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Agent Skill · 研究检索

conversation-analysis(对话分析)

conversation-analysis 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。

conversation-analysis 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,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 49,494。

总安装 649周安装 26GitHub Stars 公开资料未说明下载 210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:conversation-analysis(对话分析)
来源仓库:https://github.com/outfitter-dev/agents
仓库路径:skills/conversation-analysis
安装命令:
npx skills add outfitter-dev/agents --skill "conversation-analysis"
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。

AgentSkills.tonpx skills
npx skills add outfitter-dev/agents --skill "conversation-analysis"

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。

用于对话内容的分析与信息提取,辅助理解用户意图与上下文。适合在 Codex、Claude、Cursor、Gemini CLI 中进行对话研究或检索任务。使用 npx skills add outfitter-dev/agents --skill "conversation-analysis" 安装并调用。

需确认权限范围、维护状态及是否触发联网或文件读写操作。建议结合原始 README 核验具体功能与使用限制。

来源仓库内路径:skills/conversation-analysis

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 outfitter-dev/agents --skill "conversation-analysis";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

conversation-analysis 怎么安装?

在终端执行 npx skills add outfitter-dev/agents --skill "conversation-analysis"。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

conversation-analysis 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。

conversation-analysis 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。

conversation-analysis 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。

conversation-analysis 用的人多吗?

公开数据是累计安装 649 次,近一周安装 26 次,下载 210 次。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。

conversation-analysis 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

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。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 要求有安全扫描结论才肯安装的团队。
  • 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
conversation-analysis

对话分析 · 本页

649—只读MIT未收录

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

Conversation Analysis

Signal extraction → pattern detection → behavioral insights.

<when_to_use>

  • User requests conversation analysis
  • Identifying frustration, success, or workflow patterns
  • Extracting user preferences and requirements
  • Understanding task evolution and iterations

NOT for: real-time monitoring, content generation, single message analysis

</when_to_use>

<signal_taxonomy>

TypeSubtypeIndicators
SuccessExplicit Praise"Perfect!", "Exactly what I needed", exclamation marks
SuccessContinuation"Now do the same for...", building on prior work
SuccessAdoptionUser implements suggestion without modification
SuccessAcceptance"Looks good", "Ship it", "Merge this"
FrustrationCorrection"No, I meant...", "That's wrong", "Do X instead"
FrustrationReversionUser undoes agent changes, "Go back"
FrustrationRepetitionSame request 2+ times, escalating specificity
FrustrationExplicit"This isn't working", "Why did you...", accusatory tone
WorkflowSequence"First...", "Then...", "Finally...", numbered lists
WorkflowTransition"Now that X is done, let's Y", phase changes
WorkflowTool ChainRecurring tool usage patterns (Read → Edit → Bash)
WorkflowContext SwitchAbrupt topic changes, no transition language
RequestProhibition"Don't use X", "Never do Y", "Avoid Z"
RequestRequirement"Always check...", "Make sure to...", "You must..."
RequestPreference"I prefer...", "It's better to...", comparative language
RequestConditional"If X then Y", "When A, do B", situational rules

Confidence levels:

  • High (0.8–1.0): Explicit keywords match taxonomy, no ambiguity, strong context
  • Medium (0.5–0.79): Implicit signal, partial context, minor ambiguity
  • Low (0.2–0.49): Ambiguous language, weak context, borderline classification

</signal_taxonomy>

Track with TodoWrite. Phases advance only, never regress.

PhaseTriggeractiveForm
Parse InputSession start"Parsing input"
Extract SignalsScope validated"Extracting signals"
Detect PatternsSignals extracted"Detecting patterns"
Synthesize ReportPatterns detected"Synthesizing report"

TodoWrite format:

- Parse Input { scope description }
- Extract Signals { from N messages }
- Detect Patterns { category focus }
- Synthesize Report { output format }

Edge cases:

  • Small scope (<5 messages): Skip Extract Signals, jump to Synthesize
  • Re-analysis: Resume at Detect Patterns
  • Narrow focus (single signal type): Skip Detect Patterns

Workflow:

  • Start: Create Parse Input in_progress
  • Transition: Mark current completed, add next in_progress
  • After delivery: Mark Synthesize Report completed
  1. Define Scope

- Message range (all, recent N, date range) - Actors (user only, agent only, both) - Exclusions (system messages, tool outputs, code blocks) - Mark Parse Input completed, create Extract Signals in_progress

  1. Extract Signals

- Scan messages for signal keywords - Match against taxonomy - Assign confidence (high/medium/low) - Record: type, subtype, message_id, timestamp, quote, context - Mark Extract Signals completed, create Detect Patterns in_progress

  1. Detect Patterns

- Group signals by type/subtype - Find clusters (3+ related signals) - Identify evolution (signal changes over time) - Track repetition (recurring themes) - Spot correlations (tool chains, workflows) - Mark Detect Patterns completed, create Synthesize Report in_progress

  1. Output

- Generate JSON with signals, patterns, summary - Include confidence, recommendations, action items - Append △ Caveats if gaps exist - Mark Synthesize Report completed

<pattern_detection>

Behavioral patterns from signal clusters:

PatternDetectionConfidence
RepetitionSame signal 3+ timesStrong: 5+ signals
EvolutionSignal type changes over timeModerate: 3-4 signals
PreferencesConsistent request signalsStrong: across sessions
Tool ChainsRecurring tool sequences (5+ times)High: frequent use
Problem AreasClustered frustration signalsStrong: 3+ in same topic

Temporal patterns:

  • Escalation: Increasing frustration/stronger requirements
  • De-escalation: Frustration → success transition
  • Cyclical: Same issue recurs across sessions

</pattern_detection>

<output_format>

JSON structure:

{
  "analysis": {
    "scope": {
      "message_count": N,
      "date_range": "YYYY-MM-DD to YYYY-MM-DD",
      "actors": ["user", "agent"]
    },
    "signals": [
      {
        "type": "success|frustration|workflow|request",
        "subtype": "specific_subtype",
        "message_id": "msg_123",
        "timestamp": "ISO8601",
        "quote": "exact text",
        "confidence": "high|medium|low",
        "context": "brief explanation"
      }
    ],
    "patterns": [
      {
        "pattern_type": "repetition|evolution|preference|tool_chain",
        "category": "success|frustration|workflow|request",
        "description": "pattern summary",
        "occurrences": N,
        "confidence": "strong|moderate|weak",
        "first_seen": "ISO8601",
        "last_seen": "ISO8601",
        "recommendation": "actionable next step"
      }
    ],
    "summary": {
      "total_signals": N,
      "by_type": { "success": N, "frustration": N, ... },
      "key_insights": ["insight 1", "insight 2"],
      "action_items": ["item 1", "item 2"]
    }
  }
}

</output_format>

ALWAYS:

  • Create Parse Input at session start
  • Update todos at phase transitions
  • Include confidence levels for all signals
  • Support patterns with 2+ signals minimum
  • Mark Synthesize Report completed after delivery
  • Apply recency weighting (recent overrides old)

NEVER:

  • Skip phase transitions
  • Extract low-confidence signals without marking them
  • Claim patterns from single occurrences
  • Regress phases
  • Deliver without marking final phase complete
  • Over-interpret neutral language

平台分布

Claude Code

24.92%
按下载量换算52

windsurf

24.81%
按下载量换算52

OpenCode

17.6%
按下载量换算37

Cursor

12.45%
按下载量换算26

Codex

7.99%
按下载量换算17

Antigravity

3.43%
按下载量换算7

安全审计

暂无安全审计结果可展示。

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。目前只有一个公开来源,建议同时核对源仓库。

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