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104 lines
3.4 KiB
Markdown
104 lines
3.4 KiB
Markdown
# s05: Skills
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`s01 > s02 > s03 > s04 > [ s05 ] s06 | s07 > s08 > s09 > s10 > s11 > s12`
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> *"Load on demand, not upfront"* -- inject knowledge via tool_result, not system prompt.
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## Problem
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You want the agent to follow domain-specific workflows: git conventions, testing patterns, code review checklists. Putting everything in the system prompt wastes tokens on unused skills. 10 skills at 2000 tokens each = 20,000 tokens, most of which are irrelevant to any given task.
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## Solution
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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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Layer 1: skill *names* in system prompt (cheap). Layer 2: full *body* via tool_result (on demand).
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## How It Works
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1. Skill files live in `.skills/` as Markdown with YAML frontmatter.
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```
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.skills/
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git.md # ---\n description: Git workflow\n ---\n ...
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test.md # ---\n description: Testing patterns\n ---\n ...
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```
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2. SkillLoader parses frontmatter, separates metadata from body.
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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.glob("*.md")):
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text = f.read_text()
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meta, body = self._parse_frontmatter(text)
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self.skills[f.stem] = {"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. Layer 1 goes into the system prompt. Layer 2 is just another tool handler.
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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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The model learns what skills exist (cheap) and loads them when relevant (expensive).
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## What Changed From s04
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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/*.md files |
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| Injection | None | Two-layer (system + result)|
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## Try It
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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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