Agent skill

Skill Expert Skills

by LeoYeAI in LeoYeAI/openclaw-master-skills

Creates, optimizes, validates, and packages AI Agent Skills (SKILL.md format).

Apache-2.0Auto-check: notesResearch & Science

Install Skill Expert Skills

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill skill-expert-skills -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills skill-expert-skills --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/skill-expert-skills-openclaw .claude/skills/skill-expert-skills && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-expert-skills
GitHub stars
2.2k
Token cost
~4.3k tokens
SKILL.md length
1,203 words
Files
55 (incl. scripts, references)
Skills in repo
1,235
Repo updated
First seen
Licence
Apache-2.0

At a glance

Creates, optimizes, validates, and packages AI Agent Skills (SKILL.md format).

  • Works in 6 steps: Discovery + Hypothesis → Requirement Mining → Knowledge Acquisition → …
  • : - Creating a new Skill (writing a SKILL.md) - Optimizing an existing Skill (structure
  • SKILL.md covers Pre-Flight Check, Fast Track Decision, Phase 0: Discovery + Hypothesis and Phase 1: Requirement Mining, plus 3 more sections
  • Calls python

What it does

Skill Expert Skills is an agent skill from LeoYeAI/openclaw-master-skills. Creates, optimizes, validates, and packages AI Agent Skills (SKILL.md format). Mandatory 6-Phase workflow with quality gates: Phase 0: Task Classification + Hypothesis Generation Phase 1: Deep Requirement Mining + 5 Whys Phase 2: Knowledge Acquisition + Validation Phase 3: Skill Writing + Quality Check Phase 4: Validation + User Confirmation Phase 5: Self-Reflection + Knowledge Precipitation Use when: - Creating a new Skill (writing a SKILL.md) - Optimizing an existing Skill (structure, triggers, portability) -…

Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 57 other files, including scripts and reference files (for example `QUICK_NAVIGATION.md`, `_meta.json` and `docs/_index.md`). Compatibility notes: Python 3.8+ for validation scripts

It sits in Research & Science, covering Root cause analysis, Hypothesis generation and Quality gates. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is Apache-2.0.

When your agent uses it

  • : - Creating a new Skill (writing a SKILL.md) - Optimizing an existing Skill (structure
  • Portability) - Validating a Skill package - Packaging
  • Distributing a Skill Not for: regular programming
  • Business logic (use domain-specific skills)

Example prompts

  • “/skill-expert-skills”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Python 3.8+ for validation scripts
  • Pre-approved tools (allowed-tools): Read, Write, Bash, Grep, Glob

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Discovery + Hypothesis
  2. Requirement Mining
  3. Knowledge Acquisition
  4. Skill Writing
  5. Quality Validation + User Confirmation
  6. Self-Reflection + Knowledge Precipitation

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Bash
    • Grep
    • Glob

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • platform.claude.com
    • agentskills.io
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Python 3.8+ for validation scripts

    From compatibility in the SKILL.md frontmatter.

Context cost

Skill Expert Skills loads about 4.3k tokens when it runs, and up to ~111k if it reads all its reference files. Until then it costs about 170 tokens; SKILL.md has 1,203 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~170
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~111k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Bash, Grep, Glob

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its Apache-2.0 licence (© LeoYeAI). 1,203 words, ~4,320 tokens.

Download SKILL.mdSave it as .claude/skills/skill-expert-skills/SKILL.md (or your agent's skills folder). This skill also uses 54 other files; get the full folder from GitHub.
name
skill-expert-skills
description
Creates, optimizes, validates, and packages AI Agent Skills (SKILL.md format). Mandatory 6-Phase workflow with quality gates: Phase 0: Task Classification + Hypothesis Generation Phase 1: Deep Requirement Mining + 5 Whys Phase 2: Knowledge Acquisition + Validation Phase 3: Skill Writing + Quality Check Phase 4: Validation + User Confirmation Phase 5: Self-Reflection + Knowledge Precipitation Use when: - Creating a new Skill (writing a SKILL.md) - Optimizing an existing Skill (structure, triggers, portability) - Validating a Skill package - Packaging or distributing a Skill Not for: regular programming or business logic (use domain-specific skills).
allowed-tools
Read, Write, Bash, Grep, Glob
compatibility
Python 3.8+ for validation scripts
license
Apache-2.0
metadata.version
4.0.0
metadata.last_updated
2026-03-06
metadata.enhancement
v4.0 Added Fast Track Decision (Simple/Standard/Complex classification), v4.0 Adopted reference pointer pattern (-> references/xxx.md), v4.0 Added SKILL.md…

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
yaml
---
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.

