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 SkillMedev/course-creator-launch-system --skill testimonial-capture-interviewgit clone --depth 1 https://github.com/SkillMedev/course-creator-launch-systemWrote 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/skillmedev/course-creator-launch-system/testimonial-capture-interview)<a href="https://agentmods.dev/skills/skillmedev/course-creator-launch-system/testimonial-capture-interview"><img src="https://agentmods.dev/badge/skills/skillmedev/course-creator-launch-system/testimonial-capture-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/skillmedev/course-creator-launch-system/testimonial-capture-interview"><img src="https://agentmods.dev/badge/skills/skillmedev/course-creator-launch-system/testimonial-capture-interview.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 | $0.00159 | $0.02427 |
| Opus 5.5 | $0.00064 | $0.00971 |
| Sonnet 5.5 | $0.00032 | $0.00485 |
| Haiku 4.5 | $0.00016 | $0.00243 |
Grade A, and why
testimonial-capture-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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- testimonial-capture-interview — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testimonial Capture Interview
A testimonial that says "great product, great team!" persuades no one, and it is not the customer's fault - it is the interviewer's, because vague questions produce vague praise. The costly mistake this skill prevents is burning a willing customer's only interview slot on questions that yield nothing quotable, then either publishing mush or ghost-writing a quote the customer never said. A well-sequenced interview reliably extracts the before/after story with numbers, and the ethics workflow makes every published word both true and approved.
Operating procedure
The sequence matters most inside the interview itself (Step 3): specifics come before feelings because memory works that way - a customer asked "how do you feel about the product" gives adjectives, but a customer walked through their old workflow first gives numbers, and the feelings that follow attach to those numbers.
Step 1: Select and brief the customer
Pick customers with a measurable outcome and at least 90 days of usage - earlier than that, the "after" hasn't stabilized and the numbers won't survive scrutiny. Send a short brief when scheduling: the purpose (a story about their results, in their words), the time ask (30 minutes), the control they keep (nothing publishes without their written approval of the exact words), and a request to have any relevant numbers handy. The approval promise up front measurably improves both acceptance and candor.
Step 2: Prepare the specifics you already know
Before the call, pull what the account already shows - usage data, support history, the original sales notes on why they bought. Interviewing from zero wastes half the slot on facts you had; interviewing from data lets you ask "your team went from 40 to 300 workflows in Q2 - what happened there?", which is where good soundbites come from.
Step 3: Run the question sequence
Record the call (with consent, stated at the start - recording consent is separate from publication consent and both are required). Then run five phases in order:
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.
- 1mo ago First seen · 133 lines · 159 tokens per session scan A a03118d6f6ac
testimonial-capture-interview is a skill published in the GitHub repository SkillMedev/course-creator-launch-system (1 stars, last pushed 23d ago), licensed MIT. It adds 159 tokens to every session and 2,427 once invoked, about $0.0006 per session on Opus 5.5. 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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