/skill-creator

name: skill-creator
description: |
  Creates high-quality Claude Code and Cowork skills using evidence-based principles: expert vocabulary payloads for knowledge routing, dual-register descriptions for reliable triggering, named anti-pattern watchlists for steering past the distribution center, and progressive disclosure architecture for context efficiency. Produces SKILL.md files with structured behavioral instructions, canonical examples, and bundled references.

  Use this skill when the user wants to create a skill, build a custom capability, make a reusable prompt template, or says "I want Claude to always do X." Also triggers when Mission Planner or Agent Creator need to create a domain skill JIT. Works for any domain.

  Do NOT use for creating agent definitions (use Agent Creator) or team composition (use Mission Planner).

Skill Creator

Research-enhanced skill creator that produces higher-quality skills than built-in defaults. Every design decision is grounded in how transformers process context.

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Expert Vocabulary Payload

**Prompt Engineering & Routing:** expert vocabulary payload, dual-register description, vocabulary routing, distributional convergence, attention budget, distribution center, right altitude, retrieval anchor

**Skill Architecture:** progressive disclosure, context window management, U-shaped attention curve, YAML frontmatter, trigger surface, structural delineation, three-level loading (metadata / SKILL.md / references)

**Behavioral Design:** anti-pattern watchlist, detection signal, counter-example, imperative instruction, conditional branching, evaluation criteria

**Quality & Testing:** canonical example, few-shot learning, 15-year practitioner test, consultant-speak (banned), over-prompting, recency bias

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Anti-Pattern Watchlist

These are anti-patterns in the **skills this creator generates**. Scan every generated SKILL.md against this list before delivery.

1. Generic Consultant-Speak in Vocabulary

**Detection:** Vocabulary payload contains terms like "best practices," "leverage," "synergy," "robust solution," "scalable framework," or "holistic approach." Apply the 15-year practitioner test: would a senior domain expert use this exact term with a peer? If not, it fails. **Resolution:** Replace every generic term with the precise domain term it vaguely gestures at. "Best practices for error handling" becomes "circuit breaker pattern (Nygard), exponential backoff, dead letter queue."

2. Over-Prompting

**Detection:** SKILL.md exceeds 500 lines. The same concept is stated 2-3 times in different words "for emphasis." Instructions contain hedging phrases ("you might want to consider," "it could be helpful to"). **Resolution:** State each instruction once, in imperative form. Remove hedging. Move heavy reference content to `references/`. Test with a minimal version first; add detail only where the model demonstrably fails.

3. Positive-Only Instructions

**Detection:** Zero "do NOT" or "avoid" guidance. No anti-pattern watchlist. The skill only describes what to do, never what not to do. **Resolution:** Add 5-10 domain-specific anti-patterns with named patterns, detection signals, and resolution steps. Without negative constraints, the model gravitates to the distribution center (the most generic, average output).

4. Single-Register Description

**Detection:** Description uses only formal terminology OR only casual language. Test: would the skill trigger if a user said "help me with [casual version of task]"? If not, the casual register is missing. **Resolution:** Rewrite description to include both expert terms (for routing to deep knowledge) and natural-language trigger scenarios (for reliable activation). Add explicit "even if they don't say [formal term]" phrases.

5. Edge-Case Stuffing

**Detection:** More than 15 specific edge-case rules. Long lists of "if X then Y" covering every scenario instead of demonstrating the pattern. **Resolution:** Replace with 3-5 diverse canonical examples that show the pattern. Include one hard case. Let the model generalize from examples rather than memorize rules. Anthropic's documented recommendation is 3-5 examples; LangChain found 3 examples roughly matched 9 in effectiveness on their benchmark (diminishing returns, not a hard ceiling).

6. Paragraph-Form Logic

**Detection:** Complex multi-step behavior described in prose paragraphs. No numbered steps, no IF/THEN conditions, no imperative verbs. **Resolution:** Refactor to imperative ordered steps with explicit conditions. "First check for anti-patterns, and if you find some you should probably address them" becomes "1. Scan input for anti-patterns. IF detected: apply Detect-Name-Explain-Resolve-Prevent. IF none: proceed to step 2."

7. Missing Examples

**Detection:** Zero input-to-output examples or BAD/GOOD pairs in the generated skill. The skill relies entirely on verbal instructions. **Resolution:** Add 3-5 diverse examples. Use BAD vs GOOD pairs for quality standards, input-to-output pairs for workflows. Place the most representative example last (recency bias gives it the strongest influence).

8. Over-Prescription for Current Models

**Detection:** The generated SKILL.md enumerates exhaustive rules, hedges every instruction, or reads like it was tuned for an older, weaker model — dense and enumerative even where the vocabulary and anti-pattern sections are lean. **Resolution:** State the goal, the boundaries, and the verification standard; let the model fill in competent defaults. Keep the description pushy (triggering is still the weak point) but keep the body lean. When migrating an existing skill to a newer model generation, test whether REMOVING instructions improves output before adding more.

9. Reasoning-Echo Instructions

**Detection:** Behavioral instructions tell the model to "show your chain of thought," "transcribe your reasoning process," or "explain your internal thinking step by step." **