Agent-Skills.md

Agent Skills: Meeting Insights

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personal-productivityID: borghei/claude-skills/meeting-insights

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borghei/claude-skills
borgheiLicense: NOASSERTION
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pnpm dlx add-skill https://github.com/borghei/Claude-Skills/tree/HEAD/personal-productivity/meeting-insights

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personal-productivity/meeting-insights/SKILL.md

Skill Metadata

Name
meeting-insights
Description
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Meeting Insights

Turn raw meeting transcripts into a structured set of decisions, action items, owners, due dates, open questions, and risks.

Table of Contents

Keywords

meeting, meetings, transcript, notes, minutes, action items, decisions, decision log, follow-up, recap, sales call, customer interview, retrospective, standup, planning, async

Clarify First

Before extracting insights, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • [ ] Transcript with speaker labels — Speaker: text format drives owner attribution on action items
  • [ ] Meeting type — recap vs customer interview vs decision log changes which extractions matter (decisions/actions vs pains/quotes)
  • [ ] Output target — recap email, append-only decision log, or interview synthesis sets the structure of the deliverable

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

Process a Transcript in 5 Minutes

  1. Save your transcript text as transcript.txt (one speaker turn per line, format Speaker: text)
  2. Run: 
    python scripts/transcript_analyzer.py transcript.txt
  3. Review the structured output: decisions, action items, owners, due dates, open questions
  4. Drop into assets/recap_template.md to send a follow-up

Core Workflows

Workflow 1: Post-Meeting Recap

Goal: Convert a 60-minute conversation into a 90-second readable summary that everyone can act on.

Steps:

  1. Export the transcript (Otter, Fireflies, Zoom, Google Meet, etc.)
  2. Run: python scripts/transcript_analyzer.py transcript.txt
  3. Verify owners and due dates — the analyzer is heuristic; humans correct
  4. Paste structured output into assets/recap_template.md
  5. Send within 24 hours of the meeting

Expected Output: Recap with decisions, action items (owner + due date), open questions, and risks.

Time Estimate: 5-10 minutes vs. 30+ for manual note review.

Workflow 2: Customer Interview Synthesis

Goal: Pull the signals out of a discovery call without losing the customer's actual words.

Steps:

  1. Run analyzer in JSON mode: python scripts/transcript_analyzer.py transcript.txt --json
  2. Filter for pains and quotes — these are the discovery signals
  3. Use references/insight_extraction_patterns.md to triangulate across multiple interviews
  4. Tag findings by ICP segment for product / marketing handoff

Expected Output: Tagged customer pain list with verbatim quotes per insight.

Time Estimate: 15 minutes per interview after the call.

Workflow 3: Decision Log Maintenance

Goal: Build an organizational memory so the same decision is not re-litigated quarter after quarter.

Steps:

  1. After each meeting, run the analyzer to extract decisions
  2. Append to a running decision log keyed by date and topic
  3. When a future meeting raises an old topic, search the log first
  4. Re-open formally rather than silently overturning

Expected Output: Append-only decision log searchable by topic and date.

Time Estimate: 2-3 minutes per meeting.

Tools

transcript_analyzer.py

Reads a transcript text file and extracts:

  • Decisions — sentences with decision markers ("we decided", "agreed", "going with")
  • Action items — sentences with action markers ("will", "going to", "by next week"), with heuristic owner + due date
  • Open questions — sentences ending in "?" or marked with "open question"
  • Risks — sentences with risk markers ("risk", "concern", "blocker", "if X then Y")
  • Quotes — distinctive verbatim sentences > 12 words (for customer interview workflows)
# Human-readable
python scripts/transcript_analyzer.py transcript.txt

# JSON for programmatic use
python scripts/transcript_analyzer.py transcript.txt --json

Transcript format expected:

Alice: We need to decide on the launch date this week.
Bob: I'll send the draft by Friday.
Alice: Are we blocked on legal review?
Bob: Yes, that's the risk — if legal slips, launch slips.

Reference Guides

  • references/insight_extraction_patterns.md — Heuristic triggers for decisions, actions, and risks; how to triangulate across interviews

Templates

  • assets/recap_template.md — Post-meeting recap email with placeholder sections

Best Practices

  • Verify before sending. The analyzer is heuristic; an unverified recap that mis-attributes an action item destroys trust.
  • Owner + date or it does not exist. An action item without an owner is a hope; without a date, it is a wish.
  • Send within 24 hours. Memory of who said what fades fast; recap latency directly correlates with action-item completion rate.
  • Quote verbatim. For customer interviews, the customer's words matter more than your summary of them.
  • Decision log is append-only. Never silently overturn — re-open with a dated update.

Integration Points

  • Pairs with product-team/user-story/ for converting interview pains into stories
  • Pairs with project-management/ for action-item tracking
  • Feeds into marketing/ voice-of-customer workflows