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 jellydn/my-ai-tools --skill spec-interviewgit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/spec-interview)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/spec-interview"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/spec-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/jellydn/my-ai-tools/spec-interview"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/spec-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 | $0.00018 | $0.01829 |
| Opus 5 | $0.00009 | $0.00915 |
| Sonnet 5 | $0.00004 | $0.00366 |
| Haiku 4.5 | $0.00002 | $0.00183 |
Grade A, and why
spec-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 9d 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec Interview
When to Use
Use this skill when:
- Feature requirements are vague or incomplete
- You have a general idea but lack specifics
- Stakeholders said "you know what I mean"
- The spec has obvious gaps
- Making assumptions that could be wrong
What It Does
The agent interviews you to uncover unknown unknowns in your feature specification, focusing on questions that would change architectural decisions.
How to Execute
Step 1: Review Initial Spec
Analyze what the user provided:
- Explicit requirements (what they said)
- Implicit requirements (what they assumed)
- Missing details
- Ambiguous areas
Step 2: Categorize Gaps
Identify question categories:
Scope & Boundaries:
- What's in scope vs out of scope?
- Edge cases to handle?
- MVP vs future iterations?
User Experience:
- What happens when...?
- Error states and recovery?
- Loading and async states?
Technical Decisions:
- Performance requirements?
- Data consistency needs?
- Integration points?
- Security considerations?
Architecture Impact:
- Does this change existing patterns?
- New abstractions needed?
- Migration strategy for existing data?
Step 3: Prioritize Questions
Sort by impact on implementation:
- Architecture-changing: Would change core approach
- High-impact: Significant implementation difference
- Medium-impact: Affects specific modules
- Low-impact: Nice to clarify but not blocking
Step 4: Conduct Interview (One Question at a Time)
Use the ask_user_question tool for each question. Ask one question at a time — present it, wait for the answer, then proceed to the next. This keeps the interview focused and lets the user's answer to one question influence follow-ups.
Guidelines for using ask_user_question:
- Set
headerto a short category label (max 16 chars), e.g."Architecture","Scope","UX","Edge Cases" - Write a clear
questionstring with context about why you're asking - Provide 2-4 concrete
optionswith conciselabel(1-5 words) and descriptivedescriptionexplaining trade-offs - After the user answers, acknowledge the choice and explain how it impacts the implementation before asking the next question
- Architecture-changing questions first, then high-impact, then medium-impact
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.
- 9d ago First seen · 236 lines · 18 tokens per session scan A 371f6c225c08
spec-interview is a skill published in the GitHub repository jellydn/my-ai-tools (119 stars, last pushed 3d ago), licensed MIT. It adds 18 tokens to every session and 1,829 once invoked, about $0.0001 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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