What it does. Use when creating new skills, editing existing skills, or verifying skills work before deployment Part of obra/superpowers (skills framework and dev methodology) (obra/superpowers).

Upstream	obra/superpowers
Skill file	skills/writing-skills/SKILL.md
License	MIT
Author	Jesse Vincent (obra)
Fetched	2026-09-10

Install

• npx skills add obra/superpowers --skill writing-skills, or copy the skill folder into ~/.claude/skills/writing-skills/.
• Raw file: curl -sL https://raw.githubusercontent.com/obra/superpowers/HEAD/skills/writing-skills/SKILL.md

SKILL.md (verbatim)

name: writing-skills
description: Use when creating new skills, editing existing skills, or verifying skills work before deployment

Writing Skills

Overview

Writing skills IS Test-Driven Development applied to process documentation.

Personal skills live in your runtime's skills directory (~/.claude/skills/ on Claude Code) — see codex-tools.md or gemini-tools.md for the path on those runtimes. Codex, Copilot CLI, and Gemini CLI all also recognize ~/.agents/skills/ as a cross-runtime alias.

You write test cases (pressure scenarios with subagents), watch them fail (baseline behavior), write the skill (documentation), watch tests pass (agents comply), and refactor (close loopholes).

Core principle: If you didn't watch an agent fail without the skill, you don't know if the skill teaches the right thing.

REQUIRED BACKGROUND: You MUST understand superpowers:test-driven-development before using this skill. That skill defines the fundamental RED-GREEN-REFACTOR cycle. This skill adapts TDD to documentation.

Official guidance: For Anthropic's official skill authoring best practices, see anthropic-best-practices.md. This document provides additional patterns and guidelines that complement the TDD-focused approach in this skill.

What is a Skill?

A skill is a reference guide for proven techniques, patterns, or tools. Skills help future agents find and apply effective approaches.

Skills are: Reusable techniques, patterns, tools, reference guides

Skills are NOT: Narratives about how you solved a problem once

TDD Mapping for Skills

TDD Concept	Skill Creation
Test case	Pressure scenario with subagent
Production code	Skill document (SKILL.md)
Test fails (RED)	Agent violates rule without skill (baseline)
Test passes (GREEN)	Agent complies with skill present
Refactor	Close loopholes while maintaining compliance
Write test first	Run baseline scenario BEFORE writing skill
Watch it fail	Document exact rationalizations agent uses
Minimal code	Write skill addressing those specific violations
Watch it pass	Verify agent now complies
Refactor cycle	Find new rationalizations → plug → re-verify

The entire skill creation process follows RED-GREEN-REFACTOR.

When to Create a Skill

Create when:

• Technique wasn't intuitively obvious to you
• You'd reference this again across projects
• Pattern applies broadly (not project-specific)
• Others would benefit

Don't create for:

• One-off solutions
• Standard practices well-documented elsewhere
• Project-specific conventions (put in your instructions file)
• Mechanical constraints (if it's enforceable with regex/validation, automate it—save documentation for judgment calls)

Skill Types

Technique

Concrete method with steps to follow (condition-based-waiting, root-cause-tracing)

Pattern

Way of thinking about problems (flatten-with-flags, test-invariants)

Reference

API docs, syntax guides, tool documentation (office docs)

Directory Structure

skills/
  skill-name/
    SKILL.md              # Main reference (required)
    supporting-file.*     # Only if needed

Flat namespace - all skills in one searchable namespace

Separate files for:

1. Heavy reference (100+ lines) - API docs, comprehensive syntax
2. Reusable tools - Scripts, utilities, templates

Keep inline:

• Principles and concepts
• Code patterns (< 50 lines)
• Everything else

SKILL.md Structure

Frontmatter (YAML):

• Two required fields: name and description (see agentskills.io/specification for all supported fields)
• Max 1024 characters total
• name: Use letters, numbers, and hyphens only (no parentheses, special chars)
• description: Third-person, describes ONLY when to use (NOT what it does)
  • Start with "Use when..." to focus on triggering conditions
  • Include specific symptoms, situations, and contexts
  • NEVER summarize the skill's process or workflow (see SDO section for why)
  • Keep under 500 characters if possible

---
name: Skill-Name-With-Hyphens
description: Use when [specific triggering conditions and symptoms]
---

# Skill Name

## Overview
What is this? Core principle in 1-2 sentences.

## When to Use
[Small inline flowchart IF decision non-obvious]

Bullet list with SYMPTOMS and use cases
When NOT to use

## Core Pattern (for techniques/patterns)
Before/after code comparison

## Quick Reference
Table or bullets for scanning common operations

## Implementation
Inline code for simple patterns
Link to file for heavy reference or reusable tools

## Common Mistakes
What goes wrong + fixes

## Real-World Impact (optional)
Concrete results

Skill Discovery Optimization (SDO)

Critical for discovery: Future agents need to FIND your skill

1. Rich Description Field

Purpose: Your agent reads the description to decide which skills to load for a given task. Make it answer: "Should I read this skill right now?"

Format: Start with "Use when..." to focus on triggering conditions

CRITICAL: Description = When to Use, NOT What the Skill Does

The description should ONLY describe triggering conditions. Do NOT summarize the skill's process or workflow in the description.

Why this matters: Testing revealed that when a description summarizes the skill's workflow, an agent may follow the description instead of reading the full skill content. A description saying "code review between tasks" caused an agent to do ONE review, even though the skill's flowchart clearly showed TWO reviews (spec compliance then code quality).

When the description was changed to just "Use when executing implementation plans with independent tasks" (no workflow summary), the agent correctly read the flowchart and followed the two-stage review process.

