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skill-expert-skills-openclaw技能 expert skills OpenClaw

Agent Skill

skill-expert-skills-openclaw 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

9,517

周安装

385

GitHub Stars

公开资料未说明

下载量

2,988
OpenClaw

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

MIT-0

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-expert-skills-openclaw(技能 expert skills OpenClaw)
来源仓库:https://github.com/tinkcarlos/skill-expert-skills-openclaw
安装命令:
openclaw skills install skill-expert-skills-openclaw
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install skill-expert-skills-openclaw

简介

提供 OpenClaw 技能开发的六阶段强制工作流程。

  • 包含任务分类、质量门控和验证打包全流程。
  • 确保技能可移植性和执行一致性标准。skill-expert-skills-openclaw 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 每个阶段均有检查点防止跳过关键环节。
  • 适用于团队协作下的规模化技能生产环境。

SKILL.md

name
skill-expert-skills
description
|
license
Apache-2.0
compatibility
Python 3.8+ for validation scripts
allowed-tools
Read Write Bash Grep Glob
metadata
version
4.0.0
last_updated
2026-03-06
enhancement

Skill Expert v4.0 — Universal Edition

Transform "create/optimize a Skill" requests into triggerable, reusable, maintainable, verifiable Skill packages with quality gates.

Principles: Expertise First | User Confirmation First | Conciseness | Universality

Pre-Flight Check

#CheckpointStatus
1Read this SKILL.md?[ ]
2Identified task type? (Create / Optimize / Validate / Package)[ ]
3Ready to classify complexity? (Simple / Standard / Complex)[ ]

Fast Track Decision

After identifying task type, classify complexity to choose the execution path:

Task Classification
    |
    +-- Simple Skill (minimal template, < 100 lines, well-known domain)
    |   -> FAST TRACK: Phase 0 -> Phase 3 -> Phase 4
    |
    +-- Standard Skill (with references, 100-500 lines)
    |   -> STANDARD: Phase 0 -> Phase 1 -> Phase 2 -> Phase 3 -> Phase 4 -> Phase 5
    |
    +-- Complex Skill (knowledge-intensive, domain expertise needed)
    |   -> FULL: All phases with deep research
    |
    +-- Validate/Package Only
        -> Jump to Phase 4 / Command Reference

Phase 0: Discovery + Hypothesis

Goal: Understand the real need, check for existing skills.

0.1 Task Classification

TypeAction
Create NewContinue to 0.2
Optimize ExistingContinue to 0.2
Validate OnlySkip to Command Reference
Package OnlySkip to Command Reference

0.2 Skill Discovery (Reuse First)

-> references/skill-discovery-protocol.md

Search local skills first, then trusted external sources.

0.3 Hypothesis Generation + 5 Whys

-> references/hypothesis-ladder-for-skills.md

Generate 3-5 hypotheses about what the user really wants:

Hypothesis TypeExample Question
ScopeFull solution or single function?
AudienceNovice or expert user?
TriggerWhat scenarios activate this skill?
OutputCode, document, decision, or report?
DepthQuick utility or comprehensive workflow?

Validate with user. Use 5 Whys to uncover the deep need behind the surface request.

GATE: Hypothesis Validation

ConditionOn Failure
At least 1 hypothesis confirmed by userContinue questioning

Phase 1: Requirement Mining

Goal: Get to the REAL problem, validate it, confirm with user.

1.1 Three-Stage Elicitation

-> references/requirement-elicitation-protocol.md

Stage 1: Explicit (5W1H)  ->  Stage 2: Implicit (4 methods)  ->  Stage 3: Validation

1.2 Skill Type Classification

-> references/skill-type-taxonomy.md

Quick question to determine type (~80% accuracy):

1) Comprehensive "summary"    2) Key-only "insight/diagnosis"
3) Produce "new content"      4) Reach a "conclusion"

1.3 Non-Technical Methodology (if applicable)

-> references/non-technical-methodology-research.md

For judgment-heavy domains: find experts, golden examples, anti-patterns.

