Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cnfeat/top-pm-skills --skill user-interview-synthesisgit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cnfeat/top-pm-skills/user-interview-synthesis)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/user-interview-synthesis"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/user-interview-synthesis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/user-interview-synthesis"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/user-interview-synthesis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.00618 |
| Opus 5 | $0.00034 | $0.00309 |
| Sonnet 5 | $0.00013 | $0.00124 |
| Haiku 4.5 | $0.00007 | $0.00062 |
Grade A, and why
user-interview-synthesis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Interview Synthesis Skill
Transform raw interview transcripts into a structured synthesis document that surfaces themes, pain points, and actionable insights.
Required Inputs
Ask the user for these if not provided:
- Interview transcripts or notes (even rough notes work)
- Number of participants and their profiles (role, company size, context)
- Research questions (what was the study trying to answer?)
- Date range of research (for context)
Process
- Read all provided transcripts fully before drawing conclusions
- Identify recurring themes (minimum 3 mentions to qualify as a theme)
- Categorize findings into: Pain Points, Workflow Insights, Feature Requests, Delight Moments
- Select 2-3 verbatim quotes per theme that best represent the pattern
- Draft "So What" implications for each theme — what does this mean for the product?
- Validate — Confirm every theme has quotes from at least 3 participants. Flag any insight resting on fewer as low-confidence.
Output Structure
Research Synthesis: [Study Name]
Participants: [n] Date Range: [dates] Research Questions: [list]
Theme 1: [Theme Name]
- Summary (2-3 sentences)
- Supporting quotes (from at least 3 participants)
- Implication for product
[Repeat for each theme]
Low-Confidence Signals (1-2 participants only)
[Findings worth tracking but not acting on yet — note what further research would confirm or deny]
Recommended Next Steps
[Specific, actionable recommendations based on findings]
Quality Checks
- Every theme is supported by quotes from at least 3 participants
- Implications connect to specific product decisions, not just observations
- Researcher bias check: no leading language, findings don't all support one hypothesis
- Single-source signals are flagged separately, not mixed into main themes
- Research questions from the study brief are each addressed (even if the answer is "inconclusive")
Anti-Patterns
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 61 lines · 67 tokens per session scan A f7ddb27ead14
user-interview-synthesis is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 618 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…