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Agent Skill · 研究检索

foundation-meeting-synthesize(基金会会议综合)

foundation-meeting-synthesize 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。

foundation-meeting-synthesize 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;许可证信息还不明确,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 68,166。

总安装 235周安装 10GitHub Stars 168下载 82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
unknown

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:foundation-meeting-synthesize(基金会会议综合)
来源仓库:https://github.com/product-on-purpose/pm-skills
仓库路径:skills/foundation-meeting-synthesize
安装命令:
npx skills add https://github.com/product-on-purpose/pm-skills --skill foundation-meeting-synthesize
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/product-on-purpose/pm-skills --skill foundation-meeting-synthesize

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。

foundation-meeting-synthesize 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中整合多源信息形成结论。通过 npx skills add 命令从指定仓库安装并使用该技能。安装前需确认权限范围、维护状态及是否触发联网或命令执行。

建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/foundation-meeting-synthesize

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/product-on-purpose/pm-skills --skill foundatio…;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

foundation-meeting-synthesize 怎么安装?

在终端执行 npx skills add https://github.com/product-on-purpose/pm-skills --skill foundation-meeting-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

foundation-meeting-synthesize 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。

foundation-meeting-synthesize 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。

foundation-meeting-synthesize 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

foundation-meeting-synthesize 用的人多吗?

公开数据是累计安装 235 次,近一周安装 10 次,下载 82 次,GitHub 168 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。

foundation-meeting-synthesize 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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 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

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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

  • 需要明确授权才能用到公司项目里的场景。
  • 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
foundation-meeting-synthesize

基金会会议综合 · 本页

235168只读未明确通过

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

Meeting Synthesize

Meeting synthesis is the archaeology skill for multi-meeting initiatives. It consumes a set of meeting recaps (and optionally raw notes) over a period, and surfaces patterns that no single meeting reveals: how decisions evolved, how stakeholder positions shifted, where threads are stalling, where contradictions have emerged.

Distinct from /discover-interview-synthesis: that skill works on user-research conversations with research-specific frameworks (jobs-to-be-done, buying insights). This skill works on internal org meetings with org-specific patterns (stakeholder alignment, decision evolution, project history).

This skill belongs to the Meeting Skills Family. It conforms to the Meeting Skills Family Contract.

When to Use

  • Board prep or exec-brief preparation across a meeting sequence
  • Onboarding a new team member into the history of an initiative
  • Project retrospective input (the story of how we got here)
  • Investigating why a multi-meeting initiative has stalled
  • Quarterly review of a topic that has crossed many meetings
  • Surfacing contradictions that no single-meeting reviewer caught

When NOT to Use

  • Single-meeting summary. Use /meeting-recap instead.
  • Communicating outcomes outward. Use /stakeholder-update.
  • User research conversation synthesis. Use /discover-interview-synthesis.

Zero-friction execution

Per the family contract, this skill never blocks on interrogation. Default flow:

  1. Load all provided source files (recaps preferred, raw notes accepted with lower input-quality flag)
  2. Apply any filters (time range, topic, stakeholder)
  3. Run inference on themes, stakeholder evolution, contradictions
  4. Present a brief inference summary (meeting count after filter, time range detected, per-source input quality, scope filter applied)
  5. Accept go or corrections
  6. Produce the synthesis

If invoked with --go, skip the inference summary. Format hints (board-prep, onboarding, retro-input, exec-brief) control output presentation without changing the underlying process.

