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a9c71002d2
Comprehensive rewrite establishing the harness engineering narrative across the entire repository. README (EN/ZH/JA): added "The Model IS the Agent" manifesto with historical proof (DQN, OpenAI Five, AlphaStar, Tencent Jueyu), "What an Agent Is NOT" critique, harness engineer role definition, "Why Claude Code" as masterclass in harness design, and universe vision. Consistent framing: model = driver, harness = vehicle. docs (36 files, 3 languages): injected one-line "Harness layer" callout after the motto in every session document (s01-s12). agents (13 Python files): added harness framing comment before each module docstring. skills/agent-philosophy.md: full rewrite aligned with harness narrative.
109 lines
3.9 KiB
Markdown
109 lines
3.9 KiB
Markdown
# s05: Skills
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`s01 > s02 > s03 > s04 > [ s05 ] s06 | s07 > s08 > s09 > s10 > s11 > s12`
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> *"必要な知識を、必要な時に読み込む"* -- system prompt ではなく tool_result で注入。
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>
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> **Harness 層**: オンデマンド知識 -- モデルが求めた時だけ渡すドメイン専門性。
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## 問題
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エージェントにドメイン固有のワークフローを遵守させたい: gitの規約、テストパターン、コードレビューチェックリスト。すべてをシステムプロンプトに入れると、使われないスキルにトークンを浪費する。10スキル x 2000トークン = 20,000トークン、ほとんどが任意のタスクに無関係だ。
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## 解決策
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```
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System prompt (Layer 1 -- always present):
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+--------------------------------------+
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| You are a coding agent. |
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| Skills available: |
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| - git: Git workflow helpers | ~100 tokens/skill
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| - test: Testing best practices |
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+--------------------------------------+
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When model calls load_skill("git"):
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+--------------------------------------+
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| tool_result (Layer 2 -- on demand): |
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| <skill name="git"> |
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| Full git workflow instructions... | ~2000 tokens
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| Step 1: ... |
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| </skill> |
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+--------------------------------------+
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```
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第1層: スキル*名*をシステムプロンプトに(低コスト)。第2層: スキル*本体*をtool_resultに(オンデマンド)。
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## 仕組み
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1. 各スキルは `SKILL.md` ファイルを含むディレクトリとして配置される。
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```
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skills/
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pdf/
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SKILL.md # ---\n name: pdf\n description: Process PDF files\n ---\n ...
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code-review/
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SKILL.md # ---\n name: code-review\n description: Review code\n ---\n ...
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```
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2. SkillLoaderが `SKILL.md` を再帰的に探索し、ディレクトリ名をスキル識別子として使用する。
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```python
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class SkillLoader:
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def __init__(self, skills_dir: Path):
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self.skills = {}
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for f in sorted(skills_dir.rglob("SKILL.md")):
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text = f.read_text()
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meta, body = self._parse_frontmatter(text)
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name = meta.get("name", f.parent.name)
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self.skills[name] = {"meta": meta, "body": body}
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def get_descriptions(self) -> str:
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lines = []
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for name, skill in self.skills.items():
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desc = skill["meta"].get("description", "")
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lines.append(f" - {name}: {desc}")
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return "\n".join(lines)
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def get_content(self, name: str) -> str:
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skill = self.skills.get(name)
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if not skill:
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return f"Error: Unknown skill '{name}'."
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return f"<skill name=\"{name}\">\n{skill['body']}\n</skill>"
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```
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3. 第1層はシステムプロンプトに配置。第2層は通常のツールハンドラ。
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```python
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SYSTEM = f"""You are a coding agent at {WORKDIR}.
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Skills available:
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{SKILL_LOADER.get_descriptions()}"""
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TOOL_HANDLERS = {
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# ...base tools...
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"load_skill": lambda **kw: SKILL_LOADER.get_content(kw["name"]),
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}
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```
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モデルはどのスキルが存在するかを知り(低コスト)、関連する時にだけ読み込む(高コスト)。
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## s04からの変更点
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| Component | Before (s04) | After (s05) |
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|----------------|------------------|----------------------------|
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| Tools | 5 (base + task) | 5 (base + load_skill) |
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| System prompt | Static string | + skill descriptions |
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| Knowledge | None | skills/\*/SKILL.md files |
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| Injection | None | Two-layer (system + result)|
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## 試してみる
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```sh
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cd learn-claude-code
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python agents/s05_skill_loading.py
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```
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1. `What skills are available?`
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2. `Load the agent-builder skill and follow its instructions`
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3. `I need to do a code review -- load the relevant skill first`
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4. `Build an MCP server using the mcp-builder skill`
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