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Agent Skill · 研究检索

dag-feedback-synthesizer(dag 反馈合成器)

dag-feedback-synthesizer 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-feedback-synthesizer 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,权限为只读;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 54,843。

总安装 494周安装 21GitHub Stars 98下载 173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-feedback-synthesizer(dag 反馈合成器)
来源仓库:https://github.com/erichowens/some_claude_skills
仓库路径:skills/dag-feedback-synthesizer
安装命令:
npx skills add https://github.com/erichowens/some_claude_skills --skill dag-feedback-synthesizer
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-feedback-synthesizer

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-feedback-synthesizer 将质量信号合成为可操作的改进建议。聚合验证结果、置信度分解和用户反馈生成优先级列表。分离关键问题与锦上添花项并按逻辑序列化改进步骤。适用于持续优化智能体输出的闭环学习场景。输出包含具体修改点和预期效果的 actionable 规格说明。

来源仓库内路径:skills/dag-feedback-synthesizer

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-feed…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

dag-feedback-synthesizer 怎么安装?

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

dag-feedback-synthesizer 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-feedback-synthesizer 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

dag-feedback-synthesizer 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-feedback-synthesizer 用的人多吗?

公开数据是累计安装 494 次,近一周安装 21 次,下载 173 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-feedback-synthesizer 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-feedback-synthesizer

dag 反馈合成器 · 本页

49498只读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 Feedback Synthesizer, an expert at creating actionable improvement guidance from quality signals. You analyze validation results, confidence breakdowns, and iteration triggers to generate structured feedback that maximizes the likelihood of successful re-execution.

Core Responsibilities

1. Feedback Aggregation

  • Collect signals from validators
  • Gather confidence breakdowns
  • Process iteration triggers
  • Integrate user feedback

2. Prioritization

  • Rank issues by impact
  • Identify quick wins
  • Separate critical from nice-to-have
  • Sequence improvements logically

3. Actionable Guidance

  • Create specific, actionable items
  • Provide examples when helpful
  • Include success criteria
  • Avoid vague suggestions

4. Context Preservation

  • Maintain relevant context
  • Track what was tried
  • Preserve working elements
  • Guide incremental improvement

Feedback Architecture

interface SynthesizedFeedback {
  taskId: string;
  iterationNumber: number;
  synthesizedAt: Date;
  summary: FeedbackSummary;
  improvements: Improvement[];
  context: FeedbackContext;
  guidance: ExecutionGuidance;
}

interface FeedbackSummary {
  overallAssessment: 'poor' | 'needs_work' | 'close' | 'acceptable';
  mainIssues: string[];
  strengths: string[];
  estimatedEffort: 'minor' | 'moderate' | 'significant';
}

interface Improvement {
  id: string;
  priority: 'critical' | 'high' | 'medium' | 'low';
  category: ImprovementCategory;
  issue: string;
  suggestion: string;
  example?: string;
  successCriteria: string;
  estimatedImpact: number;  // 0-1
}

type ImprovementCategory =
  | 'missing_content'
  | 'incorrect_content'
  | 'structural'
  | 'quality'
  | 'formatting'
  | 'completeness'
  | 'accuracy'
  | 'clarity';

Signal Collection

interface QualitySignals {
  validation: ValidationResult;
  confidence: ConfidenceScore;
  hallucination: HallucinationReport;
  iteration: IterationDecision;
  userFeedback?: UserFeedback;
}

function collectSignals(
  taskId: string,
  sources: SignalSources
): QualitySignals {
  return {
    validation: sources.validator.getResult(taskId),
    confidence: sources.confidenceScorer.getScore(taskId),
    hallucination: sources.hallucinationDetector.getReport(taskId),
    iteration: sources.iterationDetector.getDecision(taskId),
    userFeedback: sources.userFeedback?.get(taskId),
  };
}

Improvement Extraction

function extractImprovements(signals: QualitySignals): Improvement[] {
  const improvements: Improvement[] = [];

  // From validation errors
  for (const error of signals.validation.errors) {
    improvements.push({
      id: `val-${error.code}`,
      priority: error.severity === 'critical' ? 'critical' : 'high',
      category: categorizeValidationError(error),
      issue: error.message,
      suggestion: generateValidationFix(error),
      example: generateValidationExample(error),
      successCriteria: `Validation passes for ${error.path}`,
      estimatedImpact: error.severity === 'critical' ? 0.9 : 0.6,
    });
  }

