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improvement-workflow改进工作流程

Agent Skill

improvement-workflow 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

416

周安装

17

GitHub Stars

9

下载量

133
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

3

许可证

MIT

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:improvement-workflow(改进工作流程)
来源仓库:https://github.com/adaptationio/skrillz
仓库路径:skills/improvement-workflow
安装命令:
npx skills add https://github.com/adaptationio/skrillz --skill improvement-workflow
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/adaptationio/skrillz --skill improvement-workflow

简介

该技能用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • 建议结合原始 README 文档进一步核验具体用法和功能边界。

SKILL.md

Improvement Workflow

Overview

improvement-workflow orchestrates the complete continuous improvement cycle for Claude Code skills, transforming review findings into applied improvements and validated enhancements.

Purpose: End-to-end skill improvement from review through validated enhancement

Component Skills (5):

  1. review-multi - Comprehensive multi-dimensional review (identify issues)
  2. analysis - Pattern analysis across findings (understand systemic issues)
  3. best-practices-learner - Extract learnings (capture insights)
  4. skill-updater - Apply improvements (implement changes)
  5. skill-validator - Validate improvements (ensure quality maintained)

Workflow Pattern: Sequential pipeline with feedback loop

Result: Systematically improved skills with validated enhancements and captured learnings

When to Use

  • Continuous improvement iterations (make good skills better)
  • Applying review recommendations (systematic implementation)
  • Post-deployment enhancement (v1.0 → v1.1)
  • Multiple skill improvements (consistent process)
  • Learning-driven development (capture and apply insights)

Improvement Workflow

Step 1: Comprehensive Review (review-multi)

Purpose: Identify improvement opportunities through multi-dimensional assessment

Process: Run review-multi comprehensive mode (all 5 operations)

Outputs:

  • Overall score and grade
  • Per-dimension scores
  • Prioritized improvement recommendations
  • Identified issues and anti-patterns

Time: 1.5-2.5 hours


Step 2: Pattern Analysis (analysis)

Purpose: Understand systemic patterns in findings

Process: Use analysis Operation 5 (Pattern Recognition)

  • Review findings from Step 1
  • Identify recurring themes (if reviewing multiple skills)
  • Understand root causes
  • Prioritize by impact

Outputs:

  • Pattern analysis (systemic issues vs one-offs)
  • Root cause understanding
  • Impact-prioritized improvements

Time: 45-90 minutes


Step 3: Extract Learnings (best-practices-learner)

Purpose: Capture insights for future application

Process: Use best-practices-learner Operations 1-2

  • Extract patterns from review findings
  • Document what worked/didn't work
  • Capture insights for guidelines

Outputs:

  • Documented patterns
  • Learnings log
  • Insights for guideline updates

Time: 30-60 minutes


Step 4: Apply Improvements (skill-updater)

Purpose: Systematically implement improvements

Process: Use skill-updater workflow

  1. Plan updates (prioritize recommendations)
  2. Backup skill
  3. Apply changes (one at a time)
  4. Test each change

Outputs:

  • Updated skill with improvements applied
  • Change documentation
  • Version update

Time: 1-4 hours (varies by number of improvements)


Step 5: Validate Improvements (skill-validator + review-multi)

Purpose: Ensure improvements effective, no regressions

Process:

  1. Run skill-validator (ensure still passes minimum standards)
  2. Re-run review-multi (compare before/after scores)
  3. Validate improvements achieved goals
  4. Document impact

Outputs:

  • Validation results (pass/fail)
  • Before/after score comparison
  • Impact measurement
  • Regression check

Time: 30-60 minutes


Step 6: Update Guidelines (best-practices-learner)

Purpose: Feed learnings back into ecosystem

Process: Use best-practices-learner Operation 3

  • Update common-patterns.md with new patterns
  • Update templates if needed
  • Propagate learnings to future skills

Outputs:

  • Updated guidelines
  • Improved templates
  • Enhanced ecosystem knowledge

Time: 20-40 minutes


Post-Workflow: Iteration Decision

After completing workflow:

If Score Improved Significantly (≥0.5 points):

  • ✅ Improvements effective
  • Document success
  • Apply similar improvements to other skills

If Score Improved Slightly (<0.5 points):

  • ⚠️ Minor impact
  • Assess if effort worth benefit
  • Consider different improvements

If Score Unchanged or Decreased:

  • ❌ Improvements ineffective or caused regressions
  • Review what went wrong
  • Revert changes if regression
  • Try different approach

Iterate:

  • Can run workflow again for further improvements
  • Diminishing returns after 2-3 iterations
  • Focus on highest-impact improvements first

Best Practices

1. Review Before Improve

Practice: Always review comprehensively before changing

Rationale: Understand current state fully prevents fixing wrong things

2. Prioritize by Impact

Practice: Apply high-impact improvements first

Rationale: Maximum benefit for effort invested

3. One Improvement at a Time

Practice: Apply and validate each change individually

Rationale: Prevents compounding errors, identifies what actually helped

4. Measure Impact

Practice: Compare before/after scores objectively

Rationale: Data-driven understanding of effectiveness

5. Capture Learnings

Practice: Document what worked for future application

Rationale: Learnings compound across improvements


Quick Reference

The 6-Step Improvement Workflow

StepSkillPurposeTimeOutput
1review-multiComprehensive review1.5-2.5hScores, recommendations
2analysisPattern analysis45-90mSystemic insights
3best-practices-learnerExtract learnings30-60mDocumented patterns
4skill-updaterApply improvements1-4hUpdated skill
5skill-validator + review-multiValidate improvements30-60mImpact measurement
6best-practices-learnerUpdate guidelines20-40mEnhanced ecosystem

Total Time: 5-9 hours for complete improvement cycle

Workflow Pattern

review-multi → Identify improvements
    ↓
analysis → Understand patterns
    ↓
best-practices-learner → Extract learnings
    ↓
skill-updater → Apply changes
    ↓
validate → Measure impact
    ↓
Update guidelines → Feed ecosystem

Typical Improvements

  • Add Quick Reference (UX)
  • Enhance examples (clarity)
  • Refine validation criteria (specificity)
  • Add error handling (completeness)
  • Improve integration docs (workflow skills)

improvement-workflow enables systematic, validated, learning-driven skill enhancement with ecosystem-wide knowledge propagation.

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

04

需要参考平台分布和安装热度时

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

Claude Code

29.69%
按下载量换算39

openclaw

24.6%
按下载量换算33

github-copilot

18.98%
按下载量换算25

Codex

12.98%
按下载量换算17

windsurf

8.39%
按下载量换算11

Cursor

3.55%
按下载量换算5

安全审计

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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。

来源信息

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