AGENT SKILL

customer-learning-notes

Turn customer conversation notes, interview transcripts, call summaries, CRM snippets, or research notes into shared team learning and next questions. Use when synthesizing raw customer notes, avoiding founder interpretation bottlenecks, extracting quotes and signals, updating beliefs, or deciding what to ask next.

License 许可
MIT
最近更新
2026-11-11
Stars
★ 1.6k
Forks
474

01 前言 Front Matter

name
customer-learning-notes
license
MIT
metadata
{ "tags": "customer-discovery,research,notes,synthesis,startups", "version": "0.1.3", "category": "Marketing", "displayName": "Customer Learning Notes" }
description
Turn customer conversation notes, interview transcripts, call summaries, CRM snippets, or research notes into shared team learning and next questions. Use when synthesizing raw customer notes, avoiding founder interpretation bottlenecks, extracting quotes and signals, updating beliefs, or deciding what to ask next.
compatibility
Codex, Claude Code, and other Agent Skills-compatible clients.

02 资源文件 2

03 SKILL.md 正文

Customer Learning Notes

Use this skill after customer conversations to turn raw notes into team-readable evidence. Good notes make it harder to misremember, overfit, or let one founder become the sole source of customer truth.

Source Traceability

Primary source: The Mom Test by Rob Fitzpatrick, especially chapter 8 and the conclusion. Guidance is paraphrased for this MIT repo; authoring notes used converted EPUB lines 3613-4445.

Signal Taxonomy

Use these labels when synthesizing notes:

Label Meaning
Pain Problem, obstacle, annoyance, risk, or cost
Goal Desired outcome, job to be done, or priority
Workaround Current manual process, tool stack, hack, or substitute
Money Budget, cost, value, purchase process, or decision owner
Person Specific stakeholder, competitor, team, buyer, or intro lead
Feature Request, buying criterion, integration need, or implementation clue
Emotion Strong excitement, anger, embarrassment, fear, or skepticism
Follow-up Promise, task, intro, research item, or next step

Synthesis Workflow

  1. Preserve concrete facts separately from interpretation.
  2. Pull out short, useful quotes only when they are needed for traceability, positioning, or internal alignment.
  3. Tag signals using the taxonomy above.
  4. Group evidence by segment, problem, workaround, budget, and commitment.
  5. Identify contradictions and mixed-segment noise.
  6. Update beliefs, risks, and the next three questions.
  7. Recommend whether to continue, narrow the segment, ask for commitments, or move to building/testing.

Confidence Levels

Level Use When
High Repeated behavior from a focused segment, with concrete cost or commitment.
Medium Specific evidence from a few good-fit conversations.
Low One-off quotes, mixed segments, opinions, or weakly anchored claims.

Output Format

# Customer Learning Synthesis

## Source Notes
- Conversations:
- Segment:
- Date range:

## Evidence
| Signal | Evidence | Segment | Confidence | Implication |
|--------|----------|---------|------------|-------------|

## Belief Updates
- Stronger / weaker / new / rejected:

## Decisions
- Product, segment, positioning, sales or access:

## Next 3 Questions
1. [Question]
2. [Question]
3. [Question]

Workflow

Use workflows/synthesize-conversation-notes.md when the user provides raw notes, transcripts, call summaries, or interview excerpts.

Quality Bar

  • Do not summarize notes into vibes.
  • Do not let one loud quote outweigh repeated behavior from a focused segment.
  • Do not mix segments without labeling them.
  • Do not treat notes as useful until they have been reviewed and turned into updated beliefs or decisions.

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