steven_li
520a21a027
添加 DEFAULT_TEAM_NODE_MAX_TOOL_ITERATIONS 配置项以控制团队节点的最大工具迭代次数， 并修改 LocalAgentRunner 中的逻辑来使用此默认值当 envelope 中未指定时。 fix(runtime): 修复团队节点运行成功判断逻辑 更新运行成功判断条件，将 finish_reason 为 "max_tool_iterations_finalized" 的情况 视为运行失败，并添加对原始工具调用输出的检测，避免将其误判为成功完成。 feat(mcp): 添加团队工作流MCP工具类别支持 增加新的本地MCP工具类别 "team_workflow" 及其对应的工具创建功能， 为团队工作流提供本地工具支持。 refactor(engine): 调整AgentLoop最大工具迭代次数设置 将 AgentProfile 中的默认 max_tool_iterations 从 30 增加到 100， 同时移除 TaskExecutionPlanner 构造函数中的重复参数传递。 perf(mcp): 优化MCP连接管理避免重复连接 添加 mcp_connected 标志来跟踪MCP连接状态，确保 connect_all 只执行一次， 提高性能并避免不必要的重复连接。 refactor(skills): 移除技能团队模板相关功能 移除与技能团队模板相关的代码，包括解析、存储和处理逻辑， 简化技能记录结构和加载流程。 feat(process): 增强会话过程投影器功能 添加技能激活快照事件处理，改进团队运行完成消息显示， 并增强技能激活事件的时间戳记录功能。 refactor(tasks): 简化任务尝试编排器团队执行逻辑 移除团队执行相关代码，将所有任务统一按单步执行处理， 简化任务编排器的复杂度并提升执行效率。 fix(evidence): 修复节点证据评估中需求验证逻辑 更新节点证据评估逻辑，跳过自然语言证据需求的确定性验证， 只执行机器可读的需求验证，避免因自然语言需求导致的节点失败。
447 lines
17 KiB
Python
447 lines
17 KiB
Python
"""Beaver skills catalog loader。
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第一版目标非常明确：
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1. 扫描技能目录
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2. 读取 `SKILL.md`
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3. 解析前置元数据
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4. 生成可注入上下文的正文与索引
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这层不负责：
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1. 动态选择本轮应该启用哪些 skill
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2. skill review / publishing
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3. skill 自动学习
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这些决策属于 resolver 或更高层工作流。
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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import json
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from pathlib import Path
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from typing import Any
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from beaver.skills.specs.storage import SkillSpecStore
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from .utils import (
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check_requirements,
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escape_xml,
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extract_required_tool_names,
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get_missing_requirements,
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parse_frontmatter,
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parse_skill_metadata_blob,
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strip_frontmatter,
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)
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@dataclass(slots=True)
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class SkillRecord:
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"""单个 skill 的目录级元数据。"""
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name: str
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path: Path
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source: str
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version: str = "legacy"
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content_hash: str | None = None
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source_kind: str = "legacy"
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status: str = "active"
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tool_hints: list[str] = field(default_factory=list)
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frontmatter: dict[str, Any] = field(default_factory=dict)
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description: str = ""
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class SkillsLoader:
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"""从 workspace/builtin 目录中发现并读取 skills。"""
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def __init__(
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self,
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workspace: str | Path,
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*,
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builtin_skills_dir: str | Path | None = None,
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extra_dirs: list[str | Path] | None = None,
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skill_store: SkillSpecStore | None = None,
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) -> None:
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self.workspace = Path(workspace)
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self.workspace_skills = self.workspace / "skills"
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self.builtin_skills = Path(builtin_skills_dir) if builtin_skills_dir is not None else Path(__file__).resolve().parent.parent / "builtin"
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self.extra_dirs = [Path(item) for item in (extra_dirs or [])]
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self.skill_store = skill_store or SkillSpecStore(self.workspace)
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def list_skills(
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self,
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*,
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filter_unavailable: bool = True,
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include_internal: bool = False,
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) -> list[SkillRecord]:
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"""列出当前可见的 skills。
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优先级：
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1. workspace
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2. extra/plugin 目录
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3. builtin
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重名 skill 只保留优先级更高的那一个。
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"""
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found: dict[str, SkillRecord] = {}
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for record in self.list_published_skills(filter_unavailable=filter_unavailable):
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if record.name in found:
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continue
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if not include_internal and self._record_internal(record):
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continue
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if filter_unavailable and not self._record_available(record):
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continue
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found[record.name] = record
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for source, root in [
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*[("plugin", path) for path in self.extra_dirs],
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("builtin", self.builtin_skills),
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]:
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if not root.exists():
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continue
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for skill_dir in root.iterdir():
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skill_file = skill_dir / "SKILL.md"
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if not skill_dir.is_dir() or not skill_file.exists():
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continue
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name = skill_dir.name
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if name in found:
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continue
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frontmatter, body = parse_frontmatter(skill_file.read_text(encoding="utf-8"))
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if not include_internal and _truthy(frontmatter.get("internal")):
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continue
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normalized_frontmatter = dict(frontmatter)
