Skill Creator

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." Resolution: Replace with a request for a conclusion plus brief stated rationale (e.g., "state the tier and explain why in one sentence"). Manual CoT scaffolding is deprecated now that adaptive thinking handles this internally; on Claude Fable 5, instructing reasoning-echo can trigger the reasoning-extraction refusal classifier and fail the request outright.

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Behavioral Instructions

Phase 1: Capture Intent

1. Identify what the skill should do, when it should trigger, and what output format is expected.
2. Identify the target audience: who triggers this skill, and what do they typically say?
3. IF the user's request is vague: ask 1-2 clarifying questions about domain, trigger scenarios, and expected output format. Do NOT proceed with a generic skill.

Phase 2: Research (Conditional)

4. IF unfamiliar domain: research the domain before drafting.
   • Use web search for domain terminology, frameworks, and named patterns.
   • Use codebase exploration if the skill is project-specific.
   • Identify 15-30 expert terms that pass the 15-year practitioner test.
   • Identify 5-10 domain anti-patterns with established names.

Phase 3: Draft SKILL.md

5. Write YAML frontmatter with dual-register description (~100 words, pushy, third person, states what AND when).

   • Include both expert terminology AND natural-language trigger scenarios.
   • Include explicit exclusions ("Do NOT use for...").
   • Be aggressive about triggering — current models undertrigger. Include synonyms, edge cases, and adjacent scenarios.
   • Platform limits (Claude Code, 2026): description max 1024 characters. Combined description + when_to_use truncates at 1,536 characters inside a skill-listing budget of ~1% of the context window — least-used skills get dropped first, so don't rely on padding. Use when_to_use to extend the triggering surface beyond the description proper.
   • Other useful frontmatter: disable-model-invocation, user-invocable, allowed-tools, model, effort, context: fork (runs the skill in a forked subagent), paths (glob-scoped activation). SKILL.md follows the Agent Skills open standard (agentskills.io) — custom slash commands have merged into skills.

6. Write Expert Vocabulary Payload (15-30 terms in 3-5 clusters).

   • Place BEFORE behavioral instructions. WHY: vocabulary primes the routing signal before execution begins.
   • Organize in sub-domain clusters of 3-8 terms each.
   • Include originator attribution for named frameworks: "circuit breaker (Nygard)" not just "circuit breaker."
   • Apply the 15-year practitioner test to every term. Remove any that fail.

7. Write Anti-Pattern Watchlist (5-10 named patterns).

   • Place BEFORE behavioral instructions. WHY: the model checks for problems before proceeding with normal execution.
   • Each pattern needs: name + origin, detection signal, resolution step.
   • Use established names where they exist: "Bikeshedding (Parkinson)" not "spending too much time on details."

8. Write Behavioral Instructions (imperative, ordered, conditional).

   • Use imperative verbs: "Scan," "Classify," "Output," not "You should try to."
   • Number every step. Use IF/THEN for branching logic.
   • Include WHY for non-obvious steps. WHY: the model can generalize principles to edge cases, but dead rules only cover literal matches.
   • Start with an anti-pattern scan step.
   • Keep the body lean. Claude 4.5+/5 follows brief instructions reliably — enumerating every case and hedging every step is tuned for older, weaker models and can degrade output on current ones. When migrating a skill, test whether removing instructions improves the result before adding any.
   • Never instruct the model to echo or transcribe its internal reasoning ("show your chain of thought," "explain your thinking step by step"). Ask for conclusions with brief stated rationale instead — on Claude Fable 5, reasoning-echo instructions can trigger the reasoning-extraction refusal classifier.

9. Write Output Format specification.

   • Define required fields, structure, and templates.
   • Use structured formats (YAML, tables, numbered lists) over prose.

10. Write 3-5 diverse Examples (BAD vs GOOD or input-to-output).

    • Cover different cases including at least one hard case.
    • Place the most representative example LAST. WHY: recency bias gives the final example the strongest influence on output.

11. Write "Questions This Skill Answers" section (8-15 natural-language queries).

    • Place at the END of the file. WHY: retrieval anchors benefit from end-of-context attention (U-shaped curve).
    • Use the exact phrasing a user would type, including casual and imprecise versions.
    • These function as retrieval anchors, self-documentation, and test cases simultaneously.