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Using Agent Skills

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addyosmani
using-agent-skills

Discovers and invokes agent skills. Use when starting a session, or when you need to decide which skill or workflow applies to the piece of work at hand. This is the meta-skill that governs how all other skills are discovered and invoked.

Overview

Publisheraddyosmani
Repositoryagent-skills
Skill nameusing-agent-skills
Stars
95.8K
Forks
10.1K
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Using Agent 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.
  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/addyosmani/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/using-agent-skills .claude/skills/using-agent-skills
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Agent 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 Using Agent 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 Using Agent 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.

Using Agent Skills

Overview

Agent Skills is a collection of engineering workflow skills organized by development phase. Each skill encodes a specific process that senior engineers follow. This meta-skill helps you discover and apply the right skill for your current task.

Skill Discovery

When a task arrives, identify the development phase and apply the corresponding skill:

Task arrives
    ├── Don't know what you want yet? ──────→ interview-me
    ├── Have a rough concept, need variants? → idea-refine
    ├── New project/feature/change? ──→ spec-driven-development
    ├── No quality bar written down? ──→ constraint-driven-development
    ├── Have a spec, need tasks? ──────→ planning-and-task-breakdown
    ├── Implementing code? ────────────→ incremental-implementation
    │   ├── UI work? ─────────────────→ frontend-ui-engineering
    │   ├── API work? ────────────────→ api-and-interface-design
    │   ├── Need better context? ─────→ context-engineering
    │   ├── Need doc-verified code? ───→ source-driven-development
    │   └── Stakes high / unfamiliar code? ──→ doubt-driven-development
    ├── Writing/running tests? ────────→ test-driven-development
    │   └── Browser-based? ───────────→ browser-testing-with-devtools
    ├── Something broke? ──────────────→ debugging-and-error-recovery
    ├── Reviewing code? ───────────────→ code-review-and-quality
    │   ├── Too complex? ─────────────→ code-simplification
    │   ├── Security concerns? ───────→ security-and-hardening
    │   └── Performance concerns? ────→ performance-optimization
    ├── Committing/branching? ─────────→ git-workflow-and-versioning
    ├── CI/CD pipeline work? ──────────→ ci-cd-and-automation
    ├── Deprecating/migrating? ────────→ deprecation-and-migration
    ├── Writing docs/ADRs? ───────────→ documentation-and-adrs
    ├── Adding logs/metrics/alerts? ───→ observability-and-instrumentation
    └── Deploying/launching? ─────────→ shipping-and-launch

Core Operating Behaviors

These behaviors apply at all times, across all skills. They are non-negotiable.

1. Surface Assumptions

Before implementing anything non-trivial, explicitly state your assumptions:

ASSUMPTIONS I'M MAKING:
1. [assumption about requirements]
2. [assumption about architecture]
3. [assumption about scope]
→ Correct me now or I'll proceed with these.

Don't silently fill in ambiguous requirements. The most common failure mode is making wrong assumptions and running with them unchecked. Surface uncertainty early — it's cheaper than rework.

2. Manage Confusion Actively

When you encounter inconsistencies, conflicting requirements, or unclear specifications:

  1. STOP. Do not proceed with a guess.
  2. Name the specific confusion.
  3. Present the tradeoff or ask the clarifying question.
  4. Wait for resolution before continuing.

Bad: Silently picking one interpretation and hoping it's right. Good: "I see X in the spec but Y in the existing code. Which takes precedence?"

3. Push Back When Warranted

You are not a yes-machine. When an approach has clear problems:

  • Point out the issue directly
  • Explain the concrete downside (quantify when possible — "this adds ~200ms latency" not "this might be slower")
  • Propose an alternative
  • Accept the human's decision if they override with full information

Sycophancy is a failure mode. "Of course!" followed by implementing a bad idea helps no one. Honest technical disagreement is more valuable than false agreement.

4. Enforce Simplicity

Your natural tendency is to overcomplicate. Actively resist it.

Before finishing any implementation, ask:

  • Can this be done in fewer lines?
  • Are these abstractions earning their complexity?
  • Would a staff engineer look at this and say "why didn't you just..."?

If you build 1000 lines and 100 would suffice, you have failed. Prefer the boring, obvious solution. Cleverness is expensive.

5. Maintain Scope Discipline

Touch only what you're asked to touch.

Do NOT:

  • Remove comments you don't understand
  • "Clean up" code orthogonal to the task
  • Refactor adjacent systems as a side effect
  • Delete code that seems unused without explicit approval
  • Add features not in the spec because they "seem useful"

Your job is surgical precision, not unsolicited renovation.

6. Verify, Don't Assume

Every skill includes a verification step. A task is not complete until verification passes. "Seems right" is never sufficient — there must be evidence (passing tests, build output, runtime data).

