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enhance-orchestrator增强协调器

Agent Skill

enhance-orchestrator 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。

总安装

1,011

周安装

43

GitHub Stars

769

下载量

354
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:enhance-orchestrator(增强协调器)
来源仓库:https://github.com/avifenesh/agentsys
仓库路径:skills/enhance-orchestrator
安装命令:
npx skills add https://github.com/avifenesh/agentsys --skill enhance-orchestrator
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/avifenesh/agentsys --skill enhance-orchestrator

简介

用于并行协调多个增强分析器,生成统一的质量评估报告。

  • 适合在批量检查提示词、技能定义或文档结构时使用,提升效率与一致性。
  • 必须并行执行所有分析器,仅报告高确定性问题,并按优先级排序输出。
  • 禁止对中低置信度问题自动修复,需用户显式授权后方可应用更改。
  • enhance-orchestrator 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

enhance-orchestrator

Coordinate all enhancement analyzers in parallel and produce a unified report.

Critical Rules

  1. MUST run enhancers in parallel - Use Promise.all for efficiency
  2. MUST only run enhancers for existing content - Skip if no files found
  3. MUST report HIGH certainty first - Priority order: HIGH → MEDIUM → LOW
  4. NEVER auto-fix without --apply flag - Explicit consent required
  5. NEVER auto-fix MEDIUM or LOW issues - Only HIGH certainty

Workflow

Phase 1: Parse Arguments

const args = '$ARGUMENTS'.split(' ').filter(Boolean);
const targetPath = args.find(a => !a.startsWith('--')) || '.';

const flags = {
  apply: args.includes('--apply'),
  focus: args.find(a => a.startsWith('--focus='))?.split('=')[1],
  verbose: args.includes('--verbose'),
  showSuppressed: args.includes('--show-suppressed'),
  resetLearned: args.includes('--reset-learned'),
  noLearn: args.includes('--no-learn'),
  exportLearned: args.includes('--export-learned')
};

// Validate focus type
const VALID_FOCUS = ['plugin', 'agent', 'claudemd', 'claude-memory', 'docs', 'prompt', 'hooks', 'skills', 'cross-file'];
if (flags.focus && !VALID_FOCUS.includes(flags.focus)) {
  console.error(`Invalid --focus: "${flags.focus}". Valid: ${VALID_FOCUS.join(', ')}`);
  return;
}

Phase 2: Discovery

Detect what exists in target path:

const discovery = {
  plugins: await Glob({ pattern: 'plugins/*/.claude-plugin/plugin.json', path: targetPath }),
  agents: await Glob({ pattern: '**/agents/*.md', path: targetPath }),
  claudemd: await Glob({ pattern: '**/CLAUDE.md', path: targetPath }) ||
            await Glob({ pattern: '**/AGENTS.md', path: targetPath }),
  docs: await Glob({ pattern: 'docs/**/*.md', path: targetPath }),
  prompts: await Glob({ pattern: '**/prompts/**/*.md', path: targetPath }) ||
           await Glob({ pattern: '**/commands/**/*.md', path: targetPath }),
  hooks: await Glob({ pattern: '**/hooks/**/*.md', path: targetPath }),
  skills: await Glob({ pattern: '**/skills/**/SKILL.md', path: targetPath }),
  // Cross-file runs if agents OR skills exist (analyzes relationships)
  'cross-file': discovery.agents?.length || discovery.skills?.length ? ['enabled'] : []
};

Phase 3: Load Suppressions

// Use relative path from skill directory to plugin lib
// Path: skills/enhance-orchestrator/ -> ../../lib/
const { getSuppressionPath } = require('../../lib/cross-platform');
const { loadAutoSuppressions, getProjectId, clearAutoSuppressions } = require('../../lib/enhance/auto-suppression');

const suppressionPath = getSuppressionPath();
const projectId = getProjectId(targetPath);

if (flags.resetLearned) {
  clearAutoSuppressions(suppressionPath, projectId);
  console.log(`Cleared suppressions for project: ${projectId}`);
}

const autoLearned = loadAutoSuppressions(suppressionPath, projectId);

Phase 4: Launch Enhancers in Parallel

CRITICAL: MUST spawn these EXACT agents using Task(). Do NOT use Explore or other agents.

Focus TypeAgent to SpawnModelJS Analyzer
pluginplugin-enhancersonnetlib/enhance/plugin-analyzer.js
agentagent-enhanceropuslib/enhance/agent-analyzer.js
claudemdclaudemd-enhanceropuslib/enhance/projectmemory-analyzer.js
docsdocs-enhanceropuslib/enhance/docs-analyzer.js
promptprompt-enhanceropuslib/enhance/prompt-analyzer.js
hookshooks-enhanceropuslib/enhance/hook-analyzer.js
skillsskills-enhanceropuslib/enhance/skill-analyzer.js
cross-filecross-file-enhancersonnetlib/enhance/cross-file-analyzer.js

Each agent has Bash(node:*) to run its JS analyzer. Do NOT substitute with Explore agents.

