跳到正文
cccskills
免费在 GitHub 公开

meeting-to-tasks

Takes meeting transcripts, extracts action items with owners and deadlines, detects implicit commitments, generates structured meeting summaries, and outputs task files compatible with Linear, GitHub Issues, and other project management tools.

中文简介正在准备中。当前显示原文说明。

集成工具GitHub

实测评分 未实测

查看安装方法

包含的文件(5)

  • SKILL.md4.1 KB
  • references/extraction-schema.md3.2 KB
  • references/markers.md3.2 KB
  • references/meeting-types.md1.3 KB
  • references/output-templates.md4.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Meeting to Tasks

Transform raw meeting transcripts into structured, actionable outputs: decisions, action items, owners, deadlines, and open questions -- including implicit commitments participants may not realize they made.

Contents

  • references/extraction-schema.md -- YAML field schemas for every extraction phase
  • references/markers.md -- Decision/action/implicit markers, priority + deadline inference, confidence scoring
  • references/output-templates.md -- Meeting summary, task files (Linear/GitHub/generic), follow-up email, directory layout
  • references/meeting-types.md -- Meeting-type detection and per-type focus

Workflow

  1. Accept input. Handle plain text, Markdown, pasted text, audio transcript exports (Otter.ai, Fireflies, Rev, Zoom, Google Meet, Microsoft Teams), or structured notes.
  2. Validate. Confirm there is enough content (~100 words minimum), identifiable speakers, a discernible topic, and any timestamps. If input is too sparse, ask for the full transcript, the participant list, or the meeting purpose before proceeding.
  3. Read the full transcript first. Do not extract from a partial read -- later context can change earlier interpretation.
  4. Detect the meeting type and adjust focus per references/meeting-types.md.
  5. Extract in phases using the schemas in references/extraction-schema.md: metadata, decisions, action items, open questions, parking lot, discussion points. Apply the markers and inference rules in references/markers.md. Preserve the exact source quote for every item.
  6. Identify speakers. Build the participant list from speaker labels and track who said what. When labels are absent, infer from context clues, otherwise mark "Unassigned" and flag for the user to assign.
  7. Score confidence on every item (high/medium/low). Flag all low-confidence items for human review per references/markers.md.
  8. Resolve conflicts and ambiguity: flag contradictory commitments as "CONFLICT" with both versions; list all candidates for unclear ownership; translate vague deadlines ("soon", "ASAP") to specific dates and flag for confirmation; offer both readings of ambiguous scope; consolidate duplicates.
  9. Generate outputs using references/output-templates.md. Confirm which project management format(s) the user wants before generating task files. Always generate the follow-up email, even if unasked.
  10. Post-process: deduplicate overlapping items; map dependencies; highlight the critical path; report owner imbalance; warn on deadline clustering; list items missing owners or deadlines.

Rules

  • Make action items actionable: start each with a verb, specific enough that the owner knows exactly what to do. Transform "Think about pricing" into "Research competitor pricing and propose new pricing tiers by [date]."
  • Err toward capturing too much; a flagged low-confidence item beats a missed commitment.
  • Do not editorialize. Report what was said; keep any analysis in clearly labeled "Analysis" sections.
  • Respect privacy. If the meeting contains HR issues, personal matters, or confidential information, flag it and ask how to handle it in outputs.
  • Handle multi-language transcripts: extract in the primary language and note language-specific nuances.
  • Maintain an extraction log noting confidence levels, ambiguities, and decisions made during extraction.

Quick Commands

  • "Extract from [file]" -- Full extraction from the specified transcript
  • "Just action items" -- Action items only (skip decisions, questions, summary)
  • "Generate tasks for Linear" / "Generate tasks for GitHub" -- Action items in that format
  • "Draft follow-up email" -- Follow-up email only
  • "Who owes what?" -- Owner-grouped view of all action items
  • "What was decided?" -- Decisions only
  • "What is still open?" -- Open questions and unresolved items

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.

中文简介正在准备中。当前显示原文说明。

OneWave-AI/claude-skills3682026年10月20日 更新

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

中文简介正在准备中。当前显示原文说明。

OneWave-AI/claude-skills3682026年10月20日 更新

Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.

中文简介正在准备中。当前显示原文说明。

OneWave-AI/claude-skills3682026年10月20日 更新

Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.

中文简介正在准备中。当前显示原文说明。

OneWave-AI/claude-skills3682026年10月20日 更新

Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.

中文简介正在准备中。当前显示原文说明。

OneWave-AI/claude-skills3682026年10月20日 更新

Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.

中文简介正在准备中。当前显示原文说明。

OneWave-AI/claude-skills3682026年10月20日 更新

查看 OneWave-AI 的全部技能

报告此技能的问题