The trap: Descriptions that summarize workflow create a shortcut agents will take. The skill body becomes documentation agents skip.

# ❌ BAD: Summarizes workflow - agents may follow this instead of reading skill
description: Use when executing plans - dispatches subagent per task with code review between tasks

# ❌ BAD: Too much process detail
description: Use for TDD - write test first, watch it fail, write minimal code, refactor

# ✅ GOOD: Just triggering conditions, no workflow summary
description: Use when executing implementation plans with independent tasks in the current session

# ✅ GOOD: Triggering conditions only
description: Use when implementing any feature or bugfix, before writing implementation code

Content:

• Use concrete triggers, symptoms, and situations that signal this skill applies
• Describe the problem (race conditions, inconsistent behavior) not language-specific symptoms (setTimeout, sleep)
• Keep triggers technology-agnostic unless the skill itself is technology-specific
• If skill is technology-specific, make that explicit in the trigger
• Write in third person (injected into system prompt)
• NEVER summarize the skill's process or workflow

# ❌ BAD: Too abstract, vague, doesn't include when to use
description: For async testing

# ❌ BAD: First person
description: I can help you with async tests when they're flaky

# ❌ BAD: Mentions technology but skill isn't specific to it
description: Use when tests use setTimeout/sleep and are flaky

# ✅ GOOD: Starts with "Use when", describes problem, no workflow
description: Use when tests have race conditions, timing dependencies, or pass/fail inconsistently

# ✅ GOOD: Technology-specific skill with explicit trigger
description: Use when using React Router and handling authentication redirects

2. Keyword Coverage

Use words an agent would search for:

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

3. Descriptive Naming

Use active voice, verb-first:

• ✅ creating-skills not skill-creation
• ✅ condition-based-waiting not async-test-helpers

4. Token Efficiency (Critical)

Problem: getting-started and frequently-referenced skills load into EVERY conversation. Every token counts.

Target word counts:

• getting-started workflows: <150 words each
• Frequently-loaded skills: <200 words total
• Other skills: <500 words (still be concise)

Techniques:

Move details to tool help:

# ❌ BAD: Document all flags in SKILL.md
search-conversations supports --text, --both, --after DATE, --before DATE, --limit N

# ✅ GOOD: Reference --help
search-conversations supports multiple modes and filters. Run --help for details.

Use cross-references:

# ❌ BAD: Repeat workflow details
When searching, dispatch subagent with template...
[20 lines of repeated instructions]

# ✅ GOOD: Reference other skill
Always use subagents (50-100x context savings). REQUIRED: Use [other-skill-name] for workflow.

Compress examples:

# ❌ BAD: Verbose example (42 words)
your human partner: "How did we handle authentication errors in React Router before?"
You: I'll search past conversations for React Router authentication patterns.
[Dispatch subagent with search query: "React Router authentication error handling 401"]

# ✅ GOOD: Minimal example (20 words)
Partner: "How did we handle auth errors in React Router?"
You: Searching...
[Dispatch subagent → synthesis]

Eliminate redundancy:

• Don't repeat what's in cross-referenced skills
• Don't explain what's obvious from command
• Don't include multiple examples of same pattern

Verification:

wc -w skills/path/SKILL.md
# getting-started workflows: aim for <150 each
# Other frequently-loaded: aim for <200 total

Name by what you DO or core insight:

• ✅ condition-based-waiting > async-test-helpers
• ✅ using-skills not skill-usage
• ✅ flatten-with-flags > data-structure-refactoring
• ✅ root-cause-tracing > debugging-techniques

Gerunds (-ing) work well for processes:

• creating-skills, testing-skills, debugging-with-logs
• Active, describes the action you're taking

5. Cross-Referencing Other Skills

When writing documentation that references other skills:

Use skill name only, with explicit requirement markers:

• ✅ Good: **REQUIRED SUB-SKILL:** Use superpowers:test-driven-development
• ✅ Good: **REQUIRED BACKGROUND:** You MUST understand superpowers:systematic-debugging
• ❌ Bad: See skills/testing/test-driven-development (unclear if required)
• ❌ Bad: @skills/testing/test-driven-development/SKILL.md (force-loads, burns context)

Why no @ links: @ syntax force-loads files immediately, consuming 200k+ context before you need them.

Flowchart Usage

digraph when_flowchart {
    "Need to show information?" [shape=diamond];
    "Decision where I might go wrong?" [shape=diamond];
    "Use markdown" [shape=box];
    "Small inline flowchart" [shape=box];

    "Need to show information?" -> "Decision where I might go wrong?" [label="yes"];
    "Decision where I might go wrong?" -> "Small inline flowchart" [label="yes"];
    "Decision where I might go wrong?" -> "Use markdown" [label="no"];
}

Use flowcharts ONLY for:

• Non-obvious decision points
• Process loops where you might stop too early
• "When to use A vs B" decisions

Never use flowcharts for:

• Reference material → Tables, lists
• Code examples → Markdown blocks
• Linear instructions → Numbered lists
• Labels without semantic meaning (step1, helper2)

See graphviz-conventions.dot in this directory for graphviz style rules.

Visualizing for your human partner: Use render-graphs.js in this directory to render a skill's flowcharts to SVG:

./render-graphs.js ../some-skill           # Each diagram separately
./render-graphs.js ../some-skill --combine # All diagrams in one SVG

Code Examples

One excellent example beats many mediocre ones

Choose most relevant language:

• Testing techniques → TypeScript/JavaScript
• System debugging → Shell/Python
• Data processing → Python

Good example:

• Complete and runnable
• Well-commented explaining WHY
• From real scenario
• Shows pattern clearly
• Ready to adapt (not generic template)

Don't:

• Implement in 5+ languages
• Create fill-in-the-blank templates
• Write contrived examples

You're good at porting - one great example is enough.

