chore: Spring AI 重构
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abel533
@@ -8,7 +8,7 @@
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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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You want the agent to follow domain-specific workflows: git conventions, testing patterns, code review checklists. Putting everything in the system prompt wastes tokens -- 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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@@ -35,7 +35,7 @@ Layer 1: skill *names* in system prompt (cheap). Layer 2: full *body* via tool_r
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## How It Works
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1. Each skill is a directory containing a `SKILL.md` with YAML frontmatter.
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1. Each skill is a directory containing a `SKILL.md` file with YAML frontmatter.
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```
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skills/
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@@ -45,42 +45,87 @@ skills/
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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 scans for `SKILL.md` files, uses the directory name as the skill identifier.
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2. SkillLoader recursively scans for `SKILL.md` files, using the directory name as the skill identifier.
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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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```java
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public class SkillLoader {
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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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private static final Pattern FRONTMATTER_PATTERN =
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Pattern.compile("^---\\n(.*?)\\n---\\n(.*)", Pattern.DOTALL);
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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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private final Map<String, SkillInfo> skills = new LinkedHashMap<>();
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record SkillInfo(Map<String, String> meta, String body, String path) {}
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public SkillLoader(Path skillsDir) {
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loadAll(skillsDir);
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}
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/** Recursively scan all SKILL.md files under the skills directory */
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private void loadAll(Path skillsDir) {
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if (!Files.exists(skillsDir)) return;
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try (Stream<Path> paths = Files.walk(skillsDir)) {
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paths.filter(p -> p.getFileName().toString().equals("SKILL.md"))
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.sorted()
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.forEach(p -> {
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String text = Files.readString(p);
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var parsed = parseFrontmatter(text);
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String name = parsed.meta().getOrDefault("name",
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p.getParent().getFileName().toString());
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skills.put(name, new SkillInfo(
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parsed.meta(), parsed.body(), p.toString()));
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});
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}
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}
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/** Layer 1: Get short descriptions of all skills (for system prompt injection) */
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public String getDescriptions() {
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if (skills.isEmpty()) return "(no skills available)";
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StringBuilder sb = new StringBuilder();
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for (var entry : skills.entrySet()) {
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String desc = entry.getValue().meta()
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.getOrDefault("description", "No description");
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sb.append(" - ").append(entry.getKey())
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.append(": ").append(desc).append("\n");
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}
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return sb.toString().stripTrailing();
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}
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/** Layer 2: Load full content of a specified skill (as @Tool method) */
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@Tool(description = "Load specialized knowledge by name.")
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public String loadSkill(
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@ToolParam(description = "Skill name to load") String name) {
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SkillInfo skill = skills.get(name);
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if (skill == null)
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return "Error: Unknown skill '" + name + "'. Available: "
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+ String.join(", ", skills.keySet());
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return "<skill name=\"" + name + "\">\n"
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+ skill.body() + "\n</skill>";
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}
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}
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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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3. Layer 1 goes into the system prompt. Layer 2 is loaded on demand via the `@Tool` annotated method on SkillLoader.
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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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```java
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public S05SkillLoading(ChatModel chatModel) {
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Path skillsDir = Path.of(System.getProperty("user.dir"), "skills");
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SkillLoader skillLoader = new SkillLoader(skillsDir);
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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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// Layer 1: Skill metadata injected into system prompt
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String system = "You are a coding agent at " + System.getProperty("user.dir") + ".\n"
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+ "Use loadSkill to access specialized knowledge.\n\n"
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+ "Skills available:\n"
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+ skillLoader.getDescriptions();
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this.chatClient = ChatClient.builder(chatModel)
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.defaultSystem(system)
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.defaultTools(
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new BashTool(), new ReadFileTool(),
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new WriteFileTool(), new EditFileTool(),
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skillLoader // Layer 2: loadSkill @Tool method
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)
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.build();
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}
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```
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@@ -88,20 +133,22 @@ The model learns what skills exist (cheap) and loads them when relevant (expensi
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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/\*/SKILL.md files |
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| Injection | None | Two-layer (system + result)|
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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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## 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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mvn exec:java -Dexec.mainClass=io.mybatis.learn.s05.S05SkillLoading
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```
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Try these prompts (English prompts work better with LLMs, but Chinese also works):
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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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