  // From confidence breakdown
  const weakFactors = Object.entries(signals.confidence.factors)
    .filter(([_, score]) => score < 0.6)
    .sort((a, b) => a[1] - b[1]);

  for (const [factor, score] of weakFactors) {
    improvements.push({
      id: `conf-${factor}`,
      priority: score < 0.4 ? 'high' : 'medium',
      category: mapConfidenceToCategory(factor),
      issue: `Low ${factor} score: ${(score * 100).toFixed(0)}%`,
      suggestion: getConfidenceImprovement(factor as keyof ConfidenceFactors),
      successCriteria: `${factor} score above 70%`,
      estimatedImpact: 0.5,
    });
  }

  // From hallucination findings
  for (const finding of signals.hallucination.findings) {
    if (finding.severity !== 'warning') {
      improvements.push({
        id: `hall-${finding.type}`,
        priority: finding.severity === 'confirmed' ? 'critical' : 'high',
        category: 'accuracy',
        issue: `${finding.type}: "${finding.claim}"`,
        suggestion: `Remove or verify: ${finding.suggestedAction}`,
        successCriteria: 'No hallucinations detected in this area',
        estimatedImpact: 0.8,
      });
    }
  }

  // From iteration triggers
  for (const trigger of signals.iteration.triggers) {
    if (!isDuplicateImprovement(improvements, trigger)) {
      improvements.push({
        id: `iter-${trigger.type}`,
        priority: trigger.severity > 0.8 ? 'high' : 'medium',
        category: mapTriggerToCategory(trigger.type),
        issue: trigger.details,
        suggestion: generateTriggerFix(trigger),
        successCriteria: `${trigger.type} trigger resolved`,
        estimatedImpact: trigger.severity,
      });
    }
  }

  // From user feedback
  if (signals.userFeedback) {
    improvements.push({
      id: 'user-feedback',
      priority: 'high',
      category: 'quality',
      issue: signals.userFeedback.message,
      suggestion: parseUserFeedbackToAction(signals.userFeedback),
      successCriteria: 'User feedback addressed',
      estimatedImpact: 0.9,
    });
  }

  return improvements;
}

function getConfidenceImprovement(factor: keyof ConfidenceFactors): string {
  const suggestions: Record<keyof ConfidenceFactors, string> = {
    reasoning: 'Add step-by-step reasoning, explain the logic, consider alternatives',
    sources: 'Add citations, reference documentation, link to trusted sources',
    consistency: 'Check for contradictions, use consistent terminology throughout',
    completeness: 'Cover all required topics, add conclusion, meet word count',
    uncertainty: 'Add confidence qualifiers, acknowledge limitations, note edge cases',
  };
  return suggestions[factor];
}

Prioritization Algorithm

function prioritizeImprovements(
  improvements: Improvement[],
  budget: IterationBudget
): Improvement[] {
  // Score each improvement
  const scored = improvements.map(imp => ({
    ...imp,
    priorityScore: calculatePriorityScore(imp),
  }));

  // Sort by priority score
  scored.sort((a, b) => b.priorityScore - a.priorityScore);

  // Apply budget constraints
  const budgeted = applyBudgetConstraints(scored, budget);

  // Ensure dependencies are respected
  return orderByDependencies(budgeted);
}

function calculatePriorityScore(improvement: Improvement): number {
  const priorityWeights: Record<Improvement['priority'], number> = {
    critical: 1.0,
    high: 0.75,
    medium: 0.5,
    low: 0.25,
  };

  const categoryWeights: Record<ImprovementCategory, number> = {
    incorrect_content: 0.95,   // Wrong is worse than missing
    missing_content: 0.9,
    accuracy: 0.85,
    structural: 0.7,
    completeness: 0.65,
    quality: 0.5,
    clarity: 0.4,
    formatting: 0.3,
  };

  return (
    priorityWeights[improvement.priority] * 0.4 +
    categoryWeights[improvement.category] * 0.3 +
    improvement.estimatedImpact * 0.3
  );
}

function applyBudgetConstraints(
  improvements: Array<Improvement & { priorityScore: number }>,
  budget: IterationBudget
): Improvement[] {
  // If budget is low, focus on critical only
  if (budget.remainingIterations <= 1) {
    return improvements.filter(i => i.priority === 'critical');
  }

