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Writing Skills

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LayneChai
writing-skills

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Overview

PublisherLayneChai
Repositorysuperpowers-dsh
Skill namewriting-skills
Stars
130
Forks
10
Bundled files
9
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LayneChai on GitHub. Read the source before you install it.

Installation

Install the Writing Skills AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LayneChai/superpowers-dsh/tree/main/skills/writing-skills
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LayneChai/superpowers-dsh.git /tmp/superpowers-dsh
mkdir -p .claude/skills
cp -r /tmp/superpowers-dsh/skills/writing-skills .claude/skills/writing-skills
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Writing Skills in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Writing Skills on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Writing Skills is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Writing Skills

Overview

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

Personal skills live in your runtime's skills directory: on the DeepSeek Harness, that is $DSH_HOME/skills/ (user level, discovered automatically by skill-filesystem), the project .dsh/skills/ / .agents/skills/ roots, or — for packaged skills — the skills/ directory inside a plugin bundle like this one. See dsh-tools.md for the full mechanism.

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 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 references/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 ConceptSkill Creation
Test casePressure scenario with subagent
Production codeSkill document (SKILL.md)
Test fails (RED)Agent violates rule without skill (baseline)
Test passes (GREEN)Agent complies with skill present
RefactorClose loopholes while maintaining compliance
Write test firstRun baseline scenario BEFORE writing skill
Watch it failDocument exact rationalizations agent uses
Minimal codeWrite skill addressing those specific violations
Watch it passVerify agent now complies
Refactor cycleFind 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

Writing SKILL.md

A skill's description says only when to use it: triggering conditions and symptoms, starting with "Use when...". It never summarizes the skill's workflow, because an agent that reads a workflow summary follows it instead of reading the skill.

When you write or change a skill's frontmatter, name or description, or need to cut its size, read references/discovery.md: the SKILL.md template, description and keyword guidance, naming, token budgets, and cross-referencing other skills.

Flowcharts

When you add or change a flowchart in a skill, read references/flowcharts.md first: when a flowchart earns its place, the graphviz conventions, and how to render one.

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

Invoke bundled scripts through their interpreter in the prose (bash scripts/tool.sh, node scripts/tool.js), never by bare path: some harness plugin packagers strip executable bits, and a bare scripts/tool.sh fails there with Permission denied.

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 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

ExcuseReality
"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 failureRight formWrong form
Skips/violates a rule under pressure (knows better, does it anyway)Prohibition + rationalization table + red flags (see references/bulletproofing.md)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 orderProhibition list ("don't restate", "never narrate")
Omits a required element from something they already produceStructural: REQUIRED field or slot in the template they fill inProse reminders near the template
Behavior should depend on a conditionConditional 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 Against Rationalization

When the baseline failure is a discipline failure, read references/bulletproofing.md before you write the guidance: closing loopholes, the spirit-vs-letter line, the rationalization table, and the red-flags list.

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 references/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

dot
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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Writing Skills AI skill do?

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Why use Writing Skills on TypingMind?

Because you install it once and use it with any model. Writing Skills is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Writing Skills in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LayneChai/superpowers-dsh/tree/main/skills/writing-skills. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Writing Skills?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Writing Skills?

As many as you like. As long as a model supports skills, you can use Writing Skills with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Writing Skills AI skill free?

It is published on GitHub by LayneChai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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