writing-skills

name	writing-skills
description	Use when creating a new skill, editing an existing skill, writing a SKILL.md, or verifying a skill works before deployment.

Writing Skills

A skill is a directory containing a SKILL.md file plus optional reference files and scripts. Skills give agents reusable domain-specific context, workflows, and tools.

Core insight: Writing skills is tdd applied to process documentation. Establish baseline behavior first (RED), write the skill (GREEN), then close loopholes (REFACTOR).

When to create a skill

Create when:

• The technique wasn't intuitively obvious
• You'd reference it again across different projects
• Others would benefit

Don't create for:

• One-off solutions
• Standard practices well-documented elsewhere
• Project-specific conventions (put those in a project config file)
• Things enforceable by automation — save skills for judgment calls

Skill types

• Technique — concrete method with steps (e.g. condition-based-waiting)
• Pattern — way of thinking about a class of problems (e.g. flatten-with-flags)
• Reference — docs, syntax guides, API reference (e.g. bigquery-schemas)

SKILL.md structure

---
name: skill-name-with-hyphens
description: Use when [specific triggering conditions and symptoms]
---

# Skill Name

## Overview
Core principle in 1-2 sentences.

## When to use
Symptoms, contexts, triggers. When NOT to use.

## Core pattern / Quick reference
Steps or code.

## Common mistakes
What goes wrong + fixes.

Frontmatter rules:

• Only name and description fields are supported
• name: letters, numbers, hyphens only (no parentheses or special chars)
• description: max 1024 chars; see below for critical rules

Descriptions: triggers only, never workflow summaries

The description is how the agent decides whether to load your skill. It must describe only when to use the skill — never summarize what the skill does or how it works.

Summarizing the workflow in the description causes agents to follow the description as a shortcut and skip reading the full skill body.

# Bad: summarizes workflow — agent may follow this instead of reading the skill
description: Use when executing plans — dispatches subagent per task with code review between tasks

# Bad: first person
description: I can help you process Excel files

# Good: triggering conditions only, third person
description: Use when executing implementation plans with independent tasks

# Good: specific symptoms, no workflow
description: Use when tests have race conditions, timing dependencies, or pass/fail inconsistently

Rules:

• Start with "Use when..."
• Write in third person (injected into system prompt)
• Include concrete symptoms, situations, file types, contexts
• Describe the problem, not language-specific symptoms unless the skill is language-specific

Formatting

Use structured text, not diagrams or tables:

• Diagrams (flowcharts, graphs) are token-expensive and rarely worth the cost — use numbered steps or nested bullets instead
• Markdown tables add formatting overhead — use bulleted lists instead
• Prefer short, scannable prose and bullets over visual structure

File structure

Use progressive disclosure: SKILL.md is the table of contents; details live in linked files loaded only when needed.

my-skill/
├── SKILL.md          # Overview + links (required)
├── reference.md      # Heavy reference: API docs, schemas (100+ lines)
├── examples.md       # Input/output pairs
└── scripts/
    └── validate.py   # Reusable utility scripts

• Keep references one level deep from SKILL.md — avoid chains (SKILL.md → a.md → b.md)
• Keep inline: principles, concepts, code patterns under ~50 lines
• Split to separate files: heavy reference (100+ lines), reusable scripts/tools
• Name files descriptively: form_validation_rules.md not doc2.md
• For reference files >100 lines, add a table of contents at the top
• Use forward slashes in all paths — never backslashes

Token efficiency

Skills load into every conversation. Every token counts.

Targets:

• Frequently-loaded skills: under 200 words
• Other skills: under 500 words

Techniques:

• Reference --help instead of documenting all flags inline
• Cross-reference other skills instead of repeating their content
• One excellent example beats several mediocre ones
• Remove anything the agent already knows

Keyword coverage for discoverability

Use terms agents would search for when encountering a problem:

• Error messages: "Hook timed out", "ENOTEMPTY", "race condition"
• Symptoms: "flaky", "hanging", "zombie", "pollution"
• Synonyms: "timeout/hang/freeze", "cleanup/teardown/afterEach"
• Tool names, library names, file types

Workflow and checklist pattern

For multi-step tasks, give the agent a copyable progress checklist:

Task Progress:
- [ ] Step 1: ...
- [ ] Step 2: ...
- [ ] Step 3: ...