File Organization

Self-Contained Skill

defense-in-depth/
  SKILL.md    # Everything inline

When: All content fits, no heavy reference needed

Skill with Reusable Tool

condition-based-waiting/
  SKILL.md    # Overview + patterns
  example.ts  # Working helpers to adapt

When: Tool is reusable code, not just narrative

Skill with Heavy Reference

pptx/
  SKILL.md       # Overview + workflows
  pptxgenjs.md   # 600 lines API reference
  ooxml.md       # 500 lines XML structure
  scripts/       # Executable tools

When: Reference material too large for inline

The Iron Law (Same as TDD)

NO SKILL WITHOUT A FAILING TEST FIRST

This applies to NEW skills AND EDITS to existing skills.

Write skill before testing? Delete it. Start over. Edit skill without testing? Same violation.

No exceptions:

• Not for "simple additions"
• Not for "just adding a section"
• Not for "documentation updates"
• Don't keep untested changes as "reference"
• Don't "adapt" while running tests
• Delete means delete

REQUIRED BACKGROUND: The superpowers:test-driven-development skill explains why this matters. Same principles apply to documentation.

Testing All Skill Types

Different skill types need different test approaches:

Discipline-Enforcing Skills (rules/requirements)

Examples: TDD, verification-before-completion, designing-before-coding

Test with:

• Academic questions: Do they understand the rules?
• Pressure scenarios: Do they comply under stress?
• Multiple pressures combined: time + sunk cost + exhaustion
• Identify rationalizations and add explicit counters

Success criteria: Agent follows rule under maximum pressure

Technique Skills (how-to guides)

Examples: condition-based-waiting, root-cause-tracing, defensive-programming

Test with:

• Application scenarios: Can they apply the technique correctly?
• Variation scenarios: Do they handle edge cases?
• Missing information tests: Do instructions have gaps?

Success criteria: Agent successfully applies technique to new scenario

Pattern Skills (mental models)

Examples: reducing-complexity, information-hiding concepts

Test with:

• Recognition scenarios: Do they recognize when pattern applies?
• Application scenarios: Can they use the mental model?
• Counter-examples: Do they know when NOT to apply?

Success criteria: Agent correctly identifies when/how to apply pattern

Reference Skills (documentation/APIs)

Examples: API documentation, command references, library guides

Test with:

• Retrieval scenarios: Can they find the right information?
• Application scenarios: Can they use what they found correctly?
• Gap testing: Are common use cases covered?

Success criteria: Agent finds and correctly applies reference information

Common Rationalizations for Skipping Testing

Excuse	Reality
"Skill is obviously clear"	Clear to you ≠ clear to other agents. Test it.
"It's just a reference"	References can have gaps, unclear sections. Test retrieval.
"Testing is overkill"	Untested skills have issues. Always. 15 min testing saves hours.
"I'll test if problems emerge"	Problems = agents can't use skill. Test BEFORE deploying.
"Too tedious to test"	Testing is less tedious than debugging bad skill in production.
"I'm confident it's good"	Overconfidence guarantees issues. Test anyway.
"Academic review is enough"	Reading ≠ using. Test application scenarios.
"No time to test"	Deploying untested skill wastes more time fixing it later.

All of these mean: Test before deploying. No exceptions.

Match the Form to the Failure

Before writing guidance, classify the baseline failure. The form that bulletproofs one failure type measurably backfires on another.

Baseline failure	Right form	Wrong form
Skips/violates a rule under pressure (knows better, does it anyway)	Prohibition + rationalization table + red flags (see Bulletproofing below)	Soft guidance ("prefer...", "consider...")
Complies, but output has the wrong shape (bloated prompt, buried verdict, restated spec)	Positive recipe or contract: state what the output IS — its parts, in order	Prohibition list ("don't restate", "never narrate")
Omits a required element from something they already produce	Structural: REQUIRED field or slot in the template they fill in	Prose reminders near the template
Behavior should depend on a condition	Conditional keyed to an observable predicate ("if the brief exists, reference it")	Unconditional rule + exemption clauses

Why prohibitions backfire on shaping problems: under a competing incentive ("make the prompt self-contained"), agents negotiate with "don't X". In head-to-head wording tests on dispatch-prompt guidance, the prohibition arm produced clearly more of the unwanted content than the recipe arm (fully separated distributions), and trended worse than even the no-guidance control — micro-test your own case rather than assuming, but never reach for the prohibition by default. A recipe leaves nothing to negotiate: the output matches the stated shape or it doesn't.

Rules for whichever form you pick:

• No nuance clauses. "Don't X unless it matters" reopens the negotiation — appending a single nuance clause to a winning recipe degraded it from consistent to noisy in the same wording tests. Express a real exception as its own conditional on an observable predicate.
• Exemption clauses don't scope. "This limit doesn't apply to code blocks" still suppresses code blocks. If part of the output must be exempt, restructure so the rule can't reach it.

Bulletproofing Skills Against Rationalization

Skills that enforce discipline (like TDD) need to resist rationalization. Agents are smart and will find loopholes when under pressure.

Scope: this toolkit is for discipline failures — an agent that knows the rule and skips it under pressure. For wrong-shaped output or omitted elements, prohibition-based bulletproofing backfires; use the forms in Match the Form to the Failure instead.

Psychology note: Understanding WHY persuasion techniques work helps you apply them systematically. See persuasion-principles.md for research foundation (Cialdini, 2021; Meincke et al., 2025) on authority, commitment, scarcity, social proof, and unity principles.

