Borrowing it
Nothing to install: this file belongs to kirti12025/gitlab-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/kirti12025/gitlab-mcp/custom-tool-pipeline-summary/.github/skills/deep-interview/SKILL.mdgit clone --depth 1 https://github.com/kirti12025/gitlab-mcpWrote 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/kirti12025/gitlab-mcp/deep-interview)<a href="https://agentmods.dev/skills/kirti12025/gitlab-mcp/deep-interview"><img src="https://agentmods.dev/badge/skills/kirti12025/gitlab-mcp/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/kirti12025/gitlab-mcp/deep-interview"><img src="https://agentmods.dev/badge/skills/kirti12025/gitlab-mcp/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 | $0.00057 | $0.00713 |
| Opus 5.5 | $0.00023 | $0.00285 |
| Sonnet 5.5 | $0.00011 | $0.00143 |
| Haiku 4.5 | $0.00006 | $0.00071 |
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 2mo 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.
This is a copy
100% identical to deep-interview — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Interview
Ouroboros-inspired Socratic questioning with mathematical ambiguity scoring. Replaces vague ideas with crystal-clear specifications by asking targeted questions that expose hidden assumptions.
Pipeline
deep-interview → ralplan (consensus refinement) → omg-autopilot (execution)
When to Use
- User has a vague idea and wants thorough requirements gathering
- Task is complex enough that jumping to code would waste cycles
- User wants mathematically-validated clarity before execution
When NOT to Use
- Detailed specific request with file paths → execute directly
- Quick fix → delegate to @executor or
/ralph - User says "just do it" → respect their intent
Phases
Phase 1: Initialize
- Parse the user's idea
- Detect brownfield vs greenfield (use @explore to check codebase)
- For brownfield: map relevant codebase areas
- Initialize ambiguity score at 100%
Phase 2: Interview Loop
Repeat until ambiguity <= 20% or user exits early:
- Generate question targeting the WEAKEST clarity dimension
- Ask ONE question at a time with current ambiguity context
- Score ambiguity across dimensions:
- Goal Clarity (40% weight for greenfield, 35% brownfield)
- Constraint Clarity (30% / 25%)
- Success Criteria (30% / 25%)
- Context Clarity (N/A / 15% for brownfield)
- Report progress with dimension scores and gaps
- Track ontology (key entities, stability ratio)
Phase 3: Challenge Agents
- Round 4+: Contrarian - challenge core assumptions
- Round 6+: Simplifier - probe for complexity removal
- Round 8+: Ontologist - find the essence (if ambiguity > 30%)
Phase 4: Crystallize Spec
When ambiguity <= threshold, generate spec to .omc/specs/deep-interview-{slug}.md:
- Goal, Constraints, Non-Goals, Acceptance Criteria
- Assumptions Exposed & Resolved
- Ontology (Key Entities) with convergence tracking
- Interview Transcript
Phase 5: Execution Bridge
Present options:
- Ralplan → OMG Autopilot (Recommended): consensus-refine then execute
- Execute with omg-autopilot (skip ralplan)
- Execute with ralph: persistence loop
- Execute with team: parallel agents
- Refine further: continue interviewing
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.
- 2mo ago First seen · 76 lines · 57 tokens per session scan A 16daa80ebb49
deep-interview is a skill published in the GitHub repository kirti12025/gitlab-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 57 tokens to every session and 713 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to deep-interview, differing in 0 lines, and is treated as a copy.
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