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/sananthanarayan/skilldrop/requirements-interviewnpx skills add sananthanarayan/skilldrop --skill requirements-interviewgit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/requirements-interview)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/requirements-interview"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/requirements-interview.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 | $0.00087 | $0.01439 |
| Opus 5 | $0.00044 | $0.00720 |
| Sonnet 5 | $0.00017 | $0.00288 |
| Haiku 4.5 | $0.00009 | $0.00144 |
Grade A, and why
requirements-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 5d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
requirements-interview
Builds the elicitation kit for the conversations before the PRD — so the answers come back in a shape brief-intake and prd-draft can consume, instead of as ten pages of agreement with the roadmap. The discipline: every question names the decision it informs, problems are asked about instead of solutions, and every script tries at least once to kill the feature.
How to respond
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Pin the feature idea and identify the stakeholder cast. From the idea, derive who holds which unknowns — typically: sponsor (success definition, constraints, appetite), end users (the actual workflow and workarounds — interview the people who do the work, not their manager), ops/support (failure modes, ticket reality, operational cost), security/compliance/legal (data constraints, regulatory floor), finance/commercial (pricing, cost ceiling) when money moves. Ask the user at most 2 questions — who's available, and what's already known — then build kits only for stakeholders who hold open unknowns. No interview exists to confirm what's already evidenced.
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Write ≤7 questions per stakeholder, ranked by design impact. Rank 1 is the answer that most changes what gets built. Each question carries two annotations: informs (the decision) and if-answer-is-X-then (how the design moves). A question that wouldn't change anything regardless of answer doesn't make the seven. ✅ "Walk me through the last time you handled a refund request — every tool you touched." (informs: scope of integration; if 3+ tools → unified view is the core, not a nicety) — ❌ "Would a refund dashboard be useful?" (everyone says yes to free things).
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Ask about problems and the past, never solutions and the hypothetical. People are reliable witnesses of what they did and unreliable predictors of what they'd use. ✅ "What did you do the last time X failed?" — ❌ "Would you use a feature that prevents X?". Solutions offered by stakeholders get recorded as evidence of pain, then converted back into the underlying problem.
What ships with it
4 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.
- 5d ago First seen · 62 lines · 87 tokens per session scan A baf80511ff90
requirements-interview is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 21d ago), licensed MIT. It adds 87 tokens to every session and 1,439 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 2026-08-31.
Other skills, from other repositories
openlore-brainstorm
Transform a feature idea into an annotated story using a Domain Sketch or Constrained Option Tree. Use when asked to brainstorm, explore, or shape a feature before implementation.
openlore-execute-refactor
Apply a confirmed .openlore/refactor-plan.md with a test gate after each change. Use when asked to execute or continue an OpenLore refactoring plan.
openlore-plan-refactor
Identify a high-priority refactoring target, assess its blast radius, and write .openlore/refactor-plan.md without changing code. Use when asked to plan or prioritize a refactor.
openlore-debug
Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.
openlore-analyze-codebase
Run a full static OpenLore analysis and summarize architecture, call graph, refactoring issues, and duplicate code. Use when asked to analyze, map, or assess a codebase without LLM inference.
team-repair
Re-index OKF v0.2 index.md/log.md files, derive CDR.md, rebuild .skills.json and AGENTS.md in team-ai-directives, migrate v0.1→v0.2 frontmatter, scan for rule conflicts, and verify directive freshness. Use when indexes are inconsistent, orphans are detected, after bulk changes, or for periodic team AI directives…