-time users will need to open multiple documents to execute the full loop.
Overview of skill-optimizer skill
skill-optimizer is a skill-optimizer skill for improving how other AI skills activate, stay concise, and hold up across models. It is most useful for Skill Authoring work: refining a skill pack that is already written, but not reliably followed, or tightening a new skill before release. The real job-to-be-done is not “make the text nicer”; it is to raise usage fidelity, reduce regressions, and keep instruction cost low enough that the skill still gets retrieved under pressure.
Best fit for Skill Authoring
Use skill-optimizer when you need to decide whether a skill is actually being applied, not just whether it sounds good. It is a strong fit for authors who are seeing weak activation, inconsistent compliance, or model-specific drop-off. It is also useful when a skill has too much prose, too many near-duplicate examples, or unclear triggers that make the model miss the intended behavior.
What it changes in practice
This skill focuses on the parts that usually determine success: explicit triggers, integrated examples, tight checklists, and benchmark loops with clear deltas. It is designed to help you answer practical questions like: what cue should make the skill fire, which rule is being ignored, and what edit will improve output without inflating context.
Where it helps most
The strongest use cases are skills that need repeatable evaluation, release gating, or regression control. If your skill includes mandatory output shapes, strict formatting, or behavior that fails quietly, skill-optimizer gives you a structured way to diagnose the failure and rewrite for better salience.
How to Use skill-optimizer skill
Install and first-read order
Install the skill with npx skills add mcollina/skills --skill skill-optimizer. Then read SKILL.md first to understand the core optimization loop, followed by the rule files that carry the detailed procedures. For most users, the best first-pass reading order is SKILL.md, rules/benchmark-loop.md, rules/activation-design.md, rules/regression-triage.md, rules/context-budget.md, and rules/release-gates.md.
Turn a rough goal into a useful prompt
A weak prompt says: “Improve this skill.” A better prompt names the failure mode, the target behavior, and the constraint that matters. For example: “Use skill-optimizer to diagnose why this skill has low activation on model X, reduce unnecessary prose, and rewrite the trigger section so the required footer is not omitted.” That gives the skill enough structure to optimize behavior instead of just rephrasing text.
What input the skill needs
Bring three things whenever possible: the current SKILL.md, one or two example failures, and any benchmark or comparison notes you already have. The skill works best when you can show a before/after gap, such as outputs that pass without the skill but fail with it, or model-specific misses on a single criterion. If you only provide a vague complaint, the optimization loop becomes guesswork.
Workflow that produces better results
Start by measuring baseline versus skill-on behavior, then classify the failure as universal, model-specific, or a regression. Next, edit for salience: move must-not-miss rules upward, add concrete integrated examples, and trim low-signal explanation. Finally, rerun the same scenarios and record deltas before shipping. This is the core skill-optimizer usage pattern and the reason the skill is more decision-oriented than a generic prompt.
skill-optimizer skill FAQ
Is skill-optimizer only for advanced authors?
No. It is beginner-friendly if you are willing to compare outputs and make targeted edits. You do not need a full eval harness to start, but you do need a concrete failure example. Beginners get the most value when they use skill-optimizer to improve one skill rule at a time instead of rewriting an entire pack.
How is it different from a normal prompt?
A normal prompt can ask for improvement, but skill-optimizer is built around activation, regression detection, and release discipline. That matters when the problem is not “what should this skill say?” but “why does the model ignore it, overrun it, or get worse after edits?” The skill-optimizer guide is therefore more operational than a one-off rewrite prompt.
When should I not use it?
Do not use it if you are only looking for copyediting, branding, or a quick summary of a skill. It is also not the right choice when the skill has no clear behavior target or no way to test outcomes. If you cannot name the desired delta, the skill-optimizer skill will have limited leverage.
Does it fit the broader skills ecosystem?
Yes. It is designed for Skill Authoring workflows where skills are installed, tested, revised, and gated over time. If your repo uses supporting rule files and release checks, skill-optimizer fits well because it points you to the exact files that matter for activation and stability rather than treating the skill as a static document.
How to Improve skill-optimizer skill
Give it tighter failure evidence
The fastest way to improve results is to provide a specific miss, not a general preference. Good inputs look like: “Model A ignores the required Refs: footer on noisy prompts,” or “The skill performs well on short tasks but fails when context exceeds 8k tokens.” Those details let skill-optimizer focus on the rule type, the retrieval problem, and the likely fix.
Use stronger source material
If you are updating the skill itself, keep the core guidance in SKILL.md and push deeper procedures into rules/*.md. The repository already signals that the important supporting files are rules/activation-design.md, rules/benchmark-loop.md, rules/context-budget.md, rules/regression-triage.md, and rules/release-gates.md. Improving those files usually gives more value than adding more overview text.
Watch for common failure modes
The main risks are overlong guidance, ambiguous “consider” language, and examples that do not reflect real prompts. A strong skill-optimizer guide should preserve explicit triggers, strict rules where correctness matters, and concise examples that show an integrated workflow. If a revision makes the skill longer without improving activation or delta quality, it likely needs pruning.
Iterate from output, not theory
After the first pass, rerun the same scenarios and compare with and without the skill. If the result improved but one criterion still fails, patch only the failing line and retest. If the skill introduced confusion, tighten the instruction boundaries and add a small positive/negative example pair. That iterative loop is where skill-optimizer delivers its real value.