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 naimkatiman/continuous-improvement --skill deep-interviewgit clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/deep-interview)<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/deep-interview"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/deep-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/naimkatiman/continuous-improvement/deep-interview"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/deep-interview.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00016 | $0.10798 |
| Opus 5.5 | $0.00006 | $0.04319 |
| Sonnet 5.5 | $0.00003 | $0.02160 |
| Haiku 4.5 | $0.00002 | $0.01080 |
Grade B, and why
deep-interview scanned grade B with 1 finding 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
- Project settings: `./.claude/settings.json` (overrides user settings) How it starts
The opening of the file, as written. The whole thing — 803 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Use_When>
- User has a vague idea and wants thorough requirements gathering before execution
- User says "deep interview", "interview me", "ask me everything", "don't assume", "make sure you understand"
- User says "ouroboros", "socratic", "I have a vague idea", "not sure exactly what I want"
- User wants to avoid "that's not what I meant" outcomes from autonomous execution
- Task is complex enough that jumping to code would waste cycles on scope discovery
- User wants mathematically-validated clarity before committing to execution </Use_When>
<Do_Not_Use_When>
- User has a detailed, specific request with file paths, function names, or acceptance criteria -- execute directly
- User wants to explore options or brainstorm -- use
omc-planskill instead - User wants a quick fix or single change -- delegate to executor or ralph
- User says "just do it" or "skip the questions" without an explicit execution path -- respect their intent by ending interview and writing a
pending approvalspec, not by mutating files or delegating execution - User already has a PRD or plan file and explicitly asks to execute it -- use the requested execution skill with that plan </Do_Not_Use_When>
<Why_This_Exists> AI can build anything. The hard part is knowing what to build. OMC's autopilot Phase 0 expands ideas into specs via analyst + architect, but this single-pass approach struggles with genuinely vague inputs. It asks "what do you want?" instead of "what are you assuming?" Deep Interview applies Socratic methodology to iteratively expose assumptions and mathematically gate readiness, ensuring the AI has genuine clarity before spending execution cycles.
Inspired by the Ouroboros project which demonstrated that specification quality is the primary bottleneck in AI-assisted development. </Why_This_Exists>
<Execution_Policy>
- Ask ONE question at a time -- never batch multiple questions
- Target the WEAKEST clarity dimension with each question
- Before Round 1 ambiguity scoring, run a one-time Round 0 topology enumeration gate that confirms the top-level component list and locks it into state
- Make weakest-dimension targeting explicit every round: name the weakest dimension, state its score/gap, and explain why the next question is aimed there
- Gather codebase facts via
exploreagent BEFORE asking the user about them - For brownfield confirmation questions, cite the repo evidence that triggered the question (file path, symbol, or pattern) instead of asking the user to rediscover it
- Score ambiguity after every answer -- display the score transparently
- When the locked topology has multiple active components, score and target each component explicitly so depth-first clarity on one component cannot hide ambiguity in siblings
- Keep prompt payloads budgeted: summarize or trim oversized initial context/history before composing question, scoring, spec, or handoff prompts
- If the user's initial context is oversized, create a concise prompt-safe summary first and wait for that summary before ambiguity scoring, question generation, or downstream execution handoff
- Do not proceed to execution until ambiguity ≤ the resolved threshold for this run and the user explicitly approves a scoped execution path
- Allow early exit with a clear warning if ambiguity is still high
- Persist interview state for resume across session interruptions
- Challenge agents activate at specific round thresholds to shift perspective </Execution_Policy>
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 Changed · +124 lines · +1 tokens per session d897923d4c15
- 1mo ago First seen · 679 lines · 15 tokens per session scan B 525b4d298b29
deep-interview is a skill published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 10,798 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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