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mcollina/skill-optimizer

Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark and release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.

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Overview
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Files

activation-design.mdrules/

name:
activation-design
description:
Patterns that increase skill activation and instruction uptake
metadata:
{"tags":"activation, salience, retrieval, instruction-design"}

Activation design

Make triggers explicit

List concrete user/task signals near the top of the skill:

  • domain nouns (e.g. CSV, OAuth callback, Fastify plugin)
  • failure cues (timeouts, backpressure, flaky tests)
  • output shape cues (commit footer, schema, endpoint)

Front-load non-negotiables

If behavior is frequently missed, move it into a numbered top-level checklist. Use imperative wording:

  • "Use pipeline(...)"
  • "Include Refs: when requested"
  • "Do not omit ..."

Use integrated examples

Add examples that combine multiple rules likely needed together in real tasks. A good integrated example:

  • starts from realistic input
  • includes critical decision points
  • shows final output format

Avoid ambiguous wording

Replace fuzzy phrasing:

  • "consider using"
  • "you may want"

With strict phrasing when correctness matters:

  • "use"
  • "must include"
  • "do not omit"

Activation anti-patterns

  • burying critical rules deep in long files
  • splitting one core behavior across too many documents
  • examples that are toy-only and do not reflect real prompts

rules

activation-design.md

benchmark-loop.md

context-budget.md

regression-triage.md

release-gates.md

SKILL.md

tile.json