1.4 User Confirmation

-> references/user-confirmation-protocol.md

Present requirements summary → get explicit user confirmation.

GATE: Requirement Gate

ConditionOn Failure
User explicitly confirms requirementsRedo mining

Phase 2: Knowledge Acquisition

Goal: Become an expert BEFORE writing.

2.1 Research Workflow

-> references/knowledge-acquisition-guide.md

LLM baseline -> Extract domains -> Research with tools -> Cross-validate -> Gate -> Self-check

Use whatever tools are available in your environment:

  • Documentation lookup tools (official docs first)
  • Web search tools (for latest practices, at least 3 sources)
  • Code search tools (for real-world examples)
  • URL fetch tools (for specific references)

If no external tools available, rely on own knowledge but mark it as "unverified".

2.2 Source Credibility Tiers

TierSource TypeTrust Level
SOfficial docs, official blogHighest — use directly
AOfficial GitHub, official examplesHigh — use directly
BKnown tech blogs, high-vote StackOverflowMedium — cross-validate
CPersonal blogs, forumsLow — must multi-source verify
DUnknown source, AI-generatedLowest — must verify against official

2.3 Deep Research (Complex skills only)

-> references/deep-research-methodology.md

Five-layer knowledge pyramid: Basics -> Principles -> Practice -> Expert -> Frontier.

GATE: Knowledge Gate (Composite)

All 4 sub-checks must pass as a single gate:

Sub-CheckPass Condition
FreshnessSource date < 1 year, grade A/B
AccuracyOfficial source + 2 independent confirmations
CompletenessCore features 100%, scenarios 80%+
FusionLLM vs fresh knowledge compared, conflicts resolved

-> references/knowledge-validation-checklist.md for details


Phase 3: Skill Writing

Goal: Write the skill following enterprise patterns.

3.1 SKILL.md Positioning (NON-NEGOTIABLE)

SKILL.md SHOULD be:
  ✅ Scannable in 30 seconds (table of contents)
  ✅ Decision tree: "what situation → which action/file"
  ✅ Command reference: one-line key commands
  ✅ Minimal necessary constraints/contracts

SKILL.md should NOT be:
  ❌ Detailed knowledge base or tutorials
  ❌ Complete protocol explanations
  ❌ Long examples or code blocks
  ❌ Background knowledge

→ All detailed content MUST go to references/

3.2 Conciseness Checklist

  • [ ] New content > 20 lines? → Move to references/
  • [ ] Does AI need this every invocation? → If not, move to references/
  • [ ] Can it be a one-line pointer? → Use → references/xxx.md
  • [ ] Body < 500 lines? → Hard limit 800 lines
  • [ ] Contains tech-stack specific content? → Abstract or move to references/

3.3 Template Selection

-> references/skill-templates.md

TemplateWhenComplexityFiles
MinimalQuick utility, personal preferenceLow1
Read-onlyAnalysis, audit, review (no file changes)Low1-2
Script-drivenAutomation, repeatable tasksMedium3+
Knowledge-intensiveExpert domain, multi-phase workflowHigh5+

3.4 Frontmatter Specification

---
name: my-skill              # Required. hyphen-case, ≤64 chars, matches directory name
description: |               # Required. ≤1024 chars, third person, no < >
  What this skill does.
  Use when:
  - scenario 1
  - scenario 2
  Not for: X, Y.
license: MIT                 # Optional
compatibility: Python 3.8+   # Optional. ≤500 chars
allowed-tools: Read Write    # Optional. space-delimited tool names
metadata:                    # Optional. extension fields
  version: 1.0.0
---

3.5 Directory Structure

my-skill/
├── SKILL.md              # Required: instructions + metadata
├── scripts/              # Optional: executable code
│   ├── main.py
│   └── requirements.txt
├── references/           # Optional: detailed docs (loaded into context)
│   ├── patterns.md
│   └── checklist.md
└── assets/               # Optional: templates, images (NOT loaded into context)
    └── template.md

3.6 Writing Standards

-> references/writing-style-guide.md -> references/universality-guide.md

GATE: Writing Gate

ConditionOn Failure
Pre-invocation check passedFix parameters, retry
Post-invocation check passedLog warning, retry

Phase 4: Quality Validation + User Confirmation

Goal: Ensure output meets quality standards and user needs.