Show full SKILL.md (484 more words)Show less
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
markdown
## 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:

bash
# 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

© LeoYeAI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 54 other files (scripts, references) in skills/skill-expert-skills-openclaw of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • LICENSE.txt
  • QUICK_NAVIGATION.md
  • _meta.json
  • docs/_index.md
  • references/deep-research-methodology.md
  • references/docs-generation-workflow.md
  • references/domain-expertise-protocol.md
  • references/domain-knowledge-template.md
  • references/domain-knowledge/_index.md
  • references/domain-knowledge/backend-expertise.md
  • references/domain-knowledge/bug-fixing-expertise.md
  • references/domain-knowledge/code-review-expertise.md
  • references/domain-knowledge/frontend-expertise.md
  • references/examples.md
  • references/hypothesis-ladder-for-skills.md
  • references/integration-examples.md
  • references/knowledge-acquisition-guide.md
  • … and 37 more

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Skill Expert Skills next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Skill Expert Skills compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skill Expert Skills this skillLeoYeAI/openclaw-master-skills2.2k—~4.3kAutomated safety check: NotesApache-2.0
Workflow Practiceseser/stack128—~743Automated safety check: PassCustom licence
Academic Pipelinebrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~11kAutomated safety check: PassCustom licence
Data Finderbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~1.7kAutomated safety check: PassCustom licence
Dx Devops Test Suite Runforcedotcom/sf-skills1.1k—~1.5kAutomated safety check: PassApache-2.0
Auto Review Loopappleweiping/WEIPING_WIKI119—~843Automated safety check: PassMIT

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Questions about Skill Expert Skills

What does Skill Expert Skills do?

Creates, optimizes, validates, and packages AI Agent Skills (SKILL.md format). Skill Expert Skills is an agent skill from LeoYeAI/openclaw-master-skills.md format).

When should I use Skill Expert Skills?

Skill Expert Skills fits situations like: : - Creating a new Skill (writing a SKILL.md) - Optimizing an existing Skill (structure; portability) - Validating a Skill package - Packaging; distributing a Skill Not for: regular programming; business logic (use domain-specific skills).

How do I install Skill Expert Skills in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill skill-expert-skills -a claude-code`. Or copy the skill folder (skills/skill-expert-skills-openclaw in LeoYeAI/openclaw-master-skills) into .claude/skills/skill-expert-skills in your project. Claude Code loads it when a task matches its description.

How do I install Skill Expert Skills in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill skill-expert-skills -a codex`. Or copy the skill folder (skills/skill-expert-skills-openclaw in LeoYeAI/openclaw-master-skills) into .agents/skills/skill-expert-skills in your project. Codex loads it when a task matches its description.

Can I use Skill Expert Skills in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill skill-expert-skills -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-expert-skills, .gemini/skills/skill-expert-skills, .github/skills/skill-expert-skills and .opencode/skills/skill-expert-skills in your project.

What does Skill Expert Skills need to run?

Going by SKILL.md and its folder, Skill Expert Skills needs the command-line tools its instructions call (python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Bash, Grep, Glob. Compatibility (from SKILL.md): Python 3.8+ for validation scripts.

Does Skill Expert Skills access the network?

SKILL.md names 3 domains. As links in the text: platform.claude.com, agentskills.io and github.com. This is read from the text; nothing was executed.

Is Skill Expert Skills safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Skill Expert Skills use?

Skill Expert Skills is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Expert Skills use?

About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 107k tokens, read only when the agent opens those files.

What are the alternatives to Skill Expert Skills?

Skills that share tags, products or a category with Skill Expert Skills: Workflow Practices (eser/stack, 128 stars), Academic Pipeline (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Data Finder (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars) and Dx Devops Test Suite Run (forcedotcom/sf-skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Expert Skills?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.