跳到正文
FunCoding

搜索

搜索文档、Skill 和 MCP

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.

AI 与智能体103kskills/using-agent-skills/SKILL.md

安装

把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性,你确认后才安装。

读取 https://funcoding.ai/skills/addyosmani/agent-skills/using-agent-skills/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

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-refine → spec-driven-development → planning-and-task-breakdown → incremental-implementation → test-driven-development → code-review-and-quality → code-simplification → shipping-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-recovery → test-driven-development → code-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

相似的 Skill

brand-guidelines
anthropics/skills180k

brand-guidelines

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

AI 与智能体

internal-comms
anthropics/skills180k

internal-comms

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

AI 与智能体

template-skill
anthropics/skills180k

template-skill

Replace with description of the skill and when Claude should use it.

AI 与智能体

mcp-builder
anthropics/skills180k

mcp-builder

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

AI 与智能体

algorithmic-art
anthropics/skills180k

algorithmic-art

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

AI 与智能体

academy-guide
anthropics/skills180k

academy-guide

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

AI 与智能体