// EXACT agent mapping - do not change
const ENHANCER_AGENTS = {
  plugin: 'plugin-enhancer',
  agent: 'agent-enhancer',
  claudemd: 'claudemd-enhancer',
  docs: 'docs-enhancer',
  prompt: 'prompt-enhancer',
  hooks: 'hooks-enhancer',
  skills: 'skills-enhancer',
  'cross-file': 'cross-file-enhancer'
};

const promises = [];

for (const [type, agentType] of Object.entries(ENHANCER_AGENTS)) {
  if (focus && focus !== type) continue;
  if (!discovery[type]?.length) continue;

  // MUST use exact subagent_type - these agents have Bash(node:*) to run JS analyzers
  promises.push(Task({
    subagent_type: agentType,
    prompt: `Analyze ${type} in ${targetPath}.
MUST use Skill tool to invoke your enhance-* skill.
The skill runs the JavaScript analyzer and returns structured findings.
verbose: ${flags.verbose}
Return JSON: { "enhancerType": "${type}", "findings": [...], "summary": { high, medium, low } }`
  }));
}

// MUST use Promise.all for parallel execution
const results = await Promise.all(promises);

Phase 5: Aggregate Results

function aggregateResults(enhancerResults) {
  const findings = [];
  const byEnhancer = {};

  for (const result of enhancerResults) {
    if (!result?.findings) continue;
    for (const finding of result.findings) {
      findings.push({ ...finding, source: result.enhancerType });
    }
    byEnhancer[result.enhancerType] = result.summary;
  }

  return {
    findings,
    byEnhancer,
    totals: {
      high: findings.filter(f => f.certainty === 'HIGH').length,
      medium: findings.filter(f => f.certainty === 'MEDIUM').length,
      low: findings.filter(f => f.certainty === 'LOW').length
    }
  };
}

Phase 6: Generate Report

Generate report directly from aggregated findings:

const { generateReport } = require('../../lib/enhance/reporter');

const report = generateReport(aggregated, {
  verbose: flags.verbose,
  showAutoFixable: flags.apply
});

console.log(report);

Phase 7: Auto-Learning

if (!flags.noLearn) {
  const { analyzeForAutoSuppression, saveAutoSuppressions } = require('../../lib/enhance/auto-suppression');

  const newSuppressions = analyzeForAutoSuppression(aggregated.findings, fileContents, { projectRoot: targetPath });

  if (newSuppressions.length > 0) {
    saveAutoSuppressions(suppressionPath, projectId, newSuppressions);
    console.log(`\nLearned ${newSuppressions.length} new suppressions.`);
  }
}

Phase 8: Apply Fixes

if (flags.apply) {
  const autoFixable = aggregated.findings.filter(f => f.certainty === 'HIGH' && f.autoFixable);

  if (autoFixable.length > 0) {
    console.log(`\n## Applying ${autoFixable.length} Auto-Fixes\n`);

    const byEnhancer = {};
    for (const fix of autoFixable) {
      const type = fix.source;
      if (!byEnhancer[type]) byEnhancer[type] = [];
      byEnhancer[type].push(fix);
    }

    for (const [type, fixes] of Object.entries(byEnhancer)) {
      await Task({
        subagent_type: enhancerAgents[type],
        prompt: `Apply HIGH certainty fixes: ${JSON.stringify(fixes, null, 2)}`
      });
    }

    console.log(`Applied ${autoFixable.length} fixes.`);
  }
}

Output Format

# Enhancement Analysis Report

**Target**: {targetPath}
**Date**: {timestamp}
**Enhancers Run**: {list}

## Executive Summary

| Enhancer | HIGH | MEDIUM | LOW | Auto-Fixable |
|----------|------|--------|-----|--------------|
| plugin   | 2    | 3      | 1   | 1            |
| agent    | 1    | 2      | 0   | 1            |
| **Total**| **3**| **5**  | **1**| **2**       |

## HIGH Certainty Issues
[Grouped by enhancer, then file]

## MEDIUM Certainty Issues
[...]

## Auto-Fix Summary
{n} issues can be fixed with `--apply` flag.

Constraints

  • MUST run enhancers in parallel (Promise.all)
  • MUST skip enhancers for missing content types
  • MUST report HIGH certainty issues first
  • MUST deduplicate findings across enhancers
  • NEVER auto-fix without explicit --apply flag
  • NEVER auto-fix MEDIUM or LOW certainty issues

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.1%
按下载量换算131

Claude

28.46%
按下载量换算101

Cursor

17.63%
按下载量换算62

Gemini CLI

9.96%
按下载量换算35

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/avifenesh/agentsys --skill enhance-orchestrator 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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