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 mshadmanrahman/pm-pilot --skill synthesize-interviewsgit clone --depth 1 https://github.com/mshadmanrahman/pm-pilotWrote 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/mshadmanrahman/pm-pilot/synthesize-interviews)<a href="https://agentmods.dev/skills/mshadmanrahman/pm-pilot/synthesize-interviews"><img src="https://agentmods.dev/badge/skills/mshadmanrahman/pm-pilot/synthesize-interviews/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/mshadmanrahman/pm-pilot/synthesize-interviews"><img src="https://agentmods.dev/badge/skills/mshadmanrahman/pm-pilot/synthesize-interviews.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01480 |
| Opus 5 | $0.00034 | $0.00740 |
| Sonnet 5 | $0.00013 | $0.00296 |
| Haiku 4.5 | $0.00007 | $0.00148 |
Grade A, and why
synthesize-interviews 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 10d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthesize Interviews: User Research Synthesis
Turn raw interview transcripts, notes, or feedback dumps into structured insights. Produces two outputs: a synthesis report (themes + recommendations) and a standalone pain points document (problems only, no solutions).
When to Activate
- User says "synthesize interviews", "what did users say about X"
- User provides interview transcripts or notes to analyze
- User says "analyze this feedback", "research findings"
- After 3+ customer conversations (suggest proactively)
Input Modes
Accept input three ways:
- Pasted content: Raw transcript or notes directly in conversation
- File path: Point to a file or directory of transcripts
- Workspace reference: "Look at the interview notes in /research"
If input is a single interview, produce per-interview notes. If 3+ interviews, produce a cross-interview synthesis.
Preferred input: interview snapshots
For multi-interview work, the best input is one Interview Snapshot per interview, produced by /pm-discovery:interview-snapshot and tagged synthesis_status: single-interview-complete. Reading structured snapshots instead of raw transcripts is what keeps quotes traceable at synthesis time.
If the user hands over raw transcripts for 3+ interviews, say so once and offer the snapshot pass first:
"I can synthesize these directly, but collapsing per-interview reading into cross-interview synthesis is where quote accuracy degrades. Want me to run
/pm-discovery:interview-snapshoton each one first? It takes one pass per interview and every quote comes back verified."
Proceed with raw transcripts if they decline. Do not block.
Hallucination guard (required)
Teresa Torres documented roughly a 30% quote hallucination rate when AI synthesizes interviews. Before delivering any synthesis that contains direct quotes:
- For every direct quote in the output, search the source transcript or snapshot for at least 60% of its words (exact match, case-insensitive).
- If the match fails, flag the quote inline with
[UNVERIFIED - edit before citing]and keep going. - Never silently repair a quote you cannot match. Mark it and move on.
- State the verified count in the output footer, for example
12/13 quotes verified against source.
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.
- 10d ago First seen · 166 lines · 67 tokens per session scan A d62f07615f74
synthesize-interviews is a skill published in the GitHub repository mshadmanrahman/pm-pilot (20 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 1,480 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 2026-08-30.
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