project-skill-scripts（项目技能脚本）

/project:skill-scripts

Analyze plugin skills to identify opportunities where supporting scripts would improve performance (fewer tokens, faster execution, consistent results), then optionally create those scripts.

When to Use This Skill

Context

• Plugin root:!git rev-parse --show-toplevel
• Total plugins:!find. -maxdepth 2 -name 'plugin.json' -type f
• Skills with scripts:!find. -name 'scripts/*.sh' -type f

Parameters

Parse $ARGUMENTS for:

• --analyze: Scan all skills, report candidates (default)
• --create <plugin/skill>: Create script for specific skill only
• --all: Analyze and create scripts for all high-scoring candidates

Execution

Execute this skill script analysis and creation workflow:

Step 1: Run analysis script

Execute analyzer to get structured data on all skills:

1. Run analyzer: bash "${CLAUDE_PLUGIN_ROOT}/skills/project-discovery/scripts/analyze-skills.sh" $(git rev-parse --show-toplevel 2>/dev/null || echo '.')
2. Parse output to identify:

- Current coverage (skills with scripts) - High-scoring candidates (score >= 8) - Script type recommendations

Step 2: Analyze candidates (--create or --all modes)

For each candidate skill:

1. Read SKILL.md to understand the workflow
2. Identify script opportunity patterns:

- Multiple sequential git/gh commands → context-gather script - Multi-phase workflow → workflow script - Project type detection + conditional execution → multi-tool script - Repeated command with different args → utility script

1. Evaluate benefit: >= 4 tool calls, consistency, error handling, reuse frequency
2. Skip if: single simple commands, interactive/creative, already well-structured

Step 3: Create scripts

For approved candidates:

1. Use standard script template with structured output (KEY=value, section markers)
2. Follow design principles: structured output, error resilience, bounded output, portable
3. Place in <plugin>/skills/<skill-name>/scripts/<script-name>.sh
4. Make executable: chmod +x <path>
5. Update SKILL.md with "Recommended" section referencing the script
6. Update modified: date in frontmatter

Step 4: Report results

Present findings:

• Current coverage (X/Y skills have scripts)
• Scripts created (plugin, skill, script, type, commands replaced)
• Remaining candidates (plugin, skill, score, type, recommendation)
• Next steps (test, commit)

Step 5: Commit changes

If scripts created:

feat(<affected-plugins>): add supporting scripts to skills

Include in body:

• Which scripts were created
• What they replace (token/call savings)
• Which SKILL.md files were updated

Examples

Analyze Only

$ /project:skill-scripts --analyze

Skill Scripts Analysis

Current Coverage: 5/191 skills have supporting scripts

Top Candidates:
  git-plugin/gh-cli-agentic         score=14  type=context-gather
  kubernetes-plugin/kubectl-debugging score=12  type=multi-tool
  testing-plugin/playwright-testing   score=10  type=workflow

Create for Specific Skill

$ /project:skill-scripts --create testing-plugin/playwright-testing

Analyzing testing-plugin/playwright-testing...
Found: 6 bash blocks, 3 phases, 12 commands

Creating scripts/run-tests.sh...
- Consolidates: test discovery, execution, report parsing
- Replaces: 5 individual tool calls
- Output: structured test results with file:line references

Updated SKILL.md with "Recommended" section.

Error Handling