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 getcrew44/crew44 --skill discovery-interview-prepgit clone --depth 1 https://github.com/getcrew44/crew44Wrote 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/getcrew44/crew44/discovery-interview-prep)<a href="https://agentmods.dev/skills/getcrew44/crew44/discovery-interview-prep"><img src="https://agentmods.dev/badge/skills/getcrew44/crew44/discovery-interview-prep/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/getcrew44/crew44/discovery-interview-prep"><img src="https://agentmods.dev/badge/skills/getcrew44/crew44/discovery-interview-prep.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 | $0.00039 | $0.04064 |
| Opus 5.5 | $0.00016 | $0.01626 |
| Sonnet 5.5 | $0.00008 | $0.00813 |
| Haiku 4.5 | $0.00004 | $0.00406 |
Grade A, and why
discovery-interview-prep 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.
How it starts
The opening of the file, as written. The whole thing — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Guide product managers through preparing for customer discovery interviews by asking adaptive questions about research goals, customer segments, constraints, and methodologies. Use this to design effective interview plans, craft targeted questions, avoid common biases, and maximize learning from limited customer access—ensuring discovery interviews yield actionable insights rather than confirmation bias or surface-level feedback.
This is not a script generator—it's a strategic prep process that outputs a tailored interview plan with methodology, question framework, and success criteria.
Key Concepts
The Discovery Interview Prep Flow
An interactive process that:
- Gathers product/problem context (marketing materials, assumptions)
- Defines research goals (what you're trying to learn)
- Identifies target customer segment and access constraints
- Recommends interview methodology (Jobs-to-be-Done, problem validation, switch interviews, etc.)
- Generates interview framework with questions, biases to avoid, and success metrics
Why This Works
- Goal-driven: Aligns interview approach to what you need to learn
- Adaptive: Adjusts methodology based on product stage (idea vs. existing product) and access constraints
- Bias-aware: Highlights common pitfalls (leading questions, confirmation bias, solution-first thinking)
- Actionable: Outputs interview guide ready to use
Anti-Patterns (What This Is NOT)
- Not a user testing script: Discovery = learning problems; testing = validating solutions
- Not a sales demo: Don't pitch—listen and learn
- Not surveys at scale: Deep qualitative interviews (5-10 people), not broad surveys (100+ people)
When to Use This
- Starting product discovery (validating problem space)
- Repositioning an existing product (understanding new market)
- Investigating churn or drop-off (retention interviews)
- Evaluating feature ideas before building
- Preparing for customer development sprints
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 · 411 lines · 39 tokens per session scan A bb3b874041a6
discovery-interview-prep is a skill published in the GitHub repository getcrew44/crew44 (356 stars, last pushed 4mo ago), licensed MIT. It adds 39 tokens to every session and 4,064 once invoked, about $0.0002 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 2026-08-30.
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