Per-skill verification is the local check. The project-wide bar that applies to every change, regardless of which skill is active, is the Definition of Done: tests pass, no regressions, behavior verified at runtime, docs updated. See ../../references/definition-of-done.md. It complements each task's acceptance criteria rather than replacing them.

Failure Modes to Avoid

These are the subtle errors that look like productivity but create problems:

  1. Making wrong assumptions without checking
  2. Not managing your own confusion — plowing ahead when lost
  3. Not surfacing inconsistencies you notice
  4. Not presenting tradeoffs on non-obvious decisions
  5. Being sycophantic ("Of course!") to approaches with clear problems
  6. Overcomplicating code and APIs
  7. Modifying code or comments orthogonal to the task
  8. Removing things you don't fully understand
  9. Building without a spec because "it's obvious"
  10. Skipping verification because "it looks right"

Skill Rules

  1. Check for an applicable skill before starting work. Skills encode processes that prevent common mistakes.

  2. Skills are workflows, not suggestions. Follow the steps in order. Don't skip verification steps.

  3. Multiple skills can apply. A feature implementation might involve idea-refinespec-driven-developmentplanning-and-task-breakdownincremental-implementationtest-driven-developmentcode-review-and-qualitycode-simplificationshipping-and-launch in sequence.

  4. When in doubt, start with a spec. If the task is non-trivial and there's no spec, begin with spec-driven-development.

Lifecycle Sequence

For a complete feature, the typical skill sequence is:

1.  interview-me                → Extract what the user actually wants
2.  idea-refine                 → Refine vague ideas
3.  spec-driven-development     → Define what we're building
4.  planning-and-task-breakdown → Break into verifiable chunks
5.  context-engineering         → Load the right context
6.  source-driven-development   → Verify against official docs
7.  incremental-implementation  → Build slice by slice
8.  observability-and-instrumentation → Instrument as you build (runs parallel with 7-9, not after)
9.  doubt-driven-development    → Cross-examine non-trivial decisions in-flight
10. test-driven-development     → Prove each slice works
11. code-review-and-quality     → Review before merge
12. code-simplification         → Reduce unnecessary complexity while preserving behavior
13. git-workflow-and-versioning → Clean commit history
14. documentation-and-adrs      → Document decisions
15. deprecation-and-migration   → Retire old systems and move users safely when needed
16. shipping-and-launch         → Deploy safely

Not every task needs every skill. A bug fix might only need: debugging-and-error-recoverytest-driven-developmentcode-review-and-quality.

Quick Reference

PhaseSkillOne-Line Summary
Defineinterview-meSurface what the user actually wants before any plan, spec, or code exists
Defineidea-refineRefine ideas through structured divergent and convergent thinking
Definespec-driven-developmentRequirements and acceptance criteria before code
Planplanning-and-task-breakdownDecompose into small, verifiable tasks
Buildincremental-implementationThin vertical slices, test each before expanding
Buildsource-driven-developmentVerify against official docs before implementing
Builddoubt-driven-developmentAdversarial fresh-context review of every non-trivial decision
Buildcontext-engineeringRight context at the right time
Buildfrontend-ui-engineeringProduction-quality UI with accessibility
Buildapi-and-interface-designStable interfaces with clear contracts
Verifytest-driven-developmentFailing test first, then make it pass
Verifybrowser-testing-with-devtoolsChrome DevTools MCP for runtime verification
Verifydebugging-and-error-recoveryReproduce → localize → fix → guard
Reviewcode-review-and-qualityFive-axis review with quality gates
Reviewcode-simplificationPreserve behavior while reducing unnecessary complexity
Reviewsecurity-and-hardeningOWASP prevention, input validation, least privilege
Reviewperformance-optimizationMeasure first, optimize only what matters
Shipgit-workflow-and-versioningAtomic commits, clean history
Shipci-cd-and-automationAutomated quality gates on every change
Shipdeprecation-and-migrationRemove old systems and migrate users safely
Shipdocumentation-and-adrsDocument the why, not just the what
Shipobservability-and-instrumentationStructured logs, RED metrics, traces, symptom-based alerts
Shipshipping-and-launchPre-launch checklist, monitoring, rollback plan

Frequently asked questions

What does the Using Agent Skills AI skill do?

Discovers and invokes agent skills. Use when starting a session, or when you need to decide which skill or workflow applies to the piece of work at hand. This is the meta-skill that governs how all other skills are discovered and invoked.

Why use Using Agent Skills on TypingMind?

Because you install it once and use it with any model. Using Agent 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 Using Agent Skills in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/addyosmani/agent-skills/tree/main/skills/using-agent-skills. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Using Agent 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 Using Agent Skills?

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

Is the Using Agent Skills AI skill free?

Yes. It is published on GitHub by addyosmani under the MIT license. 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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