Then describe each step. Include feedback loops: run validator → fix errors → repeat.

Examples pattern

For output quality that depends on style, provide input/output pairs:

**Example 1:**
Input: <user request>
Output:
<desired output>

One excellent, complete, well-commented example is better than many thin ones.

Testing skills (RED-GREEN-REFACTOR)

Never deploy a skill you haven't tested. No exceptions.

RED — establish baseline: Run the target scenario with a fresh agent session without the skill. Document what choices the agent made, what rationalizations it used (verbatim), and which pressures triggered failures.

GREEN — write minimal skill: Write a skill that addresses those specific failures. Don't add content for hypothetical cases. Re-run with the skill and verify compliance.

REFACTOR — close loopholes: If the agent finds a new rationalization, add an explicit counter. Re-test until bulletproof.

Testing approaches by skill type:

• Discipline-enforcing: pressure scenarios (time pressure, sunk cost, authority) — success = agent complies under maximum pressure
• Technique: application and variation scenarios — success = agent applies technique correctly to new cases
• Pattern: recognition and counter-example scenarios — success = agent knows when and when not to apply
• Reference: retrieval and application scenarios — success = agent finds and correctly uses the right information

Using code while testing skills

If the agent needs to write code to test a skill, use test/<skill-name>/ within the current working directory. For example, when testing the pull-requests skill, write test code to test/pull-requests/. Clean up the directory after testing is complete.

Bulletproofing discipline-enforcing skills

Skills that enforce rules need to actively resist rationalization. Agents will find loopholes under pressure.

Close every loophole explicitly — state the rule, then forbid specific workarounds:

Write code before test? Delete it. Start over.

No exceptions:
- Don't keep it as "reference"
- Don't "adapt" it while writing tests
- Delete means delete

Add a rationalization list built from your baseline testing:

• "Too simple to test" → Simple things break. Test takes 30 seconds.
• "I'll add tests after" → Tests after prove what code does, not what it should do.

Add a red flags list so agents can self-check:

## Red flags — stop and reassess
- Thinking "this is too simple to need a test"
- Thinking "I'll test it after"
- Thinking "this situation is different because..."

Skill creation checklist

RED — baseline first:

• Run target scenario without the skill
• Document exact agent behavior and rationalizations verbatim
• Identify patterns in failures

GREEN — write minimal skill:

• name uses only letters, numbers, hyphens
• description starts with "Use when...", third person, triggers only (no workflow summary)
• Body addresses the specific failures from baseline testing
• Keywords present for discoverability
• No diagrams; no markdown tables; use bullets and prose
• One strong example (not multi-language)
• Run scenarios with skill by copying the skill in your prompt to the agent — verify compliance

REFACTOR — close loopholes:

• New rationalizations? Add explicit counters
• Rationalization list built from test iterations (for discipline skills)
• Red flags list present (for discipline skills)
• Re-tested until bulletproof

Quality checks:

• Body under 500 lines; frequently-loaded skills under 200 words
• No workflow summary in description
• No narrative storytelling ("in session X we found...")
• Supporting files only for heavy reference or reusable tools
• All file paths use forward slashes

Anti-patterns

• Workflow summary in description — agents shortcut the full skill body
• Diagrams and flowcharts — token-expensive; use structured text instead
• Markdown tables — use bulleted lists instead
• Narrative examples ("in session X...") — too specific, not reusable
• Multi-language examples — diluted quality, maintenance burden
• Deeply nested references — agents may partially read and miss content
• Vague names: "Helper", "Utils" — not discoverable
• Magic numbers in scripts without justification — agent can't reason about them
• Windows-style paths (scripts\file.py) — breaks on Unix systems
• Time-sensitive information ("before August 2025...") — becomes wrong silently