Skill Coach: Creating Expert-Level Agent Skills
Encode real domain expertise, not just surface-level instructions. Focus on shibboleths - the deep knowledge that separates novices from experts.
When to Use This Skill
Use for:
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Creating new Agent Skills from scratch
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Reviewing/auditing existing skills
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Improving skill activation rates
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Adding domain expertise to skills
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Debugging why skills don't activate
NOT for:
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General Claude Code features (slash commands, MCPs)
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Non-skill coding advice
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Debugging runtime errors (use domain skills)
Quick Wins
Immediate improvements for existing skills:
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Add NOT clause to description → Prevents false activation
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Add 1-2 anti-patterns → Prevents common mistakes
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Check line count (run validator) → Should be fewer than 500 lines
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Remove dead files → Delete unreferenced scripts/references
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Test activation → Questions that should/shouldn't trigger it
What Makes a Great Skill
Great skills are progressive disclosure machines that:
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Activate precisely - Specific keywords + NOT clause
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Encode shibboleths - Expert knowledge that separates novice from expert
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Surface anti-patterns - "If you see X, that's wrong because Y, use Z"
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Capture temporal knowledge - "Pre-2024: X. 2024+: Y"
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Know their limits - "Use for A, B, C. NOT for D, E, F"
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Provide decision trees - Not templates, but "If X then A, if Y then B"
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Stay under 500 lines - Core in SKILL.md, deep dives in /references
Core Principles
Progressive Disclosure
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Phase 1 (~100 tokens): Metadata - "Should I activate?"
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Phase 2 (<5k tokens): SKILL.md - "How do I do this?"
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Phase 3 (as needed): References - "Show me the details"
Critical: Keep SKILL.md under 500 lines. Split details into /references .
Description Formula
[What] [Use for] [Keywords] NOT for [Exclusions]
❌ Bad: "Helps with images" ⚠️ Better: "Image processing with CLIP" ✅ Good: "CLIP semantic search. Use for image-text matching. Activate on 'CLIP', 'embeddings'. NOT for counting, spatial reasoning."
SKILL.md Template
name: your-skill-name description: [What] [When] [Triggers]. NOT for [Exclusions]. allowed-tools: Read,Write # Minimal only
Skill Name
[One sentence purpose]
When to Use
✅ Use for: [A, B, C] ❌ NOT for: [D, E, F]
Core Instructions
[Step-by-step, decision trees, not templates]
Common Anti-Patterns
[Pattern]
Symptom: [Recognition] Problem: [Why wrong] Solution: [Better approach]
Frontmatter Rules (CRITICAL)
Only these frontmatter keys are allowed by Claude's skill marketplace:
Key Required Purpose
name
✅ Lowercase-hyphenated identifier
description
✅ Activation keywords + NOT clause
allowed-tools
⚠️ Comma-separated tool names
license
❌ e.g., "MIT"
metadata
❌ Custom key-value pairs
Invalid keys that will FAIL upload:
❌ WRONG - These will break skill upload
integrates_with:
- orchestrator triggers:
- "activate on this" tools: Read,Write outputs: formatted text coordinates_with: other-skill python_dependencies:
- numpy
Move custom info to the body:
Integrations
Works with: orchestrator, team-builder
Activation Triggers
Responds to: "create skill", "review skill", "skill quality"
Validation command:
Find invalid frontmatter keys
for skill in .claude/skills/*/SKILL.md; do
sed -n '/^---$/,/^---$/p' "$skill" | grep -E "^[a-zA-Z_-]+:" | cut -d: -f1 |
grep -vE "^(name|description|license|allowed-tools|metadata)$" &&
echo " ^ in $(basename $(dirname $skill))"
done
Skill Structure
Mandatory:
your-skill/ └── SKILL.md # Core instructions (max 500 lines)
Strongly Recommended (self-contained skills):
├── scripts/ # Working code - NOT templates ├── mcp-server/ # Custom MCP if external APIs needed ├── agents/ # Subagent definitions if orchestration needed ├── references/ # Deep dives on domain knowledge └── CHANGELOG.md # Version history
Self-Contained Skills (RECOMMENDED)
Skills with working tools are immediately useful. See references/self-contained-tools.md for full patterns.
Quick decision: External APIs? → MCP. Multi-step workflow? → Subagents. Repeatable operations? → Scripts.
Decision Trees
When to create a NEW skill?