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meta_blob = parse_skill_metadata_blob(frontmatter.get("metadata", ""))
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record = SkillRecord(
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name=name,
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path=skill_file,
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source=source,
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version="legacy",
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source_kind=source,
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tool_hints=self._merge_tool_names(
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self._coerce_tool_names(frontmatter.get("tools")),
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self._coerce_tool_names(meta_blob.get("tools")),
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self._coerce_tool_names(meta_blob.get("required_tools")),
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extract_required_tool_names(body),
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),
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frontmatter=normalized_frontmatter,
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description=str(frontmatter.get("description") or summarize_body(body) or name),
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)
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if filter_unavailable and not self._record_available(record):
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continue
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found[name] = record
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return list(found.values())
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def list_published_skills(self, *, filter_unavailable: bool = True) -> list[SkillRecord]:
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"""只列 workspace 中正式 published 的 skill catalog。"""
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results: list[SkillRecord] = []
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for name in self.skill_store.list_published_skill_names():
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loaded = self.skill_store.read_published_skill(name)
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if loaded is None:
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continue
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if loaded.version.version == "legacy":
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path = self.workspace_skills / name / "SKILL.md"
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else:
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path = self.workspace_skills / name / "versions" / loaded.version.version / "SKILL.md"
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_frontmatter, body = parse_frontmatter(loaded.content)
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record = SkillRecord(
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name=name,
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path=path,
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source="workspace",
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version=loaded.version.version,
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content_hash=loaded.version.content_hash,
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source_kind=str(loaded.version.provenance.get("source_kind") or "workspace"),
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status=str(loaded.version.review_state or "published"),
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tool_hints=self._merge_tool_names(
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loaded.version.tool_hints,
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extract_required_tool_names(body),
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),
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frontmatter=dict(loaded.version.frontmatter),
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description=str(loaded.version.frontmatter.get("description") or loaded.version.summary or name),
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)
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if filter_unavailable and not self._record_available(record):
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continue
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results.append(record)
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return results
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def get_current_version(self, name: str) -> str | None:
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record = self._find_record(name)
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return record.version if record is not None else None
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def load_published_skill(self, name: str, version: str | None = None) -> str | None:
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loaded = self.skill_store.read_published_skill(name, version=version)
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if loaded is not None:
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return loaded.content
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return self.load_skill(name)
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def load_skill(self, name: str) -> str | None:
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"""按名称加载 skill 原始内容。"""
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record = self._find_record(name)
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if record is None:
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return None
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return record.path.read_text(encoding="utf-8")
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def get_skill_record(self, name: str) -> SkillRecord | None:
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"""按名称返回 skill record。"""
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return self._find_record(name)
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def get_skill_metadata(self, name: str) -> dict[str, Any] | None:
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"""读取 skill frontmatter 元数据。"""
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record = self._find_record(name)
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if record is not None and record.frontmatter:
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return dict(record.frontmatter)
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content = self.load_skill(name)
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if content is None:
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return None
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metadata, _ = parse_frontmatter(content)
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return metadata
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def get_skill_tool_hints(self, name: str) -> list[str]:
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"""读取 skill 显式声明的推荐工具。
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第一版只信任显式 metadata，不从正文里猜：
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- `tools: read_file, search_files`
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- `tools: ["read_file", "search_files"]`
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- YAML-like list:
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tools:
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- read_file
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- search_files
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- 兼容 metadata JSON blob 里的 `tools`
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- 兼容 canonical 正文 `## Required Tools` 段落
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"""
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record = self._find_record(name)
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if record is not None and record.tool_hints:
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return list(record.tool_hints)
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content = self.load_published_skill(name) or self.load_skill(name) or ""
