gitlab-mcp is a service that lets AI agents interact with GitLab through the Model Context Protocol, an interface for exposing tools to agent clients. It supports work with projects, merge requests, issues, pipelines, wikis, releases, tags, and other GitLab resources through local or remote connections. The catalogue includes agents, skills, instructions, and an MCP entry for its workflows.
Borrowing it
Nothing to install: this file belongs to zereight/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/zereight/gitlab-mcp/main/.github/skills/deep-interview/SKILL.mdgit clone --depth 1 https://github.com/zereight/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/zereight/gitlab-mcp/deep-interview)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/deep-interview"><img src="https://agentmods.dev/badge/skills/zereight/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/zereight/gitlab-mcp/deep-interview"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/deep-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 | $0.00057 | $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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- deep-interview — 100% identical, 0 lines differ
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.
- 1mo ago First seen · 76 lines · 57 tokens per session scan A 16daa80ebb49
deep-interview is a skill published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
gitlab-merge-request-review
Review and merge GitLab merge requests via the gitlab-api MCP server — by default the MRs assigned to you or where you are a reviewer, optionally scoped to a project, group, or the whole instance. Read an MR, gate it on a green pipeline (pipeline → jobs → job log), then approve, accept/merge, or set…
gitlab-pipelines
CI/CD pipeline review on GitLab via the gitlab-api MCP server — review pipeline results for a project, a group, or the whole instance, drill into the CI jobs of a pipeline, and read a job's log; also trigger a pipeline on a ref. Use when the agent must review CI results across a project/group/organization, check…
gitlab-vulnerabilities
Dependency and security-vulnerability review on GitLab via the gitlab-api MCP server — the GitLab counterpart to GitHub Dependabot. Read a project's Dependency List, review a project's or group's security vulnerabilities/findings, and fetch a single vulnerability by ID. Use when the agent must audit a project's…
gitlab-issues
Issue review and tracking on GitLab via the gitlab-api MCP server — review a project's issues, review issues across a whole group/organization, and read, create, update, or close an issue with the domain-typed tool. Use when the agent must triage a project's issues, see every issue affecting a group, open a new issue…
gitlab-merge-request-create
Open a new GitLab merge request against a project via the gitlab-api MCP server — from a source branch into a target branch, with title, description, assignees, reviewers, labels, and remove-source-branch/squash options. Use when the agent must create/open an MR for a project. Do NOT use to review, approve, or merge…
browser-qa
Use when you need lightweight browser QA for a web page, local HTML file, or app: inspect console errors, broken assets, keyboard/focus behavior, viewport readability, and publish evidence-backed findings JSON through a local HTML report viewer.