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dbffe7c8d0
- s03: inject reminder into tool_result instead of mutating history (#37) - s05: SkillLoader uses rglob("SKILL.md") + frontmatter name priority, matching Agent Skills standard (#30, PR #34) - CI: upgrade actions/checkout and actions/setup-node to v6 (#36) - docs: update s05 skill directory structure in all 3 languages
109 lines
3.6 KiB
Markdown
109 lines
3.6 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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## 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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