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frontmatter, body = parse_frontmatter(content)
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frontmatter = frontmatter or self.get_skill_metadata(name) or {}
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meta_blob = parse_skill_metadata_blob(frontmatter.get("metadata", ""))
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names = self._merge_tool_names(
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self._coerce_tool_names(frontmatter.get("tools")),
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self._coerce_tool_names(meta_blob.get("tools")),
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self._coerce_tool_names(meta_blob.get("required_tools")),
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extract_required_tool_names(body),
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)
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return names
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@staticmethod
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def _merge_tool_names(*groups: Any) -> list[str]:
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result: list[str] = []
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for group in groups:
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for item in SkillsLoader._coerce_tool_names(group):
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if item and item not in result:
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result.append(item)
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return result
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def load_skills_for_context(self, skill_names: list[str]) -> str:
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"""加载指定 skills 的正文，并整理成上下文块。"""
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sections: list[str] = []
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for name in skill_names:
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content = self.load_published_skill(name)
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if not content:
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continue
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body = strip_frontmatter(content).strip()
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if not body:
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continue
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sections.append(f"## {name}\n\n{body}")
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return "\n\n".join(sections)
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def build_skills_summary(self) -> str:
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"""构建可注入 system prompt 的 skills index。
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虽然函数名还沿用 `summary`，但当前语义是轻量 skills index：
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- 这里只告诉模型“系统里有哪些 skill 可用”
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- 不负责把 skill 正文塞进 system prompt
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- 真正激活的 skill 正文由 resolver/builder 走显式消息注入
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"""
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skills = self.list_skills(filter_unavailable=False)
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if not skills:
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return ""
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lines = ["<skills>"]
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for record in skills:
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frontmatter = record.frontmatter or self.get_skill_metadata(record.name) or {}
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meta_blob = parse_skill_metadata_blob(frontmatter.get("metadata", ""))
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available = check_requirements(meta_blob)
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description = frontmatter.get("description") or record.description or record.name
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lines.append(f' <skill available="{str(available).lower()}">')
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lines.append(f" <name>{escape_xml(record.name)}</name>")
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lines.append(f" <description>{escape_xml(description)}</description>")
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lines.append(f" <version>{escape_xml(record.version)}</version>")
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support_files = self.list_skill_supporting_files(record.name)
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if support_files:
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lines.append(" <supporting_files>")
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for file_path in support_files[:12]:
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lines.append(f" <file>{escape_xml(file_path)}</file>")
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if len(support_files) > 12:
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lines.append(" <file>...additional files omitted...</file>")
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lines.append(" </supporting_files>")
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if not available:
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missing = get_missing_requirements(meta_blob)
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if missing:
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lines.append(f" <requires>{escape_xml(missing)}</requires>")
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lines.append(" </skill>")
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lines.append("</skills>")
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return "\n".join(lines)
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def build_selection_candidates(self) -> list[dict[str, str]]:
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"""构建给 LLM selector 使用的候选 skill 摘要。
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这里刻意保持精简，只给：
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- `name`
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- `description`
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选择器的任务只是“从候选里挑名字”，不是直接阅读完整 skill 正文。
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真正激活后的 skill 正文仍然在后续阶段按需加载。
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"""
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candidates: list[dict[str, str]] = []
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for record in self.list_skills(filter_unavailable=True):
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frontmatter = record.frontmatter or self.get_skill_metadata(record.name) or {}
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description = str(frontmatter.get("description") or record.description or "").strip()
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if not description:
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raw_content = self.load_published_skill(record.name) or ""
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body = strip_frontmatter(raw_content).strip()
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if body:
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description = " ".join(body.splitlines()[:3])[:240].strip()
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candidates.append(
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{
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"name": record.name,
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"description": description or record.name,
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"version": record.version,
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"content_hash": record.content_hash or "",
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}
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)
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return candidates
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def list_skill_supporting_files(self, name: str) -> list[str]:
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"""列出 skill 目录下可按需查看的支持文件相对路径。"""
