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 rules/dasomel/oh-my-cursor/deep-interviewgit clone --depth 1 https://github.com/dasomel/oh-my-cursorWhat 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 | $0.00014 | $0.00456 |
| Opus 5 | $0.00007 | $0.00228 |
| Sonnet 5 | $0.00003 | $0.00091 |
| Haiku 4.5 | $0.00001 | $0.00046 |
Grade A, and why
deep-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 2d 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.
What it actually says
Deep Interview Workflow
Socratic deep interview — ask the right questions before writing any code.
Trigger Keywords
"deep interview", "interview me", "understand before coding"
Process
Phase 1: DISCOVERY (3-5 questions)
Ask targeted questions to understand:
- What problem are we solving? (not what solution to build)
- Who is affected?
- What does success look like?
- What constraints exist?
- What has been tried before?
Phase 2: SYNTHESIS
Summarize understanding → .omc-cursor/interview.md
Present summary → "Is this accurate?"
Phase 3: CLARIFICATION (if needed)
Ask 1-2 follow-up questions on ambiguous areas
Phase 4: HANDOFF
Create clear spec → .omc-cursor/spec.md
Recommend workflow: Plan-Execute, Autopilot, or TDD
Question Design Principles
- Open-ended first — "What are you trying to achieve?" not "Do you want X or Y?"
- Problem before solution — Understand the problem before discussing implementation
- One question at a time — Don't overwhelm with multiple questions
- Listen for unstated needs — What they didn't say is often important
Output
# Interview Summary
## Problem Statement
{1-2 sentences describing the core problem}
## Requirements (Confirmed)
- {requirement from user's answers}
## Constraints
- {technical, time, resource constraints}
## Success Criteria
- {measurable outcomes}
## Recommended Approach
{workflow recommendation with reasoning}
## Open Questions
- {anything still unclear}
Rules
- Never start coding during interview — this is discovery only
- Maximum 5 questions in Phase 1 (respect user's time)
- Summarize understanding before proceeding
- If user says "just do it", skip remaining questions and proceed with best understanding
- Announce: "Starting Deep Interview — I'll ask a few questions before we begin."
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.
- 2d ago First seen · 75 lines · 14 tokens per session scan A 69f48fe3b2d7
deep-interview is a cursor rule published in the GitHub repository dasomel/oh-my-cursor (2 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 456 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 2026-08-31.
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