Awesome-omni-skill skill-pipeline

リサーチから Skill/Subagent 作成までを1コマンドで実行するパイプライン。トピックを指定すると、Webリサーチ → ベストプラクティス抽出 → Skill/Subagent生成 → バリデーションまで自動実行。

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/skill-pipeline" ~/.claude/skills/diegosouzapw-awesome-omni-skill-skill-pipeline && rm -rf "$T"

manifest: skills/data-ai/skill-pipeline/SKILL.md

source content

Skill Pipeline

リサーチから実装までを一気通貫で実行するパイプライン。

Prerequisites

Required subagents (

.claude/agents/

```
parallel-researcher.md
```
- Stage 1: Research
```
auto-validator.md
```
- Stage 4: Validate

Optional subagents:

```
skill-reviewer.md
```
- Optional quality check

Error if missing: Pipeline will report which subagent is unavailable and suggest creating it.

Usage

/skill-pipeline [topic] [--type skill|subagent]

例:

/skill-pipeline "Go error handling best practices" --type skill

/skill-pipeline "TypeScript型安全性" --type subagent

Workflow

Stage 1: Research (parallel-researcher)

Task: subagent_type=parallel-researcher
Prompt: "[topic]についてベストプラクティスを調査。Skill/Subagent作成に必要な情報を収集"

Output:

.research/[topic]/synthesis.json

Stage 2: Design

リサーチ結果から Skill/Subagent の設計を生成:

name: [derived from topic]
description: [1-line summary]
type: skill | subagent
triggers: [when to use]
core_functionality:
  - [capability 1]
  - [capability 2]
references_needed:
  - [reference doc 1]
tools_required:
  - [tool 1]
  - [tool 2]

Stage 3: Generate

Skill の場合

.claude/skills/[name]/
├── SKILL.md           # メインスキル定義
├── references/        # 参考ドキュメント
│   └── best-practices.md
└── scripts/           # 必要に応じてスクリプト
    └── validate.sh

Subagent の場合

.claude/agents/[name].md   # サブエージェント定義

Stage 4: Validate (auto-validator)

Task: subagent_type=auto-validator
Prompt: "生成した Skill/Subagent を検証: 構文チェック、必須フィールド確認、ベストプラクティス適合"

Stage 5: Report

最終レポートを出力:

{
  "pipeline_id": "skill-pipeline-[timestamp]",
  "topic": "Original topic",
  "type": "skill|subagent",
  "research_summary": "Key findings from research",
  "generated_files": [
    ".claude/skills/[name]/SKILL.md",
    ".claude/skills/[name]/references/best-practices.md"
  ],
  "validation_result": {
    "status": "pass|fail",
    "issues": []
  },
  "next_steps": [
    "テストしてみる: /[skill-name]",
    "改善点があれば: /skill-pipeline feedback"
  ]
}

Pipeline Stages

┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│  Research   │───▶│   Design    │───▶│  Generate   │───▶│  Validate   │───▶│   Report    │
│  (parallel) │    │             │    │             │    │  (auto)     │    │             │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
      │                  │                  │                  │                  │
      ▼                  ▼                  ▼                  ▼                  ▼
 .research/         design.yaml      .claude/skills/    validation.json    report.json
 synthesis.json                      or agents/

Error Handling

Stage	Failure	Recovery
Research	WebSearch fails	Retry with alternative queries
Design	Ambiguous requirements	Ask user for clarification
Generate	Missing template	Use default template
Validate	Syntax errors	Auto-fix and retry

Options

Option	Description	Default	Status
`--type`	`skill` or `subagent`	Inferred	Implemented
`--skip-research`	Use existing research	`false`	Implemented
`--no-validate`	Skip validation	`false`	Implemented
`--output-dir`	Custom output directory	`.claude/`	Implemented

Execution Flow

Parse input - Extract topic and options
Check existing research - If
```
.research/[topic]/synthesis.json
```
exists and
```
--skip-research
```
not set, ask to reuse
Spawn parallel-researcher - If needed
Wait for research - Monitor agent completion
Read synthesis - Extract key findings
Design structure - Create skill/subagent spec
Generate files - Write SKILL.md or agent.md
Spawn auto-validator - Validate generated files
Collect results - Merge all outputs
Report - Display summary and next steps

Example Session

User: /skill-pipeline "Claude Code hooks automation"

Pipeline Starting...

[Stage 1/5] Research
  ├─ Spawning parallel-researcher...
  ├─ 4 research agents deployed
  └─ Waiting for completion...

[Stage 2/5] Design
  ├─ Analyzing synthesis.json
  ├─ Type: skill (detected from topic)
  └─ Name: hooks-automation

[Stage 3/5] Generate
  ├─ Creating .claude/skills/hooks-automation/
  ├─ Writing SKILL.md
  └─ Writing references/hooks-patterns.md

[Stage 4/5] Validate
  ├─ Spawning auto-validator...
  ├─ Checking syntax: ✓
  ├─ Checking required fields: ✓
  └─ Checking best practices: ✓

[Stage 5/5] Report
  ✓ Pipeline completed successfully

  Generated:
  - .claude/skills/hooks-automation/SKILL.md
  - .claude/skills/hooks-automation/references/hooks-patterns.md

  Try it: /hooks-automation

Integration with Other Skills

This skill orchestrates:

```
parallel-researcher
```
(subagent) - For comprehensive research
```
auto-validator
```
(subagent) - For validation
```
skill-reviewer
```
(subagent) - Optional quality check

Best Practices Encoded

From Claude Code official guidelines:

Skills are single-task focused
Subagents handle multi-step workflows
Always include
```
allowed-tools
```
or
```
tools
```
Use
```
model: haiku
```
for simple skills
Include triggers in description