Oh My Claude Code is a multi-agent orchestration system for Claude Code, coordinating specialized agents, commands, skills, hooks, and workflows. It is designed for developers who want Claude Code to handle coding tasks through coordinated agent roles. Catalogue entries are components of its Claude Code workflow, including agents, commands, skills, hooks, instructions, MCP configuration, and a plugin.
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 Yeachan-Heo/oh-my-claudecode --skill deep-interviewgit clone --depth 1 https://github.com/Yeachan-Heo/oh-my-claudecodeWrote 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/yeachan-heo/oh-my-claudecode/deep-interview)<a href="https://agentmods.dev/skills/yeachan-heo/oh-my-claudecode/deep-interview"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-claudecode/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/yeachan-heo/oh-my-claudecode/deep-interview"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-claudecode/deep-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 78 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
- high Agent Snooping · line 82 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
- medium Excessive Agency · line 710 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 651 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00008 | $0.05399 |
| Sonnet 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 10d 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) Copies of this mod
1 near-identical copy found in the catalogue:
- deep-interview — 89% identical, 46 lines differ
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.
- 10d ago First seen · 803 lines · 16 tokens per session scan B d897923d4c15
deep-interview is a skill published in the GitHub repository Yeachan-Heo/oh-my-claudecode (39,065 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 10,798 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
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clonedeps
Clone important project dependency source code into an ignored local workspace so OpenCode can inspect library internals. Use when the user asks to clone dependencies, inspect dependency/source internals, understand SDK/framework behavior from source, debug library implementation details, or make core dependency repos…
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Generate comprehensive hierarchical codemaps for UNFAMILIAR repositories. Expensive operation - only use when explicitly asked for codebase documentation or initial repository mapping.
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High-cost orchestrator workflow for large, high-risk, multi-phase coding efforts with meaningful dependencies and review gates. Do not activate for routine multi-file changes.
worktrees
Manage Git worktrees as OMO safe isolated coding lanes for complex, risky, or parallel work.
simplify
Simplifies code for clarity without changing behavior. Use for readability, maintainability, and complexity reduction after behavior is understood.