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 arozumenko/sdlc-skills --skill stakeholder-interviewgit clone --depth 1 https://github.com/arozumenko/sdlc-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/arozumenko/sdlc-skills/stakeholder-interview)<a href="https://agentmods.dev/skills/arozumenko/sdlc-skills/stakeholder-interview"><img src="https://agentmods.dev/badge/skills/arozumenko/sdlc-skills/stakeholder-interview/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/arozumenko/sdlc-skills/stakeholder-interview"><img src="https://agentmods.dev/badge/skills/arozumenko/sdlc-skills/stakeholder-interview.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 | $0.00193 | $0.02207 |
| Opus 5 | $0.00097 | $0.01104 |
| Sonnet 5 | $0.00039 | $0.00441 |
| Haiku 4.5 | $0.00019 | $0.00221 |
Grade A, and why
stakeholder-interview 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
stakeholder-interview
Discovery without the customer in the room is desk research. This skill closes the loop in both directions: prepare makes sure an interview extracts maximum value from limited stakeholder access; synthesize makes sure what was learned actually lands in the workspace instead of evaporating in a notes file.
Audience calibration: the product owner is a senior product professional who knows how to run an interview. The value here is aggregation (mode 1) and filing + propagation (mode 2): the mechanical work the toolchain should do for them.
What this skill reads (config, by name)
From .agents/profile.md and the project's docs/ — read for the persona cast (used to
filter questions to what an interviewee's role can actually answer and to name interviewees
by role) and the confidentiality convention (committed records refer to people by role;
name-bearing raw material lives only in the confidential docs/discovery/_inbox/ zone).
And these docs/discovery/ locations: hypotheses/ (open questions, untested assumptions —
the status: lifecycle is a frontmatter field, not a folder), problems/ (unvalidated pain),
evidence/intake/, evidence/verifications/, evidence/research/, evidence/learnings/, and
evidence/interviews/ (this skill's own write target).
Mode detection
- Upcoming conversation mentioned, or "what should I ask" → prepare.
- Raw notes/transcript provided (pasted, or a file in
docs/discovery/_inbox/transcripts/or anywhere else) → synthesize. - Both in one session is normal: prepare before, synthesize after.
Step 0 — consult relevant lessons (by tag)
Grep docs/discovery/evidence/learnings/ by topic tag for any recorded lesson matching this
interviewee's persona and subject — a question a prior lesson already answered does not belong
in the room, in either mode. This is background evidence-gathering, not a hard requirement —
skip it if no learnings exist yet.
Mode: prepare
What ships with it
2 files 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.
- 10d ago First seen · 137 lines · 193 tokens per session scan A 48c81cfd9966
stakeholder-interview is a skill published in the GitHub repository arozumenko/sdlc-skills (20 stars, last pushed 5d ago), licensed MIT. It adds 193 tokens to every session and 2,207 once invoked, about $0.0010 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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