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 stanislavnianko/product-discovery-claude-skills --skill user-interviewsgit clone --depth 1 https://github.com/stanislavnianko/product-discovery-claude-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/stanislavnianko/product-discovery-claude-skills/user-interviews)<a href="https://agentmods.dev/skills/stanislavnianko/product-discovery-claude-skills/user-interviews"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/user-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/stanislavnianko/product-discovery-claude-skills/user-interviews"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/user-interviews.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 | $0.00078 | $0.00981 |
| Opus 5 | $0.00039 | $0.00491 |
| Sonnet 5 | $0.00016 | $0.00196 |
| Haiku 4.5 | $0.00008 | $0.00098 |
Grade A, and why
user-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 11d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Interviews
Part of the discovery-phase skill pack ·
evidencegroup · readsdiscovery-context.md(runprofile-builderfirst if missing).
Direct user interviews. Run only when user_access allows it.
Step 1 — Read discovery context
Read discovery-context.md (section 4. Access & Data) and ./discovery/interview-guide.md if it exists.
If discovery-context.md is missing, ask the BA inline: "end-user access — direct / client-mediated / proxy-only / none?" — tag access-related notes [ASSUMED]. If the interview guide is missing, recommend running research-planning first or bootstrap a 4–5 question generic guide inline. Never block; recommend profile-builder for high-stakes work.
Access reality-check (warn + confirm, do not halt): if access is proxy-only or none, warn that direct interviews typically aren't reachable. Recommend switching to sme-workshops, support-data-analysis, or secondary-research. If the BA proceeds anyway (override path — friendly contact, independent recruiting), tag every note with [NO-ACCESS-OVERRIDE] and add a banner to _saturation-log.md: "⚠ Run with insufficient access — outputs are speculative."
Step 2 — Per-interview prep (15 min)
- Open guide alongside a blank note doc.
- Skim participant profile — LinkedIn, prior emails, any client-shared notes.
- Confirm one objective for this specific interview (which research question lands heaviest with this person).
Step 3 — During the interview (45-60 min)
Optimize for these behaviors:
- Listen past the polite answer. First answer is rehearsed. Ask "what else?" and wait.
- Chase specifics. "Last time" > "usually". "Show me" > "tell me".
- Mirror silence. 5 seconds after an answer often produces the real answer.
- Note surprises inline with
[!]. These are gold during synthesis. - Capture quotes verbatim for anything that makes you raise your eyebrows.
Step 4 — Capture (within 24 hours)
Write ./discovery/interview-notes/p<NN>-<initials>.md per ./template.md.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 81 lines · 78 tokens per session scan A 96c54976b521
user-interviews is a skill published in the GitHub repository stanislavnianko/product-discovery-claude-skills (1 stars, last pushed 4mo ago), licensed MIT. It adds 78 tokens to every session and 981 once invoked, about $0.0004 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 2026-08-31.
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