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record = self._find_record(name)
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if record is None:
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return []
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skill_dir = record.path.parent
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results: list[str] = []
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for subdir in ("references", "templates", "scripts", "assets"):
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root = skill_dir / subdir
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if not root.exists():
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continue
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for file in sorted(root.rglob("*")):
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if file.is_file() and not file.is_symlink():
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results.append(str(file.relative_to(skill_dir)))
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return results
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def view_skill(self, name: str, file_path: str | None = None) -> tuple[str, str] | None:
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"""读取 skill 正文或其支持文件。
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返回 `(display_name, content)`：
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- `display_name` 用于提示当前读取的是 skill 本体还是某个支持文件
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- `content` 为实际文本内容
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"""
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record = self._find_record(name)
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if record is None:
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return None
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if not self._record_available(record):
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frontmatter = record.frontmatter or self.get_skill_metadata(name) or {}
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meta_blob = parse_skill_metadata_blob(frontmatter.get("metadata", ""))
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missing = get_missing_requirements(meta_blob)
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detail = f" Missing requirements: {missing}." if missing else ""
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raise ValueError(f"Skill '{name}' is currently unavailable.{detail}")
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skill_dir = record.path.parent
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if not file_path:
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return ("SKILL.md", self._read_text_file(record.path, display_name="SKILL.md"))
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candidate = (skill_dir / file_path).resolve()
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try:
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candidate.relative_to(skill_dir.resolve())
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except ValueError as exc:
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raise ValueError("Requested skill file must stay within the skill directory") from exc
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if not candidate.exists() or not candidate.is_file():
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raise FileNotFoundError(f"Skill file '{file_path}' does not exist")
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display_name = str(candidate.relative_to(skill_dir))
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return (display_name, self._read_text_file(candidate, display_name=display_name))
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def get_always_skills(self) -> list[str]:
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"""返回标记为 always 的可用 skill 名称。"""
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result: list[str] = []
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for record in self.list_skills(filter_unavailable=True):
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frontmatter = record.frontmatter or self.get_skill_metadata(record.name) or {}
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meta_blob = parse_skill_metadata_blob(frontmatter.get("metadata", ""))
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if meta_blob.get("always") or str(frontmatter.get("always", "")).lower() == "true":
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result.append(record.name)
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return result
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@staticmethod
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def _coerce_tool_names(value: Any) -> list[str]:
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if value is None:
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return []
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if isinstance(value, str):
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raw = value.strip()
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if not raw:
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return []
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if raw.startswith("["):
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try:
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parsed = json.loads(raw)
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except Exception:
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parsed = None
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if isinstance(parsed, list):
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return [str(item).strip() for item in parsed if str(item).strip()]
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return [item.strip() for item in raw.split(",") if item.strip()]
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if isinstance(value, (list, tuple, set)):
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return [str(item).strip() for item in value if str(item).strip()]
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return []
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def _find_record(self, name: str) -> SkillRecord | None:
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for record in self.list_skills(filter_unavailable=False, include_internal=True):
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if record.name == name:
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return record
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return None
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@staticmethod
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def _record_internal(record: SkillRecord) -> bool:
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return _truthy((record.frontmatter or {}).get("internal"))
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def _record_available(self, record: SkillRecord) -> bool:
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content = record.path.read_text(encoding="utf-8")
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frontmatter, _ = parse_frontmatter(content)
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meta_blob = parse_skill_metadata_blob(frontmatter.get("metadata", ""))
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return check_requirements(meta_blob)
|
||
|
||
@staticmethod
|
||
def _read_text_file(path: Path, *, display_name: str) -> str:
|
||
try:
|
||
return path.read_text(encoding="utf-8")
|
||
except UnicodeDecodeError as exc:
|
||
raise ValueError(
|
||
f"Skill file '{display_name}' is not UTF-8 text and cannot be viewed with skill_view."
|
||
) from exc
|
||
|
||
def _skill_available(self, name: str) -> bool:
|
||
record = self._find_record(name)
|
||
if record is None:
|
||
return False
|
||
return self._record_available(record)
|
||
|
||
|
||
def summarize_body(body: str) -> str:
|
||
cleaned = " ".join(line.strip() for line in body.splitlines()[:3] if line.strip()).strip()
|
||
return cleaned[:240]
|
||
|
||
|
||
def _truthy(value: Any) -> bool:
|
||
if isinstance(value, bool):
|
||
return value
|
||
return str(value or "").strip().lower() in {"1", "true", "yes", "y", "on"}
|