Token导航 LogoToken导航

Agent Skill · 研究检索

skillforge(skillforge 分析)

skillforge 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。

skillforge 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 65,351。

总安装 282周安装 12GitHub Stars 583下载 99核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:skillforge(skillforge 分析)
来源仓库:https://github.com/tripleyak/skillforge
仓库路径:skills/skillforge
安装命令:
npx skills add https://github.com/tripleyak/skillforge --skill skillforge
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/tripleyak/skillforge --skill skillforge

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

skillforge 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。适用于技能库探索、工具推荐或知识图谱构建等研究检索场景。通过关键词匹配和来源仓库索引,提供候选技能或资源的快速检索能力。

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。可结合来源仓库、安装命令和原始 README 继续核验具体用法和功能边界。

来源仓库内路径:skills/skillforge

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/tripleyak/skillforge --skill skillforge;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skillforge 怎么安装?

在终端执行 npx skills add https://github.com/tripleyak/skillforge --skill skillforge。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skillforge 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

skillforge 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

skillforge 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

skillforge 用的人多吗?

公开数据是累计安装 282 次,近一周安装 12 次,下载 99 次,GitHub 583 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

skillforge 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
skillforge

skillforge 分析 · 本页

282583需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

SkillForge 4.1 - Intelligent Skill Router & Creator

Analyzes ANY input to find, improve, or create the right skill.


Quick Start

Any input works. SkillForge will intelligently route to the right action:

# These all work - SkillForge figures out what you need:

SkillForge: create a skill for automated code review
→ Creates new skill (after checking no duplicates exist)

help me debug this TypeError
→ Recommends ErrorExplainer skill (existing)

improve the testgen skill to handle React components better
→ Enters improvement mode for TestGen

do I have a skill for database migrations?
→ Recommends DBSchema, database-migration skills

TypeError: Cannot read property 'map' of undefined
→ Routes to debugging skills (error detected)

Triggers

Creation Triggers

  • SkillForge: {goal} - Full autonomous skill creation
  • create skill - Natural language activation
  • design skill for {purpose} - Purpose-first creation
  • ultimate skill - Emphasize maximum quality
  • skillforge --plan-only - Generate specification without execution

Routing Triggers (NEW in v4.0)

  • {any input} - Analyzes and routes automatically
  • do I have a skill for - Searches existing skills
  • which skill / what skill - Recommends matching skills
  • improve {skill-name} skill - Enters improvement mode
  • help me with / I need to - Detects task and routes
InputOutputQuality Gate
Any inputTriage → Route → ActionPhase 0 analysis
Explicit createNew skillUnanimous panel approval
Task/questionSkill recommendationMatch confidence ≥60%

Process Overview

ANY USER INPUT
(prompt, error, code, URL, question, task request)
    │
    ▼
┌─────────────────────────────────────────────────────┐
│ Phase 0: SKILL TRIAGE (NEW)                         │
│ • Classify input type (create/improve/question/task)│
│ • Scan 250+ skills in ecosystem                     │
│ • Match against existing skills with confidence %   │
│ • Route to: USE | IMPROVE | CREATE | COMPOSE        │
├─────────────────────────────────────────────────────┤
│         ↓ USE_EXISTING    ↓ IMPROVE      ↓ CREATE   │
│      [Recommend]      [Load & Enhance] [Continue]   │
└─────────────────────────────────────────────────────┘
    │ (if CREATE_NEW or IMPROVE_EXISTING)
    ▼
┌─────────────────────────────────────────────────────┐
│ Phase 1: DEEP ANALYSIS                              │
│ • Expand requirements (explicit, implicit, unknown) │
│ • Apply 11 thinking models + Automation Lens        │
│ • Question until no new insights (3 empty rounds)   │
│ • Identify automation/script opportunities          │
├─────────────────────────────────────────────────────┤
│ Phase 2: SPECIFICATION                              │
│ • Generate XML spec with all decisions + WHY        │
│ • Include scripts section (if applicable)           │
│ • Validate timelessness score ≥ 7                   │
├─────────────────────────────────────────────────────┤
│ Phase 3: GENERATION                                 │
│ • Write SKILL.md with fresh context                 │
│ • Generate references/, assets/, and scripts/       │
├─────────────────────────────────────────────────────┤
│ Phase 4: SYNTHESIS PANEL                            │
│ • 3-4 Opus agents review independently              │
│ • Script Agent added when scripts present           │
│ • All agents must approve (unanimous)               │
│ • If rejected → loop back with feedback             │
└─────────────────────────────────────────────────────┘
    │
    ▼
Production-Ready Agentic Skill

