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better-skill-review更好的技能审查

Agent Skill

better-skill-review 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

275

周安装

11

GitHub Stars

4

下载量

89
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:better-skill-review(更好的技能审查)
来源仓库:https://github.com/psylch/better-skills
仓库路径:skills/better-skill-review
安装命令:
npx skills add https://github.com/psylch/better-skills --skill better-skill-review
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/psylch/better-skills --skill better-skill-review

简介

通过自动化 linting、上下文评估与语义审查三层机制审核 Agent Skill 质量。

  • 可选自动修复问题并通过独立子代理验证修复结果。
  • 确保技能符合最佳实践,提升可维护性与用户体验。
  • 使用前应提供完整技能描述与示例,以便准确识别问题点。
  • better-skill-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Skill Review

Language

Match user's language: Respond in the same language the user uses.

Overview

Review an agent skill through three layers: automated linting (hard rules), contextual finding evaluation (agent judges with context), and structured semantic review (deep analysis against best practices). Optionally fix issues and verify fixes through independent subagent validation.

Workflow

Progress:

  • Step 1: Identify the skill
  • Step 2: Review (linting + profile + findings + semantic)
  • Step 3: Present findings
  • Step 4: Fix gate
  • Step 5: Verify & iterate

Step 1: Identify the Skill

Accept the skill location as a directory path containing SKILL.md. Auto-detect if the current working directory contains one.

Step 2: Review

Run both tools, then evaluate.

2a. Automated linting:

python3 {SKILL_DIR}/scripts/validate.py run --path <skill-path>

Output: checks (hard-rule verdicts → linter grade) + findings (soft detections needing judgment).

2b. Profile extraction:

bash {SKILL_DIR}/scripts/analyze.sh analyze <skill-path>

Note skill level (l0/l0plus/l1) and feature flags.

2c. Contextual findings review:

Judge each finding using its context_hint. See references/semantic_dimensions.md § Finding Judgment Table for decision criteria. Promote real issues to warnings; dismiss the rest with a brief note.

2d. Semantic review:

Read the skill's SKILL.md fully, then score each dimension (0-3). Read references/semantic_dimensions.md for the full checklist per dimension:

DimensionWhat it evaluates
5.1 Description QualityTrigger phrases, length, voice, specificity
5.2 Workflow DesignSteps, decision points, SKILL.md length
5.3 Runtime RobustnessPreflight, degradation, troubleshooting (L0+/L1 only)
5.4 Script QualityJSON output, error handling, exit codes (scripts only)
5.5 UX PracticesLanguage, checklist, completion report (applicability matrix)
5.6 Setup Flow IntegrityBootstrap safety, live validation, credential security

Step 3: Present Findings

Format the report:

[Skill Review] <skill-name>

═══ Linter ═══
Grade: <letter> (<pass>/<total> passed, <warn> warnings, <fail> failures)

Failures:
  ✗ <check_id>: <message> → Fix: <fix>

Warnings:
  ⚠ <check_id>: <message>

Findings (agent-reviewed):
  ✓ <finding_id>: dismissed — <reason>
  ⚠ <finding_id>: promoted to warning — <reason>

═══ Semantic Review ═══
5.1 Description Quality:   <score>/3  <one-line assessment>
5.2 Workflow Design:        <score>/3  <one-line assessment>
5.3 Runtime Robustness:     <score>/3  <one-line assessment>
5.4 Script Quality:         <score>/3  <one-line assessment>
5.5 UX Practices:           <score>/3  <one-line assessment>
5.6 Setup Flow Integrity:   <score>/3  <one-line assessment>
                            ─────────
Semantic Score:             <total>/18

═══ Improvement Suggestions ═══
For each dimension scoring < 3, provide:
  1. What to change and why
  2. Which file to edit
  3. A concrete before/after example or specific instruction
  4. Priority: High (functionality/UX) / Medium (convention) / Low (polish)

If linter grade is A and semantic score ≥ 15: congratulate and suggest publishing with better-skill-publish.

Step 4: Fix Gate

Ask the user:

  • Fix all — Apply all suggested changes, then auto-verify (Step 5)
  • Pick and choose — Select specific items, then auto-verify (Step 5)
  • None — End here, use the report as reference

After fixing, do NOT self-evaluate. Proceed directly to Step 5.

Step 5: Verify & Iterate

CRITICAL: Verification must be independent. The agent that fixed cannot judge its own work.

5a. Dispatch verification subagent:

Agent(subagent_type: "general-purpose", description: "Verify skill review", prompt: <template>)

Use the prompt template from references/semantic_dimensions.md § Verification Subagent Prompt Template. The verification subagent must:

  1. Run validate.py and analyze.sh fresh (never trust cached results)
  2. Read references/semantic_dimensions.md for scoring criteria
  3. Score all 6 dimensions independently, citing file:line evidence
  4. Return linter grade + per-dimension scores + specific FAIL items

5b. Process verification result:

  • All pass → Report success, done
  • Has failures → Fix ONLY the specific FAIL items from the verification report, then dispatch a NEW subagent to verify again
  • Max 3 rounds — if issues persist after 3 fix-verify cycles, report remaining issues and let the user decide

Anti-patterns (never do these):

Anti-patternWhy it's wrongCorrect approach
Self-evaluate after fixingBlind to own mistakesAlways dispatch subagent
Reuse same subagent for re-verifyAlready biased by prior assessmentNew subagent each round
Fix without running tools firstSubjective judgment misses issuesTools produce objective data
Ignore verification reportDiscards independent evidenceFix only reported FAIL items

Linter Reference

Grading (hard-rule checks only):

  • A — All pass, zero warnings
  • B — All pass, some warnings
  • C — 1–2 failures
  • D — 3+ failures
  • F — SKILL.md missing or no valid frontmatter

Check categories:

CategoryChecks
structureSKILL.md exists, frontmatter, required fields, directory layout
namingKebab-case, length, no consecutive hyphens, matches directory
contentDescription length, body length, heading structure
pathsReferenced files exist, scripts executable
securityNo secrets, no template placeholders

References

  • references/semantic_dimensions.md — Full checklist for each review dimension + finding judgment table + verification prompt template
  • references/validation_rules.md — Rationale for each linter check
  • references/improvement_patterns.md — Knowledge base of improvement patterns with examples
  • references/best_practices.md — Skill design conventions and quick reference

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

32.04%
按下载量换算29

Claude

31.31%
按下载量换算28

Cursor

18.51%
按下载量换算16

Gemini CLI

8.82%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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