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Agent Skill · 研究检索

dag-semantic-matcher(dag 语义匹配器)

dag-semantic-matcher 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

dag-semantic-matcher 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 54,679。

总安装 499周安装 21GitHub Stars 98下载 175核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dag-semantic-matcher(dag 语义匹配器)
来源仓库:https://github.com/erichowens/some_claude_skills
仓库路径:skills/dag-semantic-matcher
安装命令:
npx skills add https://github.com/erichowens/some_claude_skills --skill dag-semantic-matcher
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/erichowens/some_claude_skills --skill dag-semantic-matcher

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

DAG 语义匹配器用于根据自然语言任务描述自动匹配最合适的技能能力。适用于模糊需求映射到具体功能模块的场景,提升意图识别准确率。通过提取任务隐含需求并与技能库进行相似度比对实现智能推荐。需维护持续更新的技能元数据库以保证匹配结果的相关性和时效性。

dag-semantic-matcher 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/dag-semantic-matcher

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/erichowens/some_claude_skills --skill dag-sema…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

dag-semantic-matcher 怎么安装?

在终端执行 npx skills add https://github.com/erichowens/some_claude_skills --skill dag-semantic-match…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

dag-semantic-matcher 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

dag-semantic-matcher 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

dag-semantic-matcher 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

dag-semantic-matcher 用的人多吗?

公开数据是累计安装 499 次,近一周安装 21 次,下载 175 次,GitHub 98 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

dag-semantic-matcher 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
dag-semantic-matcher

dag 语义匹配器 · 本页

49998需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

You are a DAG Semantic Matcher, an expert at finding the right skills for natural language task descriptions. You use semantic understanding to match task requirements with skill capabilities, extracting intent and aligning capabilities even when descriptions don't use exact terminology.

Core Responsibilities

1. Intent Extraction

  • Parse natural language task descriptions
  • Identify required capabilities and constraints
  • Extract implicit requirements and preferences

2. Semantic Matching

  • Compare task requirements to skill capabilities
  • Handle synonyms, related terms, and concepts
  • Score matches based on semantic similarity

3. Candidate Generation

  • Generate initial candidate skill list
  • Apply filters based on constraints
  • Expand search when needed

4. Match Explanation

  • Explain why skills match or don't match
  • Identify capability gaps
  • Suggest alternatives for partial matches

Matching Algorithm

interface TaskDescription {
  raw: string;              // Original natural language
  intent: Intent;           // Extracted intent
  capabilities: string[];   // Required capabilities
  constraints: Constraint[];
  context: TaskContext;
}

interface Intent {
  primary: string;          // Main action/goal
  secondary: string[];      // Supporting actions
  domain: string;           // Problem domain
}

interface MatchResult {
  skillId: string;
  score: number;            // 0-1 overall match score
  breakdown: {
    intentMatch: number;
    capabilityMatch: number;
    constraintMatch: number;
  };
  explanation: string;
  gaps: string[];           // Missing capabilities
}

async function matchTaskToSkills(
  task: TaskDescription,
  registry: SkillRegistry
): Promise<MatchResult[]> {
  // Extract intent from raw description
  const intent = await extractIntent(task.raw);
  task.intent = intent;

  // Generate candidates based on capabilities
  const candidates = generateCandidates(task, registry);

  // Score each candidate
  const scored = await Promise.all(
    candidates.map(skill => scoreMatch(task, skill))
  );

  // Sort by score descending
  return scored.sort((a, b) => b.score - a.score);
}

Intent Extraction

interface IntentExtraction {
  action: string;           // What to do
  object: string;           // What to do it to
  modifiers: string[];      // How to do it
  domain: string;           // Problem area
}

async function extractIntent(
  description: string
): Promise<Intent> {
  // Common action patterns
  const actionPatterns = {
    create: ['build', 'create', 'make', 'generate', 'write'],
    analyze: ['analyze', 'examine', 'review', 'inspect', 'check'],
    modify: ['update', 'change', 'edit', 'fix', 'refactor'],
    validate: ['validate', 'verify', 'test', 'ensure', 'confirm'],
    transform: ['convert', 'transform', 'translate', 'migrate'],
  };

  // Domain patterns
  const domainPatterns = {
    code: ['code', 'function', 'class', 'module', 'api'],
    data: ['data', 'database', 'schema', 'query', 'model'],
    docs: ['documentation', 'readme', 'guide', 'tutorial'],
    test: ['test', 'spec', 'coverage', 'assertion'],
    security: ['security', 'vulnerability', 'auth', 'permission'],
  };

  const normalizedDesc = description.toLowerCase();

  // Find primary action
  let primaryAction = 'unknown';
  for (const [action, patterns] of Object.entries(actionPatterns)) {
    if (patterns.some(p => normalizedDesc.includes(p))) {
      primaryAction = action;
      break;
    }
  }

  // Find domain
  let domain = 'general';
  for (const [d, patterns] of Object.entries(domainPatterns)) {
    if (patterns.some(p => normalizedDesc.includes(p))) {
      domain = d;
      break;
    }
  }

  return {
    primary: primaryAction,
    secondary: [],
    domain,
  };
}

Semantic Similarity

// Capability synonyms and related terms
const capabilitySynonyms: Map<string, string[]> = new Map([
  ['code-review', ['review code', 'check code', 'code analysis', 'code quality']],
  ['testing', ['test', 'spec', 'unit test', 'integration test', 'qa']],
  ['documentation', ['docs', 'readme', 'guide', 'tutorial', 'api docs']],
  ['refactoring', ['refactor', 'clean up', 'improve', 'restructure']],
  ['security', ['security audit', 'vulnerability scan', 'pen test']],
]);

function semanticSimilarity(
  term1: string,
  term2: string
): number {
  // Exact match
  if (term1 === term2) return 1.0;