Key principles:

  • Phase 0 prevents duplicates - Always checks existing skills first
  • Evolution/timelessness is the core lens (score ≥ 7 required)
  • Every decision includes WHY
  • Zero tolerance for errors
  • Autonomous execution at maximum depth
  • Scripts enable self-verification and agentic operation

Tool Escalation Policy

Start with least privilege (Read, Glob, Grep, Write, Edit).

Only add higher-risk tools when explicitly required:

  • Bash for deterministic local scripts that cannot be replaced with file edits
  • WebFetch / WebSearch only when external facts are required
  • Task only for true parallel sub-agent orchestration

Commands

CommandAction
SkillForge: {goal}Full autonomous execution
SkillForge --plan-only {goal}Generate specification only
SkillForge --quick {goal}Reduced depth (not recommended)
SkillForge --triage {input}Run Phase 0 triage only
SkillForge --improve {skill}Enter improvement mode for existing skill

Phase 0: Skill Triage (NEW in v4.0)

Before creating anything, SkillForge intelligently analyzes your input to determine the best action.

How It Works

┌────────────────────────────────────────────────────────────────────┐
│                        ANY USER INPUT                               │
│  (prompt, error, code, URL, question, task request, anything)      │
└────────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌────────────────────────────────────────────────────────────────────┐
│  Step 1: INPUT CLASSIFICATION                                       │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐       │
│  │ explicit_create │ │ explicit_improve│ │ skill_question  │       │
│  │ "create skill"  │ │ "improve skill" │ │ "do I have..."  │       │
│  └─────────────────┘ └─────────────────┘ └─────────────────┘       │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐       │
│  │  task_request   │ │  error_message  │ │  code_snippet   │       │
│  │ "help me with"  │ │ "TypeError..."  │ │ [pasted code]   │       │
│  └─────────────────┘ └─────────────────┘ └─────────────────┘       │
└────────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌────────────────────────────────────────────────────────────────────┐
│  Step 2: SKILL ECOSYSTEM SCAN                                       │
│  • Load index of 250+ skills (discover_skills.py)                  │
│  • Match input against all skills with confidence scoring          │
│  • Identify top matches with reasons                               │
└────────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌────────────────────────────────────────────────────────────────────┐
│  Step 3: DECISION MATRIX                                            │
│                                                                     │
│  Match ≥80%  + explicit create → CLARIFY (duplicate warning)       │
│  Match ≥80%  + other input     → USE_EXISTING (recommend skill)    │
│  Match 50-79%                  → IMPROVE_EXISTING (enhance match)  │
│  Match <50%  + explicit create → CREATE_NEW (proceed to Phase 1)   │
│  Multi-domain detected         → COMPOSE (suggest skill chain)     │
│  Ambiguous input               → CLARIFY (ask for more info)       │
└────────────────────────────────────────────────────────────────────┘

Decision Actions

ActionWhenResult
USE_EXISTINGMatch ≥80%Recommends existing skill(s) to invoke
IMPROVE_EXISTINGMatch 50-79%Loads skill and enters enhancement mode
CREATE_NEWMatch <50%Proceeds to Phase 1 (Deep Analysis)
COMPOSEMulti-domainSuggests skill chain via SkillComposer
CLARIFYAmbiguous or duplicateAsks user to clarify intent

Triage Script

# Run triage on any input
python scripts/triage_skill_request.py "help me debug this error"

# JSON output for automation
python scripts/triage_skill_request.py "create a skill for payments" --json

# Examples:
python scripts/triage_skill_request.py "TypeError: Cannot read property 'map'"
# → USE_EXISTING: Recommends ErrorExplainer (92%)

python scripts/triage_skill_request.py "create a skill for code review"
# → CLARIFY: CodeReview skill exists (85%), create anyway?

python scripts/triage_skill_request.py "help me with API and auth and testing"
# → COMPOSE: Multi-domain, suggests APIDesign + AuthSystem + TestGen chain