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✅ Domain expertise not in existing skills
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✅ Pattern repeats across 3+ projects
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✅ Anti-patterns you want to prevent
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❌ One-time task → Just do it directly
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❌ Existing skill could be extended → Improve that one
Skill vs Subagent vs MCP?
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Skill: Domain expertise, decision trees (no runtime state)
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Subagent: Multi-step workflows needing tool orchestration
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MCP: External APIs, auth, stateful connections
Skill Creation Process (6 Steps)
Follow these steps in order when creating a new skill:
Step 1: Understand with Concrete Examples
Skip only if usage patterns are already clear. Ask:
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"What functionality should this skill support?"
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"Can you give examples of how it would be used?"
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"What would a user say that should trigger this skill?"
Step 2: Plan Reusable Contents
For each example, analyze:
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How to execute from scratch
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What scripts, references, assets would help with repeated execution
Example analyses:
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pdf-editor for "rotate this PDF" → Needs scripts/rotate_pdf.py
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frontend-webapp-builder → Needs assets/hello-world/ template
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big-query skill → Needs references/schema.md for table schemas
Step 3: Initialize the Skill
Create the skill directory structure:
your-skill/ ├── SKILL.md # Core instructions (max 500 lines) ├── scripts/ # Working code - NOT templates ├── references/ # Deep dives on domain knowledge └── assets/ # Files used in output (templates, icons)
Step 4: Write SKILL.md
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Write in imperative/infinitive form ("To accomplish X, do Y")
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Answer: Purpose? When to use? How to use bundled resources?
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Reference all scripts/references so Claude knows they exist
Step 5: Validate and Package
Validate skill structure and content
python scripts/validate_skill.py <path>
Check for self-contained tool completeness
python scripts/check_self_contained.py <path>
Step 6: Iterate
After real-world use:
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Notice struggles or inefficiencies
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Identify how SKILL.md or bundled resources should be updated
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Implement changes and test again
Common Workflows
Create Skill from Expertise:
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Define scope: What expertise? What keywords? What NOT to handle?
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Write description with keywords and NOT clause
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Add anti-patterns you've observed
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Test activation thoroughly
Debug Activation Issues (flowchart):
Skill not activating when expected? ├── Check description has specific keywords │ ├── NO → Add "Activate on: keyword1, keyword2" │ └── YES → Check if query contains those keywords │ ├── NO → Add missing keyword variations │ └── YES → Check for conflicting NOT clause │ ├── YES → Narrow exclusion scope │ └── NO → Check file structure │ ├── SKILL.md missing → Create it │ └── Wrong location → Move to .claude/skills/
Skill activating when it shouldn't? ├── Missing NOT clause? │ ├── YES → Add "NOT for: exclusion1, exclusion2" │ └── NO → NOT clause too narrow │ └── Expand exclusions based on false positive queries
Run python scripts/test_activation.py <path> to validate
Recursive Self-Improvement (use this skill to improve skills):
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Run python scripts/validate_skill.py <path> → Get validation report
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Run python scripts/check_self_contained.py <path> → Check tool completeness
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Address ERRORS first, then WARNINGS, then SUGGESTIONS
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Re-run validation until clean
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Update CHANGELOG.md with improvements made
Tool Permissions
Guidelines:
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Read-only skill: Read,Grep,Glob
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File modifier: Read,Write,Edit
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Build integration: Read,Write,Bash(npm:,git:)
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⚠️ Never: Unrestricted Bash for untrusted skills
Success Metrics
Metric Target
Correct activation
90%
False positive rate <5%
Token usage <5k typical
Reference Files
File Contents
references/antipatterns.md
Domain shibboleths and anti-pattern catalog with case studies
references/shibboleths.md
Expert vs novice knowledge patterns
references/validation-checklist.md
Complete review and testing guide
references/self-contained-tools.md
Scripts, MCP servers, and subagent implementation patterns
references/scoring-rubric.md
Quantitative skill evaluation (0-10 scoring)
references/skill-composition.md
Cross-skill dependencies and composition patterns
references/skill-lifecycle.md
Maintenance, versioning, and deprecation guidance
references/mcp_vs_scripts.md
Architectural decision guide: Skills vs Agents vs MCPs vs Scripts
This skill guides: Skill creation | Skill auditing | Anti-pattern detection | Progressive disclosure | Domain expertise encoding