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skill-creator技能创建器

Agent Skill

skill-creator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,137

周安装

46

GitHub Stars

2

下载量

357
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-creator(技能创建器)
来源仓库:https://github.com/wyattowalsh/agents
仓库路径:skills/skill-creator
安装命令:
npx skills add https://github.com/wyattowalsh/agents --skill skill-creator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/wyattowalsh/agents --skill skill-creator

简介

用于根据关键词或任务场景快速定位候选结果。

  • 适合在需要检索相关信息时使用。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装命令:npx skills add https://github.com/wyattowalsh/agents --skill skill-creator。
  • 安装前建议确认是否会触发联网或文件操作。

SKILL.md

Skill Creator

Create, improve, and audit AI agent skills. Every skill follows 14 proven structural patterns.

Scope: Skills only. NOT for creating agents (wagents new agent), building MCP servers (/mcp-creator), or running existing skills. This repo uses raw SKILL.md format committed directly to skills/.

Dispatch

$ARGUMENTSActionExample
create <name> / new <name>Develop (new)/skill-creator create my-analyzer
create <name> --from <source>Develop (new, from exemplar)/skill-creator create my-analyzer --from wargame
improve <name> / improve <path>Develop (existing)/skill-creator improve add-badges
plan <name> / plan <path>Plan (existing)/skill-creator plan honest-review
plan --all / plan repoPlan (repo-wide)/skill-creator plan --all
audit <name>Audit/skill-creator audit honest-review
audit --allAudit All/skill-creator audit --all
dashboardDashboard/skill-creator dashboard
package <name> / package --allPackage/skill-creator package wargame
Natural language skill ideaAuto: Develop (new)"tool that audits Python type safety"
Skill name + modification verbAuto: Develop (existing)"refactor the wargame skill"
Path to SKILL.mdAuto: Develop (existing)skills/wargame/SKILL.md
"MCP server" / "agent" / "run"Refuse + redirect
EmptyGallery/skill-creator

Auto-Detection Heuristic

If no explicit mode keyword is provided:

  1. Path ending in SKILL.md or directory under skills/Develop (existing)
  2. Existing skill name + modification verb (improve, refactor, enhance, update, fix, rewrite, optimize, polish, revise, change) → Develop (existing)
  3. --from <source> in arguments → Develop (new, from exemplar)
  4. New capability description ("I want to build...", "tool that...", "skill for...") → Develop (new) — derive name, confirm before scaffolding
  5. "MCP server", "agent", "run" → refuse gracefully and redirect
  6. Ambiguous → ask the user which mode they want

Quick Start

wagents new skill <name>           # Scaffold from template
wagents validate                   # Check all skills
wagents eval validate              # Check eval manifests after eval changes
uv run python skills/skill-creator/scripts/audit.py skills/<name>/  # Score quality
wagents package <name> --dry-run   # Check single-skill portability before packaging

Skill Development

Unified process for creating new skills and improving existing ones. Load references/workflow.md for the full procedure.

StepNew SkillExisting Skill
1. UnderstandDefine use cases, scope, patternsAudit + understand user's intent
2. PlanStructure, description, frontmatterGap analysis + improvement plan (approval gate)
3. Scaffoldwagents new skill <name>Skip
4. BuildWrite/edit body, references, scripts, templates, evalsSame
5. Validatewagents validate + wagents eval validate + audit.pySame
6. IterateTest, identify issues, loop to Step 4Same

Repo-Wide / Multi-Skill Planning

Use plan <name> for an existing-skill refinement plan without editing and plan --all or plan repo for a ranked repo-wide planning pass.

Required planning output:

  1. baseline audit summary
  2. highest-value findings
  3. explicit file targets
  4. expected score impact
  5. approval gate before any edits

For repo-wide planning, produce a ranked queue plus one standalone refinement plan per promoted skill or skill cluster. Do not edit any skill until the user approves the plan.

Load references/refinement-plan.md when producing the standalone refinement-plan packet.

Audit

Score a skill using deterministic analysis + AI review. Load references/audit-guide.md.

Audit All

Comparative ranking of all repository skills. Load references/audit-guide.md § Audit All.

Dashboard

Render visual creation process monitor or audit quality dashboard. Load references/audit-guide.md § Dashboard.

Auto-detects mode from data: phases field → process monitor; skills array → audit overview.

Gallery (Empty Arguments)

Present skill inventory with scores and available actions. Run uv run python skills/skill-creator/scripts/audit.py --all --format table, display results, offer mode menu.

Package

Package skills into portable ZIP files for Claude Code Desktop import. Load references/packaging-guide.md for ZIP structure, manifest schema, portability checks, and cross-agent compatibility.

wagents package <name> --dry-run  # Check a single skill before emitting a ZIP
wagents package <name>            # Single skill → <name>-v<version>.skill.zip
wagents package --all             # All skills → dist/ with manifest.json
wagents package --all --dry-run   # Check portability without creating ZIPs

Hooks

PreToolUse hooks intercept tool calls during skill execution. The hooks: frontmatter field scopes hooks to this skill only — they activate when the skill is loaded and deactivate when it completes.