4.1 Structural Validation Checklist

CheckCriteria
FrontmatterHas name + description, valid YAML
Namehyphen-case, ≤64 chars, matches directory
DescriptionThird person, 3-5 triggers, has "Use when" + "Not for"
Body length< 500 lines (warn at 500, error at 800)
No angle bracketsDescription has no < or >
References usedDetailed content in references/, not SKILL.md body
Output ContractDefined what the skill produces
Decision TreeAI knows "what situation → which action"

4.2 Portability Checklist

CheckCriteria
No hardcoded pathsNo absolute paths or project-specific directories
No hardcoded tool namesUses generic tool categories, not specific MCP servers
No project-specific contextWorks without knowledge of a specific codebase
Synthetic examplesExamples are self-contained, not from a real project
Platform-agnosticWorks in any AI coding assistant environment

4.3 User Final Confirmation

-> references/user-confirmation-protocol.md

Present: validation results + deliverables + features summary. Get explicit confirmation.

GATE: Delivery Gate

ConditionOn Failure
Validation checks passFix and re-validate
User explicitly confirmsFix and re-confirm

Phase 5: Self-Reflection + Knowledge Precipitation

Goal: Learn from the experience.

5.1 Self-Reflection Report

## Self-Reflection

| Dimension | Score (1-5) | Evidence |
|-----------|-------------|----------|
| Requirement Understanding | [1-5] | [notes] |
| Knowledge Completeness | [1-5] | [notes] |
| Output Quality | [1-5] | [notes] |
| User Satisfaction | [1-5] | [notes] |
| **Total** | **[/20]** | |

| Problem | Cause | Prevention |
|---------|-------|------------|
| [issue] | [why] | [measure] |

5.2 Knowledge Precipitation

  • [ ] Document lessons learned
  • [ ] Update references if new patterns discovered
  • [ ] Note what worked well for future skills

GATE: Reflection Complete

ConditionOn Failure
Score + analysis documentedComplete before closing

Decision Tree

【Create New Skill】
  Phase 0: Classify task → Generate hypotheses → [Fast Track?] → User confirms
  Phase 1: 5 Whys → Skill Type → Validate requirements → User confirms
  Phase 2: Research domain → 4-Layer knowledge gate
  Phase 3: Select template → Write SKILL.md → Conciseness check
  Phase 4: Structural validation → Portability check → User confirms
  Phase 5: Self-reflect → Precipitate knowledge

【Optimize Existing Skill】
  Phase 0: Classify → Hypothesize what to improve → [Fast Track?] → User confirms
  Phase 1: 5 Whys on current pain points → User confirms
  Phase 2: Research latest patterns → 4-Layer gate
  Phase 3: Modify SKILL.md → Conciseness check
  Phase 4: Validate → User confirms
  Phase 5: Self-reflect → Document changes

【Validate / Package Only】
  -> Phase 4: Run validation scripts → Report results

Command Reference

Run from project root:

# Search installed skills (reuse-first)
python scripts/search_skills.py "<keyword>" --root <skills-directory>

# Initialize new skill
python scripts/init_skill.py <skill-name> --path <skills-directory>

# Validate (required before delivery)
python scripts/quick_validate.py <skill-directory>
python scripts/universal_validate.py <skill-directory>

# Package for distribution (optional)
python scripts/package_skill.py <skill-directory> ./dist

# Maintenance
python scripts/upgrade_skill.py <skill-directory>
python scripts/diff_with_official.py <skill-directory>
python scripts/analyze_trigger.py <skill-directory>

