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 skills/centminmod/my-claude-code-setup/consult-codexnpx skills add centminmod/my-claude-code-setup --skill consult-codexgit clone --depth 1 https://github.com/centminmod/my-claude-code-setupWhat 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 | $0.00044 | $0.05034 |
| Opus 5 | $0.00022 | $0.02517 |
| Sonnet 5 | $0.00009 | $0.01007 |
| Haiku 4.5 | $0.00004 | $0.00503 |
Grade B, and why
consult-codex 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 3d 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.
(`~/.codex/config.toml` — this installation is configured for `gpt-5.6-sol`, high effort). Copies of this mod
1 near-identical copy found in the catalogue:
- consult-zai — 88% identical, 313 lines differ
How it starts
The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dual-AI Consultation: Codex vs Code-Searcher
You orchestrate consultation between OpenAI's Codex and Claude's code-searcher to provide comprehensive analysis with comparison.
When to Use This Skill
High value queries:
- Complex code analysis requiring multiple perspectives
- Debugging difficult issues
- Architecture/design questions
- Code review requests
- Finding specific implementations across a codebase
Lower value (single AI may suffice):
- Simple syntax questions
- Basic file lookups
- Straightforward documentation queries
Workflow
When the user asks a code question:
1. Build Enhanced Prompt
Problem-restate pre-flight (non-blocking). Before building the prompt, emit ONE line