Instructions

When asked to create a meeting synthesis, follow these steps:

  1. Load sources Read all provided recap filenames or note files. Parse frontmatter to extract meeting metadata. Note per-source input quality (recap frontmatter's input_quality if available; otherwise assess from content). Metadata source tracking (v1.1.0): for each recap, also note the meeting_type_source field (explicit | inferred | null). When synthesizing across mixed sources, the synthesis must document the mix explicitly in the Scope section: "meeting_type values: N explicit, M inferred, K null." This prevents non-reproducible results when filtering by meeting_type across a mix of confidence levels. If filtering by meeting_type, state whether the filter includes inferred values and how null values are handled.
  2. Apply filters If time range, topic, or stakeholder filter provided, narrow the source set before proceeding. Record the filter applied in frontmatter scope_filter.
  3. Present go-mode inference summary Meeting count after filter, time range detected from source metadata, per-source input quality levels, scope filter description.
  4. Build the plain-text timeline Chronological order by meeting_date. Each entry shows date, meeting name, key decision or shift, and confidence or contradiction flag if applicable. Render as markdown (no binary images. must render everywhere).
  5. Extract themes Cluster recurring topics across sources. For each theme, record description, sources where it appeared, and confidence marker tied to frequency ("appears in 5/5 meetings" → high; "appears in 2/5 meetings" → medium; "mentioned in 1 meeting" → low).
  6. Track stakeholder positions For each named stakeholder across sources, record initial position → current position, alignment state (aligned / divergent / shifting), and key statements with dates. Flag confidence on each position based on whether it was direct-quoted or paraphrased.
  7. Consolidate decisions Cross-meeting, sorted chronologically. Table format: Date | Decision | Context | Meeting | Confidence.
  8. Separate decision evolution from unresolved contradictions (v1.1.0) Two distinct outputs, not one combined "contradictions" section: The distinction matters because v1.0.0 conflated them, creating false-positive "contradictions" in exec contexts where the "contradiction" was actually intentional scope evolution.

- Decision evolution (resolved): earlier decision → later decision on the same topic, where the later one supersedes. This is historical context, not a red flag. No ⚠ emphasis. - Unresolved contradictions: decisions or positions currently in conflict that need reconciliation. Use ⚠ visual emphasis. For each: - Earlier reference (meeting + date + decision) - Later reference (meeting + date + decision that conflicts) - Status: unresolved / needs reconciliation

  1. Identify open items and stalled threads Topics surfacing 2+ times without resolution. When they last appeared.
  2. Draft narrative summary 2-3 paragraphs: what happened, what changed, where we are, what is at stake. The "story" of the meeting set.
  3. Prioritize follow-up suggestions

- High: unblocking now; suggested owner or forum - Medium: important but not blocking - Low: monitor Each with rationale.

  1. Apply format hint (if provided) One process generates the full synthesis data. The hint controls section ordering and truncation for the specific use case:

- board-prep: lead with narrative + contradictions + prioritized follow-ups; shorter timeline - onboarding: lead with narrative + stakeholder tracking; full timeline - retro-input: lead with themes + stalled threads + meeting-quality aggregate - exec-brief: TL;DR + top 3 items only

  1. Render TEMPLATE.md and validate

- source_meetings list is non-empty - time_range.start ≤ time_range.end - Every theme has a confidence marker - Every contradiction has before/after source citations - At least 1 prioritized follow-up

Quality checklist

  • Source meetings listed with filename + date + per-source input quality
  • Time range populated and sensible
  • Scope filter described (or "none applied")
  • Timeline is plain-text markdown (no binary images)
  • Themes include confidence markers tied to frequency
  • Stakeholder position tracking shows evolution (initial → current), not just snapshot
  • Consolidated decision list is chronological with source citations
  • Contradictions are flagged in their own first-class section (not buried)
  • Narrative summary is 2-3 paragraphs, not bullets
  • Follow-ups are prioritized (High / Medium / Low) with rationale
  • Frontmatter omits single-meeting fields (meeting_title, meeting_date, etc.)
  • Sources and References weights input quality across sources

See also

平台分布

Codex

35.54%
按下载量换算29

Claude

28.37%
按下载量换算23

Cursor

16.99%
按下载量换算14

Gemini CLI

9.11%
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。目前只有一个公开来源,建议同时核对源仓库。

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