Key Constraints

ItemConstraint
namehyphen-case, ≤64 chars, must match directory name
descriptionNo < >, ≤1024 chars, third person, 3-5 triggers
licenseOptional, license name or reference to bundled file
compatibilityOptional, ≤500 chars, environment requirements
allowed-toolsOptional, space-delimited tool names
SKILL.md body< 500 lines recommended, hard limit 800
UniversalityNo project paths, no hardcoded tool names, portable examples

Output Contract

Required: Updated SKILL.md + change summary (triggers, domains, validation results)

On-demand: references/ | scripts/ | assets/


Gate System Summary

GatePhasePass ConditionOn Failure
Hypothesis Validation0≥1 hypothesis confirmed by userKeep asking
User Confirmation1User explicitly confirms requirementsRedo mining
Knowledge Freshness2Source < 1 year oldRe-acquire
Knowledge Accuracy2Official + 2 independent sourcesCross-validate
Knowledge Completeness2Core 100%, scenarios 80%+Supplement
Knowledge Fusion2Own vs new knowledge comparedMust compare
Writing Gate3Pre/post invocation checks passFix and retry
Delivery Gate4Scripts pass + user confirmsFix and redo
Reflection Complete5Score + analysis doneComplete it

Definition of Done

Complete ALL before declaring done:

Phase 0-1: Understanding

  • [ ] Task type identified
  • [ ] 3-5 hypotheses generated, ≥1 confirmed
  • [ ] 5 Whys completed
  • [ ] User explicitly confirmed requirements

Phase 2: Knowledge

  • [ ] Domain researched (used available tools or marked as unverified)
  • [ ] Freshness, accuracy, completeness gates passed
  • [ ] Own knowledge vs findings compared

Phase 3: Writing

  • [ ] SKILL.md body < 500 lines
  • [ ] Frontmatter valid (name, description)
  • [ ] Detailed content in references/ (not body)
  • [ ] Has decision tree or workflow
  • [ ] Has output contract

Phase 4: Validation

  • [ ] Structural checks passed
  • [ ] Portability checks passed (no hardcoded paths/tools/projects)
  • [ ] User explicitly confirmed output

Phase 5: Reflection

  • [ ] Quality score calculated
  • [ ] Improvement areas documented
  • [ ] Lessons captured

Self-check: Did I follow Phase 0 → 1 → 2 → 3 → 4 → 5 in order? If phases were skipped → go back and complete them.


References Navigation

Core Phase References

FilePurposePhase
hypothesis-ladder-for-skills.mdHypothesis generation + 5 Whys0
skill-discovery-protocol.mdSkill discovery (reuse-first)0
task-narrowing-framework.mdTask narrowing (5-layer)0
requirement-elicitation-protocol.mdRequirement elicitation1
user-requirement-validation.mdRequirement validation1
user-confirmation-protocol.mdUser confirmation template1, 4
skill-type-taxonomy.mdSkill type taxonomy1
knowledge-acquisition-guide.mdResearch protocol + 4-layer gate2
knowledge-validation-checklist.mdKnowledge validation2
deep-research-methodology.mdDeep research + domain expertise2
skill-templates.mdSkill structure templates3
writing-style-guide.mdWriting standards + style3
universality-guide.mdPortability guide3

Supporting References

FilePurpose
non-technical-methodology-research.mdNon-technical methodology
methodology-seed-database.mdMethodology seed database
learn-from-github-protocol.mdLearn from GitHub protocol
domain-expertise-protocol.mdDomain expertise protocol
docs-generation-workflow.mdDocs generation workflow
examples.mdComplete examples + patterns
patterns.mdWorkflow patterns
troubleshooting.mdCommon issues and fixes
official-best-practices.mdAnthropic official guidelines

Official Resources

ResourceURL
AgentSkills.iohttps://agentskills.io/
Skills Overviewhttps://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview
Best Practiceshttps://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices
Anthropic Skills Repohttps://github.com/anthropics/skills

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

76.74%
按下载量换算2,293

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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。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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