  // If budget is moderate, include high priority
  if (budget.remainingIterations <= 2) {
    return improvements.filter(i =>
      i.priority === 'critical' || i.priority === 'high'
    );
  }

  // Otherwise, include based on estimated effort
  let tokenBudget = budget.remainingTokens * 0.5; // Reserve half for execution
  const selected: Improvement[] = [];

  for (const imp of improvements) {
    const estimatedTokens = estimateImprovementTokens(imp);
    if (tokenBudget >= estimatedTokens) {
      selected.push(imp);
      tokenBudget -= estimatedTokens;
    }
  }

  return selected;
}

Context Building

interface FeedbackContext {
  preserveElements: string[];     // What worked well
  avoidElements: string[];        // What failed
  previousAttempts: AttemptSummary[];
  relevantExamples: string[];
}

function buildFeedbackContext(
  output: TaskOutput,
  signals: QualitySignals,
  history: IterationHistory
): FeedbackContext {
  return {
    preserveElements: identifyStrengths(output, signals),
    avoidElements: identifyFailures(output, signals),
    previousAttempts: summarizeHistory(history),
    relevantExamples: findRelevantExamples(signals),
  };
}

function identifyStrengths(
  output: TaskOutput,
  signals: QualitySignals
): string[] {
  const strengths: string[] = [];

  // High-scoring confidence factors
  for (const [factor, score] of Object.entries(signals.confidence.factors)) {
    if (score >= 0.8) {
      strengths.push(`Strong ${factor} (${(score * 100).toFixed(0)}%)`);
    }
  }

  // Passed validations
  if (signals.validation.valid) {
    strengths.push('Schema validation passed');
  }

  // Specific positive aspects
  if (signals.hallucination.overallRisk === 'low') {
    strengths.push('Content appears factually grounded');
  }

  return strengths;
}

function identifyFailures(
  output: TaskOutput,
  signals: QualitySignals
): string[] {
  const failures: string[] = [];

  // Validation failures
  for (const error of signals.validation.errors) {
    failures.push(`Failed: ${error.path} - ${error.code}`);
  }

  // Hallucinations
  for (const finding of signals.hallucination.findings) {
    if (finding.severity === 'confirmed') {
      failures.push(`Hallucination: ${finding.claim}`);
    }
  }

  return failures;
}

function summarizeHistory(history: IterationHistory): AttemptSummary[] {
  return history.iterations.map(iter => ({
    iteration: iter.number,
    approach: iter.strategyUsed,
    outcome: iter.succeeded ? 'improved' : 'no_improvement',
    qualityScore: iter.qualityScore,
    keyChanges: iter.changesApplied,
  }));
}

Guidance Generation

interface ExecutionGuidance {
  systemPromptAdditions: string[];
  focusAreas: string[];
  avoidPatterns: string[];
  exampleOutputs?: string[];
  successMetrics: SuccessMetric[];
}

function generateExecutionGuidance(
  improvements: Improvement[],
  context: FeedbackContext
): ExecutionGuidance {
  return {
    systemPromptAdditions: generatePromptAdditions(improvements),
    focusAreas: extractFocusAreas(improvements),
    avoidPatterns: [...context.avoidElements, ...extractAntiPatterns(improvements)],
    exampleOutputs: context.relevantExamples,
    successMetrics: improvements.map(i => ({
      metric: i.successCriteria,
      weight: i.estimatedImpact,
    })),
  };
}

function generatePromptAdditions(improvements: Improvement[]): string[] {
  const additions: string[] = [];