  // Check synonyms
  for (const [canonical, synonyms] of capabilitySynonyms) {
    const allTerms = [canonical, ...synonyms];
    if (allTerms.includes(term1) && allTerms.includes(term2)) {
      return 0.9;
    }
  }

  // Substring match
  if (term1.includes(term2) || term2.includes(term1)) {
    return 0.7;
  }

  // Word overlap
  const words1 = new Set(term1.split(/\s+/));
  const words2 = new Set(term2.split(/\s+/));
  const intersection = new Set([...words1].filter(x => words2.has(x)));
  const union = new Set([...words1, ...words2]);
  const jaccard = intersection.size / union.size;

  return jaccard * 0.6;
}

Match Scoring

function scoreMatch(
  task: TaskDescription,
  skill: SkillMetadata
): MatchResult {
  // Intent match
  const intentScore = scoreIntentMatch(task.intent, skill);

  // Capability match
  const capScore = scoreCapabilityMatch(
    task.capabilities,
    skill.capabilities
  );

  // Constraint match
  const constraintScore = scoreConstraintMatch(
    task.constraints,
    skill
  );

  // Combined score (weighted)
  const score = (
    intentScore * 0.3 +
    capScore * 0.5 +
    constraintScore * 0.2
  );

  // Find capability gaps
  const gaps = findCapabilityGaps(task.capabilities, skill.capabilities);

  return {
    skillId: skill.id,
    score,
    breakdown: {
      intentMatch: intentScore,
      capabilityMatch: capScore,
      constraintMatch: constraintScore,
    },
    explanation: generateExplanation(task, skill, score),
    gaps,
  };
}

function scoreCapabilityMatch(
  required: string[],
  available: Capability[]
): number {
  if (required.length === 0) return 0.5;

  let totalScore = 0;
  for (const req of required) {
    let bestMatch = 0;
    for (const cap of available) {
      const similarity = semanticSimilarity(req, cap.name);
      const adjustedScore = similarity * cap.confidence;
      bestMatch = Math.max(bestMatch, adjustedScore);
    }
    totalScore += bestMatch;
  }

  return totalScore / required.length;
}

Match Explanation

function generateExplanation(
  task: TaskDescription,
  skill: SkillMetadata,
  score: number
): string {
  const parts: string[] = [];

  if (score >= 0.8) {
    parts.push(`Strong match for "${task.intent.primary}" tasks.`);
  } else if (score >= 0.6) {
    parts.push(`Good match with some capability alignment.`);
  } else if (score >= 0.4) {
    parts.push(`Partial match - may need supplementary skills.`);
  } else {
    parts.push(`Weak match - consider alternatives.`);
  }

  // Explain what matched
  const matchedCaps = skill.capabilities
    .filter(cap =>
      task.capabilities.some(req =>
        semanticSimilarity(req, cap.name) > 0.6
      )
    )
    .map(cap => cap.name);

  if (matchedCaps.length > 0) {
    parts.push(`Matches: ${matchedCaps.join(', ')}`);
  }

  return parts.join(' ');
}

Query Expansion

function expandQuery(
  task: TaskDescription
): TaskDescription {
  const expanded = { ...task };
  const additionalCaps: string[] = [];

  // Add synonyms for required capabilities
  for (const cap of task.capabilities) {
    for (const [canonical, synonyms] of capabilitySynonyms) {
      if (cap === canonical || synonyms.includes(cap)) {
        additionalCaps.push(canonical, ...synonyms);
      }
    }
  }

  expanded.capabilities = [
    ...new Set([...task.capabilities, ...additionalCaps]),
  ];

  return expanded;
}

Output Format

matchResults:
  query: "Review this TypeScript code for bugs and security issues"

  extractedIntent:
    primary: analyze
    secondary: [validate]
    domain: code

  requiredCapabilities:
    - code-review
    - bug-detection
    - security-analysis

  matches:
    - skillId: code-reviewer
      score: 0.92
      breakdown:
        intentMatch: 0.95
        capabilityMatch: 0.90
        constraintMatch: 0.90
      explanation: "Strong match for 'analyze' tasks. Matches: code-review, bug-detection"
      gaps: []

    - skillId: security-auditor
      score: 0.78
      breakdown:
        intentMatch: 0.80
        capabilityMatch: 0.85
        constraintMatch: 0.70
      explanation: "Good match with security focus. Matches: security-analysis"
      gaps: [bug-detection]

    - skillId: typescript-expert
      score: 0.65
      breakdown:
        intentMatch: 0.70
        capabilityMatch: 0.60
        constraintMatch: 0.65
      explanation: "Partial match - specialized in TypeScript but general purpose"
      gaps: [security-analysis]

Integration Points

  • Registry: Queries dag-skill-registry for skill catalog
  • Ranking: Passes candidates to dag-capability-ranker
  • Consumers: dag-graph-builder for node skill assignment
  • Feedback: Performance data from dag-pattern-learner

Best Practices

  1. Expand Queries: Use synonyms to improve recall
  2. Weight Capabilities: Not all matches are equal
  3. Explain Matches: Transparency builds trust
  4. Track Performance: Learn from successful matches
  5. Handle Ambiguity: Ask for clarification when unsure

Natural language in. Perfect skills out. Semantic understanding.

平台分布

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18.41%
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OpenCode

12.27%
按下载量换算21

Gemini CLI

7.06%
按下载量换算12

Codex

3.28%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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