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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.
111 lines
3.7 KiB
Markdown
111 lines
3.7 KiB
Markdown
# s05: Skills (技能加载)
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`s01 > s02 > s03 > s04 > [ s05 ] s06 | s07 > s08 > s09 > s10 > s11 > s12`
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> *"用到什么知识, 临时加载什么知识"* -- 通过 tool_result 注入, 不塞 system prompt。
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>
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> **Harness 层**: 按需知识 -- 模型开口要时才给的领域专长。
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## 问题
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你希望智能体遵循特定领域的工作流: git 约定、测试模式、代码审查清单。全塞进系统提示太浪费 -- 10 个技能, 每个 2000 token, 就是 20,000 token, 大部分跟当前任务毫无关系。
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## 解决方案
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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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第一层: 系统提示中放技能名称 (低成本)。第二层: tool_result 中按需放完整内容。
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## 工作原理
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1. 每个技能是一个目录, 包含 `SKILL.md` 文件和 YAML frontmatter。
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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 递归扫描 `SKILL.md` 文件, 用目录名作为技能标识。
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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. 第一层写入系统提示。第二层不过是 dispatch map 中的又一个工具。
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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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模型知道有哪些技能 (便宜), 需要时再加载完整内容 (贵)。
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## 相对 s04 的变更
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| 组件 | 之前 (s04) | 之后 (s05) |
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|----------------|------------------|--------------------------------|
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| Tools | 5 (基础 + task) | 5 (基础 + load_skill) |
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| 系统提示 | 静态字符串 | + 技能描述列表 |
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| 知识库 | 无 | skills/\*/SKILL.md 文件 |
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| 注入方式 | 无 | 两层 (系统提示 + result) |
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## 试一试
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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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试试这些 prompt (英文 prompt 对 LLM 效果更好, 也可以用中文):
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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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