Post-edit enforcement for this skill:

  • SKILL.md edits trigger uv run wagents validate
  • evals/*.json edits trigger uv run wagents eval validate
  • hook-bearing skill/settings edits trigger uv run wagents hooks validate
  • failures surface to the agent instead of being swallowed

Stop hook enforcement:

  • runs uv run python skills/skill-creator/scripts/verify.py stop
  • validates dirty skill-definition, eval, and hook surfaces before exit
  • exits immediately when hook input has stop_hook_active: true to avoid recursive Stop-hook loops

State Management

Creation progress persists at ~/.{gemini|copilot|codex|claude}/skill-progress/<name>.json. Read/write via scripts/progress.py. Survives session restarts. Use --state-dir to override the default location.

Reference File Index

FileContentRead When
references/workflow.mdUnified 6-step skill development process for new and existing skillsDevelop (new), Develop (existing)
references/refinement-plan.mdStandalone refinement-plan contract for existing-skill and repo-wide planning outputPlan (existing), Plan (repo-wide)
references/audit-guide.mdAudit procedure, Audit All, Dashboard rendering, Gallery, grade thresholdsAudit, Audit All, Dashboard, Gallery
references/proven-patterns.md14 structural patterns with examples from repo skillsStep 4 (Build), gap analysis
references/best-practices.mdAnthropic guide + superpowers methodology + cross-agent awarenessStep 2 (Plan), Step 4 (Build), description writing
references/frontmatter-spec.mdFull field catalog, invocation matrix, decision treeStep 3 (Scaffold), frontmatter configuration
references/packaging-guide.mdZIP structure, manifest schema, portability checks, import instructionsPackage
references/evaluation-rubric.md13 weighted scoring dimensions normalized to 100, grade thresholds, pressure testingAudit (pressure testing), scoring targets

Read reference files as indicated by the "Read When" column above. Do not rely on memory or prior knowledge of their contents.

Core Principles

Conciseness is respect — The context window is shared. Every line competes with the agent's working memory. Earn every line or delete it.

Progressive disclosure — Frontmatter for discovery (~100 tokens), body for dispatch (<5K tokens), references for deep knowledge (on demand), scripts/templates for execution (never loaded).

Self-exemplar — This skill follows every pattern it teaches. When in doubt, look at how skill-creator applies it.

Critical Rules

  1. Run uv run wagents validate before declaring any skill complete
  2. Run uv run wagents eval validate after changing evals and before declaring the skill complete
  3. Run uv run python skills/skill-creator/scripts/audit.py after every significant SKILL.md change
  4. Never create a skill without a dispatch table — it is the routing contract
  5. Never create a dispatch table without an empty-args handler — unrouted input is a bug
  6. Every reference file must appear in the Reference File Index — orphan refs are invisible
  7. Every indexed reference must exist on disk — phantom refs cause agent errors
  8. Body must stay under 500 lines (below frontmatter) — move detail to references
  9. Description must include "Use when" trigger phrases AND "NOT for" exclusions
  10. Names must be kebab-case, 2-64 chars, no consecutive hyphens, no reserved words
  11. Scripts use argparse + JSON to stdout — no custom output formats
  12. Templates are self-contained HTML with no external dependencies
  13. Do NOT call wagents docs generate — delegate to docs-steward
  14. Do NOT create agents or MCP servers — refuse gracefully and redirect
  15. Improving existing skills requires presenting an improvement plan and getting user approval before implementing changes
  16. Audit mode is read-only — never modify the skill being audited
  17. Update evals when dispatch behavior or modes change — stale evals are invisible bugs
  18. plan <name> and plan --all are read-only planning modes — never edit during planning
  19. Repo-wide or multi-skill requests require a ranked plan and standalone refinement-plan output before any implementation begins
  20. Stop hooks must include a stop_hook_active guard — recursive hook loops are implementation bugs

Canonical terms (use these exactly throughout):

  • Modes: "Develop (new)", "Develop (existing)", "Audit", "Audit All", "Dashboard", "Package", "Gallery"
  • Steps (Development): "Understand", "Plan", "Scaffold", "Build", "Validate", "Iterate"
  • Grade scale: "A" (90-100), "B" (75-89), "C" (60-74), "D" (40-59), "F" (<40)
  • Patterns: "dispatch-table", "reference-file-index", "critical-rules", "canonical-vocabulary", "scope-boundaries", "classification-gating", "scaling-strategy", "state-management", "scripts", "templates", "hooks", "progressive-disclosure", "body-substitutions", "stop-hooks"
  • Audit dimensions: "frontmatter", "description", "dispatch-table", "body-structure", "pattern-coverage", "reference-quality", "critical-rules", "script-quality", "portability", "conciseness", "canonical-vocabulary", "evaluation-coverage", "validation-contract"

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.55%
按下载量换算134

Claude

27.16%
按下载量换算97

Cursor

18.19%
按下载量换算65

Gemini CLI

8.82%
按下载量换算31

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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