customer-interview-synthesizer
Skill1 starsChanged 20 days agoMITView on GitHub
Synthesizes raw customer interview transcripts into themes, quotes, Jobs-to-be-Done (JTBD) signals, force-of-progress diagrams, and actionable opportunity hypotheses with confidence levels. Use when synthesizing a round of discovery, switching, win-loss, or churn interviews; preparing a research readout; aligning a team on what the customer signal actually says; or feeding inputs into an Opportunity Solution Tree (OST).
1 other project uses a version of this.
Adopted by
Most recently taken up by
- Xipher-Labs/walter-ostheir own version, first seen May 21, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- Xipher-Labs/walter-os★ 8same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository41 other files it gives its agents.
- ai-activation-optimizerSkill
- ai-content-quality-gateSkill
- ai-feature-experiment-designerSkill
- ai-launch-playbookSkill
- ai-pricing-margin-modelerSkill
- ai-product-positioningSkill
- ai-search-visibility-optimizerSkill
- agent-autonomy-level-mapperSkill
- ai-failure-state-designerSkill
- ai-usability-test-plannerSkill
- ai-ux-auditSkill
- human-in-the-loop-designerSkill
- prompt-surface-designerSkill
- uncertainty-and-explainability-patternsSkill
- ai-agent-financial-analystSkill
- burn-rate-runway-plannerSkill
- capital-allocation-frameworkSkill
- dcf-valuation-builderSkill
- financial-statement-analyzerSkill
- saas-cohort-analyzerSkill
- competitive-response-modelerSkill
- mechanism-design-plannerSkill
- nash-equilibrium-solverSkill
- negotiation-strategistSkill
- repeated-game-strategistSkill
- ai-decision-engineSkill
- ai-product-strategySkill
- ai-stakeholder-translatorSkill
- jobs-to-be-done-extractorSkill
- opportunity-solution-treeSkill
- product-discovery-coachSkill
- product-roadmap-prioritizerSkill
- blue-ocean-strategy-canvasSkill
- go-to-market-strategySkill
- gtm-engineeringSkill
- market-sizingSkill
- okr-cascade-plannerSkill
- porters-five-forces-analyzerSkill
- pricing-strategy-analyzerSkill
- strategic-landscape-mapperSkill
- swot-tows-analyzerSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
No theme saturation check (claiming a theme on a sample size of one)
- Distinctive: line 44
Read each transcript line by line.
- Distinctive: line 72
Write the job statement: *When [situation], I want to [motivation], so I can [outcome].*
- Distinctive: line 110
Never blur the line. Stakeholders will treat them as the same unless you separate them.
- Distinctive: line 135
Use the analyst's words as if they were the customer's
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Customer Interview Synthesizer
Turns hours of qualitative interviews into a tight, evidence-anchored set of opportunities.
What this skill is
A structured workflow for synthesizing interview transcripts: code utterances, theme them, score saturation, extract Jobs-to-be-Done (JTBD) signals and forces of progress, identify opportunity hypotheses, and produce a readout that distinguishes evidence from interpretation. Designed to remove the analyst's narrative-fitting bias and keep the customer's voice central.
What it solves
- "What did we learn?" readouts that summarize whichever interview was freshest
- Cherry-picking quotes that support a pre-existing hypothesis
- No theme saturation check (claiming a theme on a sample size of one)
- Conflating what the customer said with what the analyst inferred
- Failure to translate findings into prioritized hypotheses with confidence levels
When to invoke
- Concluding a discovery or generative interview round
- Synthesizing switching interviews after a churn wave
- Win-loss analysis from a sales cycle
- Sensemaking after a competitive deep-dive
- Feeding the Opportunity Solution Tree (OST) with fresh evidence
Phase 1: Catalog the corpus
For each interview, log:
- Participant pseudonym, segment, role
- Date, duration, interviewer
- Recording or transcript location
- Recruitment source (and any selection bias)
- Whether incentives were offered
- Whether the participant is current, former, or prospective customer
Minimum saturation guideline: 5-8 interviews per segment for generative discovery; more for major decisions.
Phase 2: Code the utterances
Read each transcript line by line. Tag passages with codes - short labels for the topic discussed.
Two-pass approach:
- Open coding - generate codes inductively from the data
- Axial coding - group related codes into categories
Each code must include:
- A verbatim quote from the transcript
- Interview Identifier (ID) and timestamp
- Whether it's a report (something they did or experienced) or opinion (something they said they'd do)
Reports far outweigh opinions in predictive value. Flag the distinction.
Phase 3: Theme the codes
Group codes into themes. For each theme:
- Theme title in the customer's voice
- Definition
- Number of participants who mentioned it (saturation count)
- 3-5 representative verbatim quotes
- Segments where the theme is strongest
- Disconfirming evidence (interviews where the theme didn't appear)
Saturation rule: a theme appearing in less than 30% of interviews in a segment is a hypothesis, not a finding.
Phase 4: Extract JTBD signals
For passages describing what the customer was trying to accomplish:
- Write the job statement: When [situation], I want to [motivation], so I can [outcome].
- Classify functional / emotional / social
- Identify the job stage (Define / Locate / Prepare / Confirm / Execute / Monitor / Modify / Conclude)
Phase 5: Forces of progress
For switching, churn, and win-loss interviews, map the four forces per participant:
| Force | Evidence from interview |
| Push of current situation | What was broken or frustrating? |
| Pull of new solution | What did they hope for? |
| Anxiety of new solution | What worried them about switching? |
| Habit of old | What kept them on the old solution? |
Aggregate across interviews to identify which force dominates the decision.
Phase 6: Generate opportunity hypotheses
Convert themes into opportunity hypotheses:
We believe [customer segment] experiences [pain or unmet need] when [situation], because [evidence].
Confidence: [High / Medium / Low] based on [number of interviews] and [type of evidence].
If we solved this, we'd expect to see [observable outcome].
Each hypothesis links back to:
- The theme(s) it came from
- The verbatim quotes that support it
- The disconfirming evidence (where it didn't show up)
Phase 7: Separate observation from interpretation
The synthesis output has two tiers, clearly labeled:
- What we heard (verbatim, quoted, sourced) - high confidence
- What it might mean (analyst interpretation) - explicitly labeled as inference
Never blur the line. Stakeholders will treat them as the same unless you separate them.
Output
- Coded transcript library (referenceable for any future synthesis)
- Theme list with saturation counts and supporting verbatims
- JTBD inventory: functional, emotional, and social jobs by segment
- Forces-of-progress diagram for switching or churn signals
- Opportunity hypothesis register with confidence levels and source evidence
- 1-page readout with the 3 most decision-grade findings
- Explicit list of disconfirming evidence
- Recommended next-round research questions
Operating rules
Always
- Quote verbatim before paraphrasing
- Distinguish reports from opinions
- Score theme saturation (count of interviews / total interviews per segment)
- Surface disconfirming evidence explicitly
- Label observation versus interpretation
Never
- Claim a theme from a single interview
- Cherry-pick quotes that confirm a hypothesis
- Use the analyst's words as if they were the customer's
- Skip the recruitment-bias note
- Merge wildly different segments into one theme
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