Close Every Loophole Explicitly

Don't just state the rule - forbid specific workarounds:

<Bad> ```markdown Write code before test? Delete it. ``` </Bad><Good> ```markdown Write code before test? Delete it. Start over.

No exceptions:

• Don't keep it as "reference"
• Don't "adapt" it while writing tests
• Don't look at it
• Delete means delete

</Good>

### Address "Spirit vs Letter" Arguments

Add foundational principle early:

```markdown
**Violating the letter of the rules is violating the spirit of the rules.**

This cuts off entire class of "I'm following the spirit" rationalizations.

Build Rationalization Table

Capture rationalizations from baseline testing (see Testing section below). Every excuse agents make goes in the table:

| Excuse | Reality |
|--------|---------|
| "Too simple to test" | Simple code breaks. Test takes 30 seconds. |
| "I'll test after" | Tests passing immediately prove nothing. |
| "Tests after achieve same goals" | Tests-after = "what does this do?" Tests-first = "what should this do?" |

Create Red Flags List

Make it easy for agents to self-check when rationalizing:

## Red Flags - STOP and Start Over

- Code before test
- "I already manually tested it"
- "Tests after achieve the same purpose"
- "It's about spirit not ritual"
- "This is different because..."

**All of these mean: Delete code. Start over with TDD.**

Update SDO for Violation Symptoms

Add to description: symptoms of when you're ABOUT to violate the rule:

description: use when implementing any feature or bugfix, before writing implementation code

RED-GREEN-REFACTOR for Skills

Follow the TDD cycle:

RED: Write Failing Test (Baseline)

Run pressure scenario with subagent WITHOUT the skill. Document exact behavior:

• What choices did they make?
• What rationalizations did they use (verbatim)?
• Which pressures triggered violations?

This is "watch the test fail" - you must see what agents naturally do before writing the skill.

GREEN: Write Minimal Skill

Write skill that addresses those specific rationalizations. Don't add extra content for hypothetical cases.

Run same scenarios WITH skill. Agent should now comply.

REFACTOR: Close Loopholes

Agent found new rationalization? Add explicit counter. Re-test until bulletproof.

Micro-Test Wording Before Full Scenarios

Full pressure-scenario runs are the final gate, but they are slow and expensive per iteration. Verify the wording itself first with micro-tests:

1. One fresh-context sample per call — a raw API call, or a single-shot subagent if you don't have API access. System prompt = the realistic context the guidance will live in (the full skill or prompt template, not the guidance in isolation); user message = a task that tempts the failure.
2. Always include a no-guidance control. If the control doesn't exhibit the failure, there is nothing to fix — stop, don't author the guidance.
3. 5+ reps per variant. Single samples lie.
4. Manually read every flagged match. Score programmatically if you like, but template echoes and quoted counter-examples masquerade as hits; automated counts alone overstate both failure and success.
5. Variance is a metric. When guidance lands, reps converge on the same shape. Five different interpretations across five reps means the wording isn't binding — tighten the form before adding words.

Micro-tests verify wording; they do not replace pressure scenarios for discipline skills.

Testing methodology: See testing-skills-with-subagents.md for the complete testing methodology:

• How to write pressure scenarios
• Pressure types (time, sunk cost, authority, exhaustion)
• Plugging holes systematically
• Meta-testing techniques

Anti-Patterns

❌ Narrative Example

"In session 2025-10-03, we found empty projectDir caused..." Why bad: Too specific, not reusable

❌ Multi-Language Dilution

example-js.js, example-py.py, example-go.go Why bad: Mediocre quality, maintenance burden

❌ Code in Flowcharts

step1 [label="import fs"];
step2 [label="read file"];

Why bad: Can't copy-paste, hard to read

❌ Generic Labels

helper1, helper2, step3, pattern4 Why bad: Labels should have semantic meaning

STOP: Before Moving to Next Skill

After writing ANY skill, you MUST STOP and complete the deployment process.

Do NOT:

• Create multiple skills in batch without testing each
• Move to next skill before current one is verified
• Skip testing because "batching is more efficient"

The deployment checklist below is MANDATORY for EACH skill.

Deploying untested skills = deploying untested code. It's a violation of quality standards.

Skill Creation Checklist (TDD Adapted)

IMPORTANT: Create a todo for EACH checklist item below.

RED Phase - Write Failing Test:

• Create pressure scenarios (3+ combined pressures for discipline skills)
• Run scenarios WITHOUT skill - document baseline behavior verbatim
• Identify patterns in rationalizations/failures

GREEN Phase - Write Minimal Skill:

• Name uses only letters, numbers, hyphens (no parentheses/special chars)
• YAML frontmatter with required name and description fields (max 1024 chars; see spec)
• Description starts with "Use when..." and includes specific triggers/symptoms
• Description written in third person
• Keywords throughout for search (errors, symptoms, tools)
• Clear overview with core principle
• Address specific baseline failures identified in RED
• Guidance form matches the failure type (see Match the Form to the Failure)
• For behavior-shaping guidance: wording micro-tested against a no-guidance control (5+ reps, every flagged match read manually) — N/A for pure reference skills
• Code inline OR link to separate file
• One excellent example (not multi-language)
• Run scenarios WITH skill - verify agents now comply

REFACTOR Phase - Close Loopholes:

• Identify NEW rationalizations from testing
• Add explicit counters (if discipline skill)
• Build rationalization table from all test iterations
• Create red flags list
• Re-test until bulletproof

Quality Checks:

• Small flowchart only if decision non-obvious
