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 lenar-amirov/product-pipeline-public --skill interview-analysisgit clone --depth 1 https://github.com/lenar-amirov/product-pipeline-publicWrote 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/lenar-amirov/product-pipeline-public/interview-analysis)<a href="https://agentmods.dev/skills/lenar-amirov/product-pipeline-public/interview-analysis"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/interview-analysis/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/lenar-amirov/product-pipeline-public/interview-analysis"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/interview-analysis.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 | $0.00073 | $0.00634 |
| Opus 5 | $0.00036 | $0.00317 |
| Sonnet 5 | $0.00015 | $0.00127 |
| Haiku 4.5 | $0.00007 | $0.00063 |
Grade A, and why
interview-analysis 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 12d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Analysis — notes → codes → verdicts
Interviews are the highest-confidence qualitative REAL source (0.9–1.0 by the evidence-typing scale) — and the easiest to cherry-pick. This method keeps them honest.
1. Intake
Input: research/interview-notes.md (or pasted notes / transcripts).
Establish per interview: participant segment (matches CONTEXT.md segment?),
date, moderated by whom. Off-segment participants get analyzed separately —
their quotes must not launder into the main verdicts.
2. Coding (before any conclusions)
Walk the notes and tag utterances with codes — behaviors, pains, workarounds, triggers ("compares prices elsewhere", "doesn't trust payment"). Rules:
- Code what the user DID or experienced, not what they proposed ("add feature X" → code the underlying pain, not the feature)
- One utterance may carry several codes; keep participant id attached
- New code ≠ new hypothesis yet — codes are raw material
3. Patterns with frequency honesty
Group codes → patterns with explicit counts: "4 of 6 participants mentioned…". Rules:
- A pattern needs ≥3 participants (of a typical 5–8 batch) — below that it's an anecdote; report it as such
- Actively look for DISCONFIRMING quotes for each pattern — one honest counter-example in the report beats ten confirmations
- Divergent answers may mean segmentation, not noise — say which
4. Verdicts to the registry
Map each pattern onto open hypotheses:
hypotheses.py set <id> --status confirmed|refuted|testing --type REAL --confidence 0.9 --add-source "research/interview-notes.md::N of M participants, codes: <...>". Confidence scales with N/M and segment match
(0.9–1.0 full batch on-segment; 0.6–0.7 thin or mixed). Patterns matching
no hypothesis → candidate hypotheses.py add (ask the PM). Then
hypotheses.py validate && hypotheses.py render.
5. Deliver
Synthesis to research/interview-notes.md (append section): patterns with
counts and verbatim quotes (marked per participant), disconfirming
evidence, what interviews CANNOT tell us (quantification → analytics),
registry changes made. Update decisions.md. If the batch was synthetic
(personas, not people) — this skill does not apply; that's SYNTHETIC 0.2–0.4
territory in step 2.
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.
- 12d ago First seen · 57 lines · 73 tokens per session scan A c183e0116a58
interview-analysis is a skill published in the GitHub repository lenar-amirov/product-pipeline-public (12 stars, last pushed 22d ago), licensed MIT. It adds 73 tokens to every session and 634 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 2026-08-30.
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