Ecosystem Index

Phase 0 uses a pre-built index of all skills:

# Rebuild skill index (run periodically or after installing new skills)
python scripts/discover_skills.py

# Index location: ~/.cache/skillrecommender/skill_index.json
# Scans: ~/.claude/skills/, plugins/marketplaces/*, plugins/cache/*

Integration with Phases 1-4

  • USE_EXISTING: Exits early, no creation needed
  • IMPROVE_EXISTING: Loads existing skill → Phase 1 analyzes gaps → Phase 2-4 enhance
  • CREATE_NEW: Full pipeline (Phase 1 → 2 → 3 → 4)
  • COMPOSE: Suggests using SkillComposer instead
  • CLARIFY: Pauses for user input before proceeding

Validation & Packaging

Before distribution, validate your skill:

# Quick validation (required for packaging)
python scripts/quick_validate.py ~/.claude/skills/my-skill/

# Full structural validation
python scripts/validate-skill.py ~/.claude/skills/my-skill/

# Package for distribution
python scripts/package_skill.py ~/.claude/skills/my-skill/ ./dist

Frontmatter Requirements

Skills must use only these allowed frontmatter properties:

PropertyRequiredDescription
nameYesHyphen-case, max 64 chars
descriptionYesMax 1024 chars, no angle brackets
licenseNoMIT, Apache-2.0, etc.
allowed-toolsNoRestrict tool access (comma-separated or YAML list)
modelNoSpecific Claude model (e.g., claude-sonnet-4-20250514)
contextNoSet to fork for isolated sub-agent context
agentNoAgent type when context: fork (Explore, Plan, general-purpose)
hooksNoLifecycle hooks (PreToolUse, PostToolUse, Stop)
user-invocableNoShow in slash menu (default: true)
metadataNoCustom fields (version, author, domains, etc.)

Basic Example:

---
name: my-skill
description: What this skill does and when to use it
license: MIT
model: claude-opus-4-5-20251101
user-invocable: true
metadata:
  version: 1.0.0
  author: your-name
---

Advanced Example (with forked context and hooks):

---
name: isolated-analyzer
description: Runs analysis in isolated context with validation hooks
license: MIT
model: claude-opus-4-5-20251101
context: fork
agent: Explore
user-invocable: true
allowed-tools:
  - Read
  - Glob
  - Grep
hooks:
  PreToolUse:
    - matcher: "Bash"
      hooks:
        - type: command
          command: "./scripts/validate.sh"
metadata:
  version: 1.0.0
---

Field Details:

FieldValuesNotes
contextforkCreates isolated sub-agent with separate conversation history
agentExplore, Plan, general-purposeOnly valid when context: fork
user-invocabletrue, falsefalse hides from slash menu but Claude can still auto-invoke
hooksObjectSee Hooks Integration section

Skill Output Structure

~/.claude/skills/{skill-name}/
├── SKILL.md                    # Main entry point (required)
├── references/                 # Deep documentation (optional)
│   ├── patterns.md
│   └── examples.md
├── assets/                     # Templates (optional)
│   └── templates/
└── scripts/                    # Automation scripts (optional)
    ├── validate.py             # Validation/verification
    ├── generate.py             # Artifact generation
    └── state.py                # State management

Scripts Directory

Scripts enable skills to be agentic - capable of autonomous operation with self-verification.

CategoryPurposeWhen to Include
ValidationVerify outputs meet standardsSkill produces artifacts
GenerationCreate artifacts from templatesRepeatable artifact creation
State ManagementTrack progress across sessionsLong-running operations
TransformationConvert/process dataData processing tasks
CalculationCompute metrics/scoresScoring or analysis

Script Requirements:

  • Python 3.x with standard library only (graceful fallbacks for extras)
  • Result dataclass pattern for structured returns
  • Exit codes: 0=success, 1=failure, 10=validation failure, 11=verification failure
  • Self-verification where applicable
  • Documented in SKILL.md with usage examples

See: references/script-integration-framework.md

Hooks Integration

Skills can define lifecycle hooks for validation, logging, and safety:

---
name: secure-skill
hooks:
  PreToolUse:
    - matcher: "Bash"
      hooks:
        - type: command
          command: "./scripts/validate-input.sh"
  PostToolUse:
    - matcher: "Write"
      hooks:
        - type: command
          command: "./scripts/log-output.sh"
          once: true
  Stop:
    - hooks:
        - type: command
          command: "./scripts/cleanup.sh"
---

Hook Types:

HookWhen TriggeredUse Case
PreToolUseBefore tool executionInput validation, security checks
PostToolUseAfter tool executionOutput logging, verification
StopWhen skill completesCleanup, state persistence

Hook Configuration:

FieldDescription
matcherTool name pattern to match (e.g., "Bash", "Write", "Bash(python:*)")
typeHook type: command (shell) or prompt (Claude evaluation)
commandShell command to execute (for type: command)
onceIf true, run only once per session (default: false)

Read $TOOL_INPUT / $TOOL_OUTPUT inside hook scripts from environment variables. Do not interpolate untrusted tool payloads directly into shell command strings.

When to Use Hooks:

ScenarioHook TypeExample
Validate script inputsPreToolUseCheck parameters before python scripts/*.py
Log generated artifactsPostToolUseRecord files created by Write tool
Security gatePreToolUseBlock dangerous bash commands
Cleanup temp filesStopRemove intermediate artifacts

Example: Script Validation Hook

For skills with scripts, add input validation:

hooks:
  PreToolUse:
    - matcher: "Bash(python:scripts/*)"
      hooks:
        - type: command
          command: "python scripts/quick_validate.py . 2>/dev/null || true"
          once: true

Anti-Patterns

AvoidWhyInstead
Duplicate skillsBloats registryCheck existing first
Single triggerHard to discover3-5 varied phrases
No verificationCan't confirm successMeasurable outcomes
Over-engineeringComplexity without valueStart simple
Missing WHYCan't evolveDocument rationale
Invalid frontmatterCan't packageUse allowed properties only

Verification Checklist

After creation:

  • Frontmatter valid (only allowed properties)
  • Name is hyphen-case, ≤64 chars
  • Description ≤1024 chars, no < or >
  • 3-5 trigger phrases defined
  • Timelessness score ≥ 7
  • python scripts/quick_validate.py passes
  • python scripts/check_docs_safety.py passes

1A: Input Expansion

Transform user's goal into comprehensive requirements:

USER INPUT: "Create a skill for X"
                │
                ▼
┌─────────────────────────────────────────────────────────┐
│ EXPLICIT REQUIREMENTS                                    │
│ • What the user literally asked for                      │
│ • Direct functionality stated                            │
├─────────────────────────────────────────────────────────┤
│ IMPLICIT REQUIREMENTS                                    │
│ • What they probably expect but didn't say               │
│ • Standard quality expectations                          │
│ • Integration with existing patterns                     │
├─────────────────────────────────────────────────────────┤
│ UNKNOWN UNKNOWNS                                         │
│ • What they don't know they need                         │
│ • Expert-level considerations they'd miss                │
│ • Future needs they haven't anticipated                  │
├─────────────────────────────────────────────────────────┤
│ DOMAIN CONTEXT                                           │
│ • Related skills that exist                              │
│ • Patterns from similar skills                           │
│ • Lessons from skill failures                            │
└─────────────────────────────────────────────────────────┘

Check for overlap with existing skills:

ls ~/.claude/skills/
# Grep for similar triggers in existing SKILL.md files
Match ScoreAction
>7/10Use existing skill instead
5-7/10Clarify distinction before proceeding
<5/10Proceed with new skill

1B: Multi-Lens Analysis

Apply all 11 thinking models systematically:

LensCore QuestionApplication
First PrinciplesWhat's fundamentally needed?Strip convention, find core
InversionWhat guarantees failure?Build anti-patterns
Second-OrderWhat happens after the obvious?Map downstream effects
Pre-MortemWhy did this fail?Proactive risk mitigation
Systems ThinkingHow do parts interact?Integration mapping
Devil's AdvocateStrongest counter-argument?Challenge every decision
ConstraintsWhat's truly fixed?Separate real from assumed
ParetoWhich 20% delivers 80%?Focus on high-value features
Root CauseWhy is this needed? (5 Whys)Address cause not symptom
ComparativeHow do options compare?Weighted decision matrix
Opportunity CostWhat are we giving up?Explicit trade-offs

Minimum requirement: All 11 lenses scanned, at least 5 applied in depth.