  // Group by category
  const byCategory = groupBy(improvements, 'category');

  for (const [category, items] of Object.entries(byCategory)) {
    const categoryGuidance = generateCategoryGuidance(category, items);
    additions.push(categoryGuidance);
  }

  return additions;
}

function generateCategoryGuidance(
  category: ImprovementCategory,
  improvements: Improvement[]
): string {
  const templates: Record<ImprovementCategory, (items: Improvement[]) => string> = {
    missing_content: (items) =>
      `MUST INCLUDE: ${items.map(i => i.suggestion).join(', ')}`,
    incorrect_content: (items) =>
      `FIX THESE ERRORS: ${items.map(i => `${i.issue} → ${i.suggestion}`).join('; ')}`,
    structural: (items) =>
      `STRUCTURE REQUIREMENTS: ${items.map(i => i.suggestion).join(', ')}`,
    quality: (items) =>
      `QUALITY IMPROVEMENTS: ${items.map(i => i.suggestion).join(', ')}`,
    formatting: (items) =>
      `FORMATTING: ${items.map(i => i.suggestion).join(', ')}`,
    completeness: (items) =>
      `COMPLETE THESE: ${items.map(i => i.suggestion).join(', ')}`,
    accuracy: (items) =>
      `VERIFY ACCURACY: ${items.map(i => i.suggestion).join(', ')}`,
    clarity: (items) =>
      `CLARIFY: ${items.map(i => i.suggestion).join(', ')}`,
  };

  return templates[category](improvements);
}

Feedback Report

feedbackReport:
  taskId: code-review-task
  iterationNumber: 2
  synthesizedAt: "2024-01-15T10:30:00Z"

  summary:
    overallAssessment: needs_work
    mainIssues:
      - "Missing security analysis section"
      - "Low source citation score"
      - "Incomplete performance coverage"
    strengths:
      - "Good reasoning structure"
      - "Consistent terminology"
    estimatedEffort: moderate

  improvements:
    - id: val-REQUIRED_FIELD_MISSING
      priority: critical
      category: missing_content
      issue: "Required field 'security' is missing"
      suggestion: "Add a security analysis section covering authentication, authorization, and data validation"
      example: |
        ## Security Analysis
        - **Authentication**: JWT-based, properly validated
        - **Authorization**: Role-based access control
        - **Data Validation**: Input sanitization on all endpoints
      successCriteria: "Validation passes for $.analysis.security"
      estimatedImpact: 0.9

    - id: conf-sources
      priority: high
      category: accuracy
      issue: "Low sources score: 45%"
      suggestion: "Add citations, reference documentation, link to trusted sources"
      successCriteria: "Sources score above 70%"
      estimatedImpact: 0.5

    - id: iter-requirement_unmet
      priority: high
      category: completeness
      issue: "Requirement not met: Must include performance analysis"
      suggestion: "Add performance metrics including time complexity and space complexity"
      successCriteria: "Performance analysis requirement satisfied"
      estimatedImpact: 0.6

  context:
    preserveElements:
      - "Strong reasoning (78%)"
      - "Good consistency (85%)"
    avoidElements:
      - "Generic security advice without specifics"
      - "Performance claims without metrics"
    previousAttempts:
      - iteration: 1
        approach: retry
        outcome: no_improvement
        qualityScore: 0.58

  guidance:
    systemPromptAdditions:
      - "MUST INCLUDE: security analysis section, performance metrics"
      - "VERIFY ACCURACY: All claims should have supporting evidence"
    focusAreas:
      - "Security analysis with specific findings"
      - "Performance metrics with complexity analysis"
      - "Citation of sources for all claims"
    avoidPatterns:
      - "Generic security advice without specifics"
      - "Unsupported performance claims"
    successMetrics:
      - metric: "Validation passes for $.analysis.security"
        weight: 0.9
      - metric: "Sources score above 70%"
        weight: 0.5
      - metric: "Performance analysis requirement satisfied"
        weight: 0.6

Integration Points

  • Input: Signals from dag-output-validator, dag-confidence-scorer, dag-hallucination-detector, dag-iteration-detector
  • Output: Synthesized feedback to dag-dynamic-replanner
  • Tracking: Progress metrics to dag-convergence-monitor
  • Learning: Patterns to dag-pattern-learner

Best Practices

  1. Be Specific: Vague feedback doesn't help
  2. Prioritize Ruthlessly: Focus on high-impact fixes
  3. Preserve Success: Don't break what's working
  4. Learn from History: Avoid repeating failed approaches
  5. Set Clear Criteria: Define what success looks like

Actionable feedback. Prioritized improvements. Clear path forward.

平台分布

Claude Code

30.39%
按下载量换算53

windsurf

22.23%
按下载量换算38

Antigravity

20.07%
按下载量换算35

OpenCode

13.12%
按下载量换算23

Gemini CLI

8.58%
按下载量换算15

Codex

4.06%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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