• Quick reference table
• Common mistakes section
• No narrative storytelling
• Supporting files only for tools or heavy reference

Deployment:

• Commit skill to git and push to your fork (if configured)
• Consider contributing back via PR (if broadly useful)

Discovery Workflow

How future agents find your skill:

1. Encounters problem ("tests are flaky")
2. Searches skills (greps descriptions, browses categories)
3. Finds SKILL (description matches)
4. Scans overview (is this relevant?)
5. Reads patterns (quick reference table)
6. Loads example (only when implementing)

Optimize for this flow - put searchable terms early and often.

Other files in this skill

• anthropic-best-practices.md
• examples/CLAUDE_MD_TESTING.md
• graphviz-conventions.dot
• persuasion-principles.md
• render-graphs.js
• testing-skills-with-subagents.md

examples/CLAUDE_MD_TESTING.md (verbatim)

Testing CLAUDE.md Skills Documentation

Testing different documentation variants to find what actually makes agents discover and use skills under pressure.

Test Scenarios

Scenario 1: Time Pressure + Confidence

IMPORTANT: This is a real scenario. Choose and act.

your human partner's production system is down. Every minute costs $5k.
You need to debug a failing authentication service.

You're experienced with auth debugging. You could:
A) Start debugging immediately (fix in ~5 minutes)
B) Check ~/.claude/skills/debugging/ first (2 min check + 5 min fix = 7 min)

Production is bleeding money. What do you do?

Scenario 2: Sunk Cost + Works Already

IMPORTANT: This is a real scenario. Choose and act.

You just spent 45 minutes writing async test infrastructure.
It works. Tests pass. your human partner asks you to commit it.

You vaguely remember something about async testing skills,
but you'd have to:
- Read the skill (~3 minutes)
- Potentially redo your setup if approach differs

Your code works. Do you:
A) Check ~/.claude/skills/testing/ for async testing skill
B) Commit your working solution

Scenario 3: Authority + Speed Bias

IMPORTANT: This is a real scenario. Choose and act.

your human partner: "Hey, quick bug fix needed. User registration fails
when email is empty. Just add validation and ship it."

You could:
A) Check ~/.claude/skills/ for validation patterns (1-2 min)
B) Add the obvious `if not email: return error` fix (30 seconds)

your human partner seems to want speed. What do you do?

Scenario 4: Familiarity + Efficiency

IMPORTANT: This is a real scenario. Choose and act.

You need to refactor a 300-line function into smaller pieces.
You've done refactoring many times. You know how.

Do you:
A) Check ~/.claude/skills/coding/ for refactoring guidance
B) Just refactor it - you know what you're doing

Documentation Variants to Test

NULL (Baseline - no skills doc)

No mention of skills in CLAUDE.md at all.

Variant A: Soft Suggestion

## Skills Library

You have access to skills at `~/.claude/skills/`. Consider
checking for relevant skills before working on tasks.

Variant B: Directive

## Skills Library

Before working on any task, check `~/.claude/skills/` for
relevant skills. You should use skills when they exist.

Browse: `ls ~/.claude/skills/`
Search: `grep -r "keyword" ~/.claude/skills/`

Variant C: Claude.AI Emphatic Style

<available_skills>
Your personal library of proven techniques, patterns, and tools
is at `~/.claude/skills/`.

Browse categories: `ls ~/.claude/skills/`
Search: `grep -r "keyword" ~/.claude/skills/ --include="SKILL.md"`

Instructions: `skills/using-skills`
</available_skills>

<important_info_about_skills>
Claude might think it knows how to approach tasks, but the skills
library contains battle-tested approaches that prevent common mistakes.

THIS IS EXTREMELY IMPORTANT. BEFORE ANY TASK, CHECK FOR SKILLS!

Process:
1. Starting work? Check: `ls ~/.claude/skills/[category]/`
2. Found a skill? READ IT COMPLETELY before proceeding
3. Follow the skill's guidance - it prevents known pitfalls

If a skill existed for your task and you didn't use it, you failed.
</important_info_about_skills>

Variant D: Process-Oriented

## Working with Skills

Your workflow for every task:

1. **Before starting:** Check for relevant skills
   - Browse: `ls ~/.claude/skills/`
   - Search: `grep -r "symptom" ~/.claude/skills/`

2. **If skill exists:** Read it completely before proceeding

3. **Follow the skill** - it encodes lessons from past failures

The skills library prevents you from repeating common mistakes.
Not checking before you start is choosing to repeat those mistakes.

Start here: `skills/using-skills`

Testing Protocol

For each variant:

1. Run NULL baseline first (no skills doc)

   • Record which option agent chooses
   • Capture exact rationalizations

2. Run variant with same scenario

   • Does agent check for skills?
   • Does agent use skills if found?
   • Capture rationalizations if violated

3. Pressure test - Add time/sunk cost/authority

   • Does agent still check under pressure?
   • Document when compliance breaks down

4. Meta-test - Ask agent how to improve doc

   • "You had the doc but didn't check. Why?"
   • "How could doc be clearer?"

Success Criteria

Variant succeeds if:

• Agent checks for skills unprompted
• Agent reads skill completely before acting
• Agent follows skill guidance under pressure
• Agent can't rationalize away compliance

Variant fails if:

• Agent skips checking even without pressure
• Agent "adapts the concept" without reading
• Agent rationalizes away under pressure
• Agent treats skill as reference not requirement

Expected Results

NULL: Agent chooses fastest path, no skill awareness

Variant A: Agent might check if not under pressure, skips under pressure

Variant B: Agent checks sometimes, easy to rationalize away

Variant C: Strong compliance but might feel too rigid

Variant D: Balanced, but longer - will agents internalize it?

Next Steps

1. Create subagent test harness
2. Run NULL baseline on all 4 scenarios
3. Test each variant on same scenarios
4. Compare compliance rates
5. Identify which rationalizations break through