See: references/multi-lens-framework.md

1C: Regression Questioning

Iterative self-questioning until no new insights emerge:

ROUND N:
│
├── "What am I missing?"
├── "What would an expert in {domain} add?"
├── "What would make this fail?"
├── "What will this look like in 2 years?"
├── "What's the weakest part of this design?"
└── "Which thinking model haven't I applied?"
    │
    └── New insights > 0?
        │
        ├── YES → Incorporate and loop
        └── NO → Check termination criteria

Termination Criteria:

  • Three consecutive rounds produce no new insights
  • All 11 thinking models have been applied
  • At least 3 simulated expert perspectives considered
  • Evolution/timelessness explicitly evaluated
  • Automation opportunities identified

See: references/regression-questions.md

1D: Automation Analysis

Identify opportunities for scripts that enable agentic operation:

FOR EACH operation in the skill:
│
├── Is this operation repeatable?
│   └── YES → Consider generation script
│
├── Does this produce verifiable output?
│   └── YES → Consider validation script
│
├── Does this need state across sessions?
│   └── YES → Consider state management script
│
├── Does this involve external tools?
│   └── YES → Consider integration script
│
└── Can Claude verify success autonomously?
    └── NO → Add self-verification script

Automation Lens Questions:

QuestionScript Category if YES
What operations will be repeated identically?Generation
What outputs require validation?Validation
What state needs to persist?State Management
Can the skill run overnight autonomously?All categories
How will Claude verify correct execution?Verification

Decision: Script vs No Script

Create Script WhenSkip Script When
Operation is deterministicRequires human judgment
Output can be validatedOne-time setup
Will be reused across invocationsSimple text output
Enables autonomous operationNo verification needed
External tool integrationPure Claude reasoning

See: references/script-integration-framework.md

Specification Structure

The specification captures all analysis insights in XML format:

<skill_specification>
  <metadata>
    <name>skill-name</name>
    <analysis_iterations>N</analysis_iterations>
    <timelessness_score>X/10</timelessness_score>
  </metadata>

  <context>
    <problem_statement>What + Why + Who</problem_statement>
    <existing_landscape>Related skills, distinctiveness</existing_landscape>
  </context>

  <requirements>
    <explicit>What user asked for</explicit>
    <implicit>Expected but unstated</implicit>
    <discovered>Found through analysis</discovered>
  </requirements>

  <architecture>
    <pattern>Selected pattern with WHY</pattern>
    <phases>Ordered phases with verification</phases>
    <decision_points>Branches and defaults</decision_points>
  </architecture>

  <scripts>
    <decision_summary>needs_scripts + rationale</decision_summary>
    <script_inventory>name, category, purpose, patterns</script_inventory>
    <agentic_capabilities>autonomous, self-verify, recovery</agentic_capabilities>
  </scripts>

  <evolution_analysis>
    <timelessness_score>X/10</timelessness_score>
    <extension_points>Where skill can grow</extension_points>
    <obsolescence_triggers>What would break it</obsolescence_triggers>
  </evolution_analysis>

  <anti_patterns>
    <pattern>What to avoid + WHY + alternative</pattern>
  </anti_patterns>

  <success_criteria>
    <criterion>Measurable + verification method</criterion>
  </success_criteria>
</skill_specification>

See: references/specification-template.md

Specification Validation

Before proceeding to Phase 3:

  • All sections present with no placeholders
  • Every decision includes WHY
  • Timelessness score ≥ 7 with justification
  • At least 2 extension points documented
  • All requirements traceable to source
  • Scripts section complete (if applicable)
  • Agentic capabilities documented (if scripts present)

Context: Fresh, clean (no analysis artifacts polluting) Standard: Zero errors—every section verified before proceeding

Generation Order

1. Create directory structure
   mkdir -p ~/.claude/skills/{skill-name}/references
   mkdir -p ~/.claude/skills/{skill-name}/assets/templates
   mkdir -p ~/.claude/skills/{skill-name}/scripts  # if scripts needed

