Skills

s01 > s02 > s03 > s04 > [ s05 ] s06 | s07 > s08 > s09 > s10 > s11 > s12

"Load knowledge when you need it, not upfront" -- inject via tool_result, not the system prompt.

Harness layer: On-demand knowledge -- domain expertise, loaded when the model asks.

Problem

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.

Solution

System prompt (Layer 1 -- always present):
+--------------------------------------+
| You are a coding agent.              |
| Skills available:                    |
|   - git: Git workflow helpers        |  ~100 tokens/skill
|   - test: Testing best practices     |
+--------------------------------------+

When model calls load_skill("git"):
+--------------------------------------+
| tool_result (Layer 2 -- on demand):  |
| <skill name="git">                   |
|   Full git workflow instructions...  |  ~2000 tokens
|   Step 1: ...                        |
| </skill>                             |
+--------------------------------------+

Layer 1: skill names in system prompt (cheap). Layer 2: full body via tool_result (on demand).

How It Works

1. Each skill is a directory containing a SKILL.md with YAML frontmatter.

skills/
  pdf/
    SKILL.md       # ---\n name: pdf\n description: Process PDF files\n ---\n ...
  code-review/
    SKILL.md       # ---\n name: code-review\n description: Review code\n ---\n ...

1. SkillLoader scans for SKILL.md files and uses the directory name as the fallback skill identifier.

class SkillLoader {
  skills: Record<string, SkillRecord> = {};

  constructor(private skillsDir: string) {
    this.loadAll();
  }

  private loadAll() {
    for (const filePath of collectSkillFiles(this.skillsDir)) {
      const text = readFileSync(filePath, "utf8");
      const { meta, body } = parseFrontmatter(text);
      const fallbackName = filePath.replace(/\\/g, "/").split("/").slice(-2, -1)[0] ?? "unknown";
      const name = meta.name || fallbackName;
      this.skills[name] = { meta, body, path: filePath };
    }
  }

  getContent(name: string): string {
    const skill = this.skills[name];
    if (!skill) {
      return `Error: Unknown skill '${name}'.`;
    }
    return `<skill name="${name}">\n${skill.body}\n</skill>`;
  }
}

1. Layer 1 goes into the system prompt. Layer 2 is just another tool handler.

const SYSTEM = `You are a coding agent at ${WORKDIR}.
Use load_skill to access specialized knowledge before tackling unfamiliar topics.

Skills available:
${skillLoader.getDescriptions()}`;

const TOOL_HANDLERS = {
  // ...base tools...
  load_skill: (input) => skillLoader.getContent(String(input.name ?? "")),
};

The model learns what skills exist (cheap) and loads them when relevant (expensive).

What Changed From s04

Component	Before (s04)	After (s05)
Tools	5 (base + task)	5 (base + load_skill)
System prompt	Static string	+ skill descriptions
Knowledge	None	skills/*/SKILL.md files
Injection	None	Two-layer (system + result)

Try It

cd learn-claude-code

cd agents-ts
npm install
npm run s05

1. What skills are available?
2. Load the agent-builder skill and follow its instructions
3. I need to do a code review -- load the relevant skill first
4. Build an MCP server using the mcp-builder skill