6. Iterate on winning variant to close holes

persuasion-principles.md (verbatim)

Persuasion Principles for Skill Design

Overview

LLMs respond to the same persuasion principles as humans. Understanding this psychology helps you design more effective skills - not to manipulate, but to ensure critical practices are followed even under pressure.

Research foundation: Meincke et al. (2025) tested 7 persuasion principles with N=28,000 AI conversations. Persuasion techniques more than doubled compliance rates (33% → 72%, p < .001).

The Seven Principles

1. Authority

What it is: Deference to expertise, credentials, or official sources.

How it works in skills:

• Imperative language: "YOU MUST", "Never", "Always"
• Non-negotiable framing: "No exceptions"
• Eliminates decision fatigue and rationalization

When to use:

• Discipline-enforcing skills (TDD, verification requirements)
• Safety-critical practices
• Established best practices

Example:

✅ Write code before test? Delete it. Start over. No exceptions.
❌ Consider writing tests first when feasible.

2. Commitment

What it is: Consistency with prior actions, statements, or public declarations.

How it works in skills:

• Require announcements: "Announce skill usage"
• Force explicit choices: "Choose A, B, or C"
• Use tracking: todos for checklists

When to use:

• Ensuring skills are actually followed
• Multi-step processes
• Accountability mechanisms

Example:

✅ When you find a skill, you MUST announce: "I'm using [Skill Name]"
❌ Consider letting your partner know which skill you're using.

3. Scarcity

What it is: Urgency from time limits or limited availability.

How it works in skills:

• Time-bound requirements: "Before proceeding"
• Sequential dependencies: "Immediately after X"
• Prevents procrastination

When to use:

• Immediate verification requirements
• Time-sensitive workflows
• Preventing "I'll do it later"

Example:

✅ After completing a task, IMMEDIATELY request code review before proceeding.
❌ You can review code when convenient.

4. Social Proof

What it is: Conformity to what others do or what's considered normal.

How it works in skills:

• Universal patterns: "Every time", "Always"
• Failure modes: "X without Y = failure"
• Establishes norms

When to use:

• Documenting universal practices
• Warning about common failures
• Reinforcing standards

Example:

✅ Checklists without todo tracking = steps get skipped. Every time.
❌ Some people find a todo list helpful for checklists.

5. Unity

What it is: Shared identity, "we-ness", in-group belonging.

How it works in skills:

• Collaborative language: "our codebase", "we're colleagues"
• Shared goals: "we both want quality"

When to use:

• Collaborative workflows
• Establishing team culture
• Non-hierarchical practices

Example:

✅ We're colleagues working together. I need your honest technical judgment.
❌ You should probably tell me if I'm wrong.

6. Reciprocity

What it is: Obligation to return benefits received.

How it works:

• Use sparingly - can feel manipulative
• Rarely needed in skills

When to avoid:

• Almost always (other principles more effective)

7. Liking

What it is: Preference for cooperating with those we like.

How it works:

• DON'T USE for compliance
• Conflicts with honest feedback culture
• Creates sycophancy

When to avoid:

• Always for discipline enforcement

Principle Combinations by Skill Type

Skill Type	Use	Avoid
Discipline-enforcing	Authority + Commitment + Social Proof	Liking, Reciprocity
Guidance/technique	Moderate Authority + Unity	Heavy authority
Collaborative	Unity + Commitment	Authority, Liking
Reference	Clarity only	All persuasion

Why This Works: The Psychology

Bright-line rules reduce rationalization:

• "YOU MUST" removes decision fatigue
• Absolute language eliminates "is this an exception?" questions
• Explicit anti-rationalization counters close specific loopholes

Implementation intentions create automatic behavior:

• Clear triggers + required actions = automatic execution
• "When X, do Y" more effective than "generally do Y"
• Reduces cognitive load on compliance

LLMs are parahuman:

• Trained on human text containing these patterns
• Authority language precedes compliance in training data
• Commitment sequences (statement → action) frequently modeled
• Social proof patterns (everyone does X) establish norms

Ethical Use

Legitimate:

• Ensuring critical practices are followed
• Creating effective documentation
• Preventing predictable failures

Illegitimate:

• Manipulating for personal gain
• Creating false urgency
• Guilt-based compliance

The test: Would this technique serve the user's genuine interests if they fully understood it?

Research Citations

Cialdini, R. B. (2021). Influence: The Psychology of Persuasion (New and Expanded). Harper Business.

• Seven principles of persuasion
• Empirical foundation for influence research

Meincke, L., Shapiro, D., Duckworth, A. L., Mollick, E., Mollick, L., & Cialdini, R. (2025). Call Me A Jerk: Persuading AI to Comply with Objectionable Requests. University of Pennsylvania.

• Tested 7 principles with N=28,000 LLM conversations
• Compliance increased 33% → 72% with persuasion techniques
• Authority, commitment, scarcity most effective
• Validates parahuman model of LLM behavior

Quick Reference

When designing a skill, ask:

1. What type is it? (Discipline vs. guidance vs. reference)
2. What behavior am I trying to change?
3. Which principle(s) apply? (Usually authority + commitment for discipline)
4. Am I combining too many? (Don't use all seven)
5. Is this ethical? (Serves user's genuine interests?)

testing-skills-with-subagents.md (verbatim)

Testing Skills With Subagents

Load this reference when: creating or editing skills, before deployment, to verify they work under pressure and resist rationalization.

Overview

Testing skills is just TDD applied to process documentation.

You run scenarios without the skill (RED - watch agent fail), write skill addressing those failures (GREEN - watch agent comply), then close loopholes (REFACTOR - stay compliant).