2. Write SKILL.md
   • Frontmatter (YAML - allowed properties only)
   • Title and brief intro
   • Quick Start section
   • Triggers (3-5 varied phrases)
   • Quick Reference table
   • How It Works overview
   • Commands
   • Scripts section (if applicable)
   • Validation section
   • Anti-Patterns
   • Verification criteria
   • Deep Dive sections (in <details> tags)

3. Generate reference documents (if needed)
   • Deep documentation for complex topics
   • Templates for generated artifacts
   • Checklists for validation

4. Create assets (if needed)
   • Templates for skill outputs

5. Create scripts (if needed)
   • Use script-template.py as base
   • Include Result dataclass pattern
   • Add self-verification
   • Document exit codes
   • Test before finalizing

Quality Checks During Generation

CheckRequirement
FrontmatterOnly allowed properties (name, description, license, allowed-tools, metadata)
NameHyphen-case, ≤64 chars
Description≤1024 chars, no angle brackets
Triggers3-5 distinct, natural language
Phases1-3 max, not over-engineered
VerificationConcrete, measurable
Tables over proseStructured information in tables
No placeholder textEvery section fully written
Scripts (if present)Shebang, docstring, argparse, exit codes, Result pattern
Script docsScripts section in SKILL.md with usage examples

Panel: 3-4 Opus agents with distinct evaluative lenses Requirement: Unanimous approval (all agents) Fallback: Return to Phase 1 with feedback (max 5 iterations)

Panel Composition

AgentFocusKey CriteriaWhen Active
Design/ArchitectureStructure, patterns, correctnessPattern appropriate, phases logical, no circular depsAlways
Audience/UsabilityClarity, discoverability, completenessTriggers natural, steps unambiguous, no assumed knowledgeAlways
Evolution/TimelessnessFuture-proofing, extension, ecosystemScore ≥7, extension points clear, ecosystem fitAlways
Script/AutomationAgentic capability, verification, qualityScripts follow patterns, self-verify, documentedWhen scripts present

Script Agent (Conditional)

The Script Agent is activated when the skill includes a scripts/ directory. Focus areas:

CriterionChecks
Pattern ComplianceResult dataclass, argparse, exit codes
Self-VerificationScripts can verify their own output
Error HandlingGraceful failures, actionable messages
DocumentationUsage examples in SKILL.md
Agentic CapabilityCan run autonomously without human intervention

Script Agent Scoring:

ScoreMeaning
8-10Fully agentic, self-verifying, production-ready
6-7Functional but missing some agentic capabilities
<6Requires revision - insufficient automation quality

Agent Evaluation

Each agent produces:

## [Agent] Review

### Verdict: APPROVED / CHANGES_REQUIRED

### Scores
| Criterion | Score (1-10) | Notes |
|-----------|--------------|-------|

### Strengths
1. [Specific with evidence]

### Issues (if CHANGES_REQUIRED)
| Issue | Severity | Required Change |
|-------|----------|-----------------|

### Recommendations
1. [Even if approved]

Consensus Protocol

IF all agents APPROVED (3/3 or 4/4):
    → Finalize skill
    → Run validate-skill.py
    → Update registry
    → Complete

ELSE:
    → Collect all issues (including script issues)
    → Return to Phase 1 with issues as input
    → Re-apply targeted questioning
    → Regenerate skill and scripts
    → Re-submit to panel

IF 5 iterations without consensus:
    → Flag for human review
    → Present all agent perspectives
    → User makes final decision

See: references/synthesis-protocol.md

Every skill is evaluated through the evolution lens:

Temporal Projection

TimeframeKey Question
6 monthsHow will usage patterns evolve?
1 yearWhat ecosystem changes are likely?
2 yearsWhat new capabilities might obsolete this?
5 yearsIs the core problem still relevant?

Timelessness Scoring

ScoreDescriptionVerdict
1-3Transient, will be obsolete in monthsReject
4-6Moderate, depends on current toolingRevise
7-8Solid, principle-based, extensibleApprove
9-10Timeless, addresses fundamental problemExemplary

Requirement: All skills must score ≥7.

