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 agentmods add skills/nicepkg/ai-workflow/discovery-interviews-surveysnpx skills add nicepkg/ai-workflow --skill discovery-interviews-surveysgit clone --depth 1 https://github.com/nicepkg/ai-workflowWrote 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/nicepkg/ai-workflow/discovery-interviews-surveys)<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/discovery-interviews-surveys"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/discovery-interviews-surveys.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.02672 |
| Opus 5 | $0.00039 | $0.01336 |
| Sonnet 5 | $0.00015 | $0.00534 |
| Haiku 4.5 | $0.00008 | $0.00267 |
Grade A, and why
discovery-interviews-surveys 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 yesterday.
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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discovery Interviews & Surveys
Table of Contents
Purpose
Discovery Interviews & Surveys help you learn from users systematically to:
- Validate assumptions before investing in building
- Discover real problems users experience (not just stated needs)
- Understand jobs-to-be-done (what users "hire" your product to do)
- Identify pain points and current workarounds
- Test concepts and positioning with target audience
- Uncover unmet needs that users may not articulate directly
This moves from guessing to evidence-based product decisions.
When to Use
Use this skill when:
- Pre-build validation: Testing product ideas before development
- Problem discovery: Understanding user pain points and workflows
- Jobs-to-be-done research: Identifying hiring/firing triggers and desired outcomes
- Market research: Understanding target audience, competitive landscape, willingness to pay
- Concept testing: Validating positioning, messaging, feature prioritization
- Post-launch learning: Understanding adoption barriers, churn reasons, expansion opportunities
- Customer satisfaction research: Identifying satisfaction/dissatisfaction drivers
- UX research: Mental models, task flows, usability issues
- Voice of customer: Gathering qualitative insights for roadmap prioritization
Trigger phrases: "user research", "customer interviews", "surveys", "discovery", "validation study", "voice of customer", "jobs-to-be-done", "JTBD", "user needs"
What Is It?
Discovery Interviews & Surveys provide structured approaches to learn from users while avoiding common biases (leading questions, confirmation bias, selection bias).
Key components:
- Interview guides: Open-ended questions that reveal problems and context
- Survey instruments: Scaled questions for quantitative validation at scale
- JTBD probes: Questions focused on hiring/firing triggers and desired outcomes
- Bias-avoidance techniques: Past behavior focus, "show me" requests, avoiding hypotheticals
- Analysis frameworks: Thematic coding, affinity mapping, statistical analysis
What ships with it
3 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.
- yesterday First seen · 211 lines · 77 tokens per session scan A da617d1d2555
discovery-interviews-surveys is a skill published in the GitHub repository nicepkg/ai-workflow (282 stars, last pushed 7mo ago), licensed MIT. It adds 77 tokens to every session and 2,672 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 2026-09-03.
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