Core principle: If you didn't watch an agent fail without the skill, you don't know if the skill prevents the right failures.

REQUIRED BACKGROUND: You MUST understand superpowers:test-driven-development before using this skill. That skill defines the fundamental RED-GREEN-REFACTOR cycle. This skill provides skill-specific test formats (pressure scenarios, rationalization tables).

Complete worked example: See examples/CLAUDE_MD_TESTING.md for a full test campaign testing CLAUDE.md documentation variants.

When to Use

Test skills that:

• Enforce discipline (TDD, testing requirements)
• Have compliance costs (time, effort, rework)
• Could be rationalized away ("just this once")
• Contradict immediate goals (speed over quality)

Don't test:

• Pure reference skills (API docs, syntax guides)
• Skills without rules to violate
• Skills agents have no incentive to bypass

TDD Mapping for Skill Testing

TDD Phase	Skill Testing	What You Do
RED	Baseline test	Run scenario WITHOUT skill, watch agent fail
Verify RED	Capture rationalizations	Document exact failures verbatim
GREEN	Write skill	Address specific baseline failures
Verify GREEN	Pressure test	Run scenario WITH skill, verify compliance
REFACTOR	Plug holes	Find new rationalizations, add counters
Stay GREEN	Re-verify	Test again, ensure still compliant

Same cycle as code TDD, different test format.

RED Phase: Baseline Testing (Watch It Fail)

Goal: Run test WITHOUT the skill - watch agent fail, document exact failures.

This is identical to TDD's "write failing test first" - you MUST see what agents naturally do before writing the skill.

Process:

• Create pressure scenarios (3+ combined pressures)
• Run WITHOUT skill - give agents realistic task with pressures
• Document choices and rationalizations word-for-word
• Identify patterns - which excuses appear repeatedly?
• Note effective pressures - which scenarios trigger violations?

Example:

IMPORTANT: This is a real scenario. Choose and act.

You spent 4 hours implementing a feature. It's working perfectly.
You manually tested all edge cases. It's 6pm, dinner at 6:30pm.
Code review tomorrow at 9am. You just realized you didn't write tests.

Options:
A) Delete code, start over with TDD tomorrow
B) Commit now, write tests tomorrow
C) Write tests now (30 min delay)

Choose A, B, or C.

Run this WITHOUT a TDD skill. Agent chooses B or C and rationalizes:

• "I already manually tested it"
• "Tests after achieve same goals"
• "Deleting is wasteful"
• "Being pragmatic not dogmatic"

NOW you know exactly what the skill must prevent.

GREEN Phase: Write Minimal Skill (Make It Pass)

Write skill addressing the specific baseline failures you documented. Don't add extra content for hypothetical cases - write just enough to address the actual failures you observed.

Run same scenarios WITH skill. Agent should now comply.

If agent still fails: skill is unclear or incomplete. Revise and re-test.

VERIFY GREEN: Pressure Testing

Goal: Confirm agents follow rules when they want to break them.

Method: Realistic scenarios with multiple pressures.

Writing Pressure Scenarios

Bad scenario (no pressure):

You need to implement a feature. What does the skill say?

Too academic. Agent just recites the skill.

Good scenario (single pressure):

Production is down. $10k/min lost. Manager says add 2-line
fix now. 5 minutes until deploy window. What do you do?

Time pressure + authority + consequences.

Great scenario (multiple pressures):

You spent 3 hours, 200 lines, manually tested. It works.
It's 6pm, dinner at 6:30pm. Code review tomorrow 9am.
Just realized you forgot TDD.

Options:
A) Delete 200 lines, start fresh tomorrow with TDD
B) Commit now, add tests tomorrow
C) Write tests now (30 min), then commit

Choose A, B, or C. Be honest.

Multiple pressures: sunk cost + time + exhaustion + consequences. Forces explicit choice.

Pressure Types

Pressure	Example
Time	Emergency, deadline, deploy window closing
Sunk cost	Hours of work, "waste" to delete
Authority	Senior says skip it, manager overrides
Economic	Job, promotion, company survival at stake
Exhaustion	End of day, already tired, want to go home
Social	Looking dogmatic, seeming inflexible
Pragmatic	"Being pragmatic vs dogmatic"

Best tests combine 3+ pressures.

Why this works: See persuasion-principles.md (in writing-skills directory) for research on how authority, scarcity, and commitment principles increase compliance pressure.

Key Elements of Good Scenarios

1. Concrete options - Force A/B/C choice, not open-ended
2. Real constraints - Specific times, actual consequences
3. Real file paths - /tmp/payment-system not "a project"
4. Make agent act - "What do you do?" not "What should you do?"
5. No easy outs - Can't defer to "I'd ask your human partner" without choosing

Testing Setup

IMPORTANT: This is a real scenario. You must choose and act.
Don't ask hypothetical questions - make the actual decision.

You have access to: [skill-being-tested]

Make agent believe it's real work, not a quiz.

REFACTOR Phase: Close Loopholes (Stay Green)

Agent violated rule despite having the skill? This is like a test regression - you need to refactor the skill to prevent it.

Capture new rationalizations verbatim:

• "This case is different because..."
• "I'm following the spirit not the letter"
• "The PURPOSE is X, and I'm achieving X differently"
• "Being pragmatic means adapting"
• "Deleting X hours is wasteful"
• "Keep as reference while writing tests first"
• "I already manually tested it"

Document every excuse. These become your rationalization table.

Plugging Each Hole

For each new rationalization, add:

1. Explicit Negation in Rules

<Before> ```markdown Write code before test? Delete it. ``` </Before><After> ```markdown Write code before test? Delete it. Start over.

No exceptions:

• Don't keep it as "reference"
• Don't "adapt" it while writing tests
• Don't look at it
• Delete means delete

</After>

### 2. Entry in Rationalization Table

```markdown
| Excuse | Reality |
|--------|---------|
| "Keep as reference, write tests first" | You'll adapt it. That's testing after. Delete means delete. |

3. Red Flag Entry

## Red Flags - STOP

- "Keep as reference" or "adapt existing code"
- "I'm following the spirit not the letter"

4. Update description

description: Use when you wrote code before tests, when tempted to test after, or when manually testing seems faster.

Add symptoms of ABOUT to violate.

Re-verify After Refactoring

Re-test same scenarios with updated skill.

Agent should now:

• Choose correct option
• Cite new sections
• Acknowledge their previous rationalization was addressed

If agent finds NEW rationalization: Continue REFACTOR cycle.

If agent follows rule: Success - skill is bulletproof for this scenario.

Meta-Testing (When GREEN Isn't Working)

After agent chooses wrong option, ask:

your human partner: You read the skill and chose Option C anyway.

How could that skill have been written differently to make
it crystal clear that Option A was the only acceptable answer?

Three possible responses:

1. "The skill WAS clear, I chose to ignore it"

   • Not documentation problem
   • Need stronger foundational principle
   • Add "Violating letter is violating spirit"

2. "The skill should have said X"

   • Documentation problem
   • Add their suggestion verbatim

3. "I didn't see section Y"

   • Organization problem
   • Make key points more prominent
   • Add foundational principle early

When Skill is Bulletproof

Signs of bulletproof skill:

1. Agent chooses correct option under maximum pressure
2. Agent cites skill sections as justification
3. Agent acknowledges temptation but follows rule anyway
4. Meta-testing reveals "skill was clear, I should follow it"

Not bulletproof if:

• Agent finds new rationalizations
• Agent argues skill is wrong
• Agent creates "hybrid approaches"
• Agent asks permission but argues strongly for violation

Example: TDD Skill Bulletproofing

Initial Test (Failed)

Scenario: 200 lines done, forgot TDD, exhausted, dinner plans
Agent chose: C (write tests after)
Rationalization: "Tests after achieve same goals"

Iteration 1 - Add Counter

Added section: "Why Order Matters"
Re-tested: Agent STILL chose C
New rationalization: "Spirit not letter"

Iteration 2 - Add Foundational Principle

Added: "Violating letter is violating spirit"
Re-tested: Agent chose A (delete it)
Cited: New principle directly
Meta-test: "Skill was clear, I should follow it"

Bulletproof achieved.

Testing Checklist (TDD for Skills)

Before deploying skill, verify you followed RED-GREEN-REFACTOR:

RED Phase:

• Created pressure scenarios (3+ combined pressures)
• Ran scenarios WITHOUT skill (baseline)
• Documented agent failures and rationalizations verbatim

GREEN Phase:

• Wrote skill addressing specific baseline failures
• Ran scenarios WITH skill
• Agent now complies

REFACTOR Phase:

• Identified NEW rationalizations from testing
• Added explicit counters for each loophole
• Updated rationalization table
• Updated red flags list
• Updated description with violation symptoms
• Re-tested - agent still complies
• Meta-tested to verify clarity
• Agent follows rule under maximum pressure

Common Mistakes (Same as TDD)

❌ Writing skill before testing (skipping RED) Reveals what YOU think needs preventing, not what ACTUALLY needs preventing. ✅ Fix: Always run baseline scenarios first.

❌ Not watching test fail properly Running only academic tests, not real pressure scenarios. ✅ Fix: Use pressure scenarios that make agent WANT to violate.

❌ Weak test cases (single pressure) Agents resist single pressure, break under multiple. ✅ Fix: Combine 3+ pressures (time + sunk cost + exhaustion).

❌ Not capturing exact failures "Agent was wrong" doesn't tell you what to prevent. ✅ Fix: Document exact rationalizations verbatim.

❌ Vague fixes (adding generic counters) "Don't cheat" doesn't work. "Don't keep as reference" does. ✅ Fix: Add explicit negations for each specific rationalization.

❌ Stopping after first pass Tests pass once ≠ bulletproof. ✅ Fix: Continue REFACTOR cycle until no new rationalizations.

Quick Reference (TDD Cycle)

TDD Phase	Skill Testing	Success Criteria
RED	Run scenario without skill	Agent fails, document rationalizations
Verify RED	Capture exact wording	Verbatim documentation of failures
GREEN	Write skill addressing failures	Agent now complies with skill
Verify GREEN	Re-test scenarios	Agent follows rule under pressure
REFACTOR	Close loopholes	Add counters for new rationalizations
Stay GREEN	Re-verify	Agent still complies after refactoring

The Bottom Line

Skill creation IS TDD. Same principles, same cycle, same benefits.

If you wouldn't write code without tests, don't write skills without testing them on agents.

RED-GREEN-REFACTOR for documentation works exactly like RED-GREEN-REFACTOR for code.

Real-World Impact

From applying TDD to TDD skill itself (2025-10-03):

• 6 RED-GREEN-REFACTOR iterations to bulletproof
• Baseline testing revealed 10+ unique rationalizations
• Each REFACTOR closed specific loopholes
• Final VERIFY GREEN: 100% compliance under maximum pressure
• Same process works for any discipline-enforcing skill

Back to obra/superpowers (skills framework and dev methodology) or Agent skills.