Anti-Obsolescence Patterns

DoDon't
Design around principlesHardcode implementations
Document the WHYOnly document the WHAT
Include extension pointsCreate closed systems
Abstract volatile dependenciesDirect coupling
Version-agnostic patternsPin specific versions

See: references/evolution-scoring.md

Select based on task complexity:

PatternUse WhenStructure
Single-PhaseSimple linear tasksSteps 1-2-3
ChecklistQuality/compliance audits☐ Item verification
GeneratorCreating artifactsInput → Transform → Output
Multi-PhaseComplex ordered workflowsPhase 1 → Phase 2 → Phase 3
Multi-Agent ParallelIndependent subtasksLaunch agents concurrently
Multi-Agent SequentialDependent subtasksAgent 1 → Agent 2 → Agent 3
OrchestratorCoordinating multiple skillsMeta-skill chains

Selection Decision Tree

Is it a simple procedure?
├── Yes → Single-Phase
└── No → Does it produce artifacts?
    ├── Yes → Generator
    └── No → Does it verify/audit?
        ├── Yes → Checklist
        └── No → Are subtasks independent?
            ├── Yes → Multi-Agent Parallel
            └── No → Multi-Agent Sequential or Multi-Phase
SKILLCREATOR_CONFIG:
  mode: autonomous
  depth: maximum  # always
  core_lens: evolution_timelessness

  analysis:
    min_lens_depth: 5
    max_questioning_rounds: 7
    termination_empty_rounds: 3

  synthesis:
    panel_size: 3
    require_unanimous: true
    max_iterations: 5
    escalate_to_human: true

  evolution:
    min_timelessness_score: 7
    min_extension_points: 2
    require_temporal_projection: true

  model:
    primary: claude-opus-4-5-20251101
    subagents: claude-opus-4-5-20251101

References


Related Skills

SkillRelationship
skill-composerCan orchestrate created skills
claude-authoring-guideDeeper patterns reference
codereviewPattern for multi-agent panels
maker-frameworkZero error standard source

Extension Points

  1. Additional Lenses: Add new thinking models to references/multi-lens-framework.md
  2. New Synthesis Agents: Extend panel beyond 4 agents for specific domains
  3. Custom Patterns: Add architecture patterns to selection guide
  4. Domain Templates: Add domain-specific templates to assets/templates/
  5. Script Patterns: Add new patterns to references/script-patterns-catalog.md
  6. Script Categories: Extend the 7 script categories for new use cases

Changelog

v4.1.0 (Current)

  • Extended frontmatter support - Full support for model, context, agent, hooks, user-invocable
  • Created scripts/_constants.py for shared validation constants
  • Updated scripts/quick_validate.py with extended property validation
  • Updated scripts/validate-skill.py with hooks and agent validation
  • Fixed scripts/discover_skills.py to extract version from metadata.version
  • Added Hooks Integration section to SKILL.md and script-integration-framework.md
  • Added Forked Context documentation to synthesis-protocol.md
  • Updated skill template with modern best practices
  • Expanded frontmatter requirements table with 10 properties

v4.0.0

  • Phase 0 Skill Triage - Intelligent routing before creation
  • Universal input handling - any prompt works
  • Skill ecosystem scanning with 250+ skills indexed
  • Decision matrix: USE | IMPROVE | CREATE | COMPOSE
  • Renamed from SkillCreator to SkillForge

v3.2.0

  • Added Script Integration Framework for agentic skills
  • Added 4th Script Agent to synthesis panel (conditional)
  • Added Phase 1D: Automation Analysis
  • Added Automation Lens questions to regression questioning
  • Created references/script-integration-framework.md
  • Created references/script-patterns-catalog.md
  • Created assets/templates/script-template.py
  • Updated skill-spec-template.xml with <scripts> section
  • Updated validate-skill.py with script validation
  • Skills can now include self-verifying Python scripts

v3.1.0

  • Added progressive disclosure structure
  • Fixed frontmatter for packaging compatibility
  • Added validation & packaging section
  • Deep dive sections now collapsible

v3.0.0

  • Complete redesign as ultimate meta-skill
  • Added regression questioning loop
  • Added multi-lens analysis framework (11 models)
  • Added evolution/timelessness core lens
  • Added multi-agent synthesis panel

v2.0.0

  • Pattern selection guide
  • Quality standards checklist

v1.0.0

  • Basic skill structure

平台分布

Claude

33.9%
按下载量换算34

Codex

33.53%
按下载量换算33

Cursor

17.24%
按下载量换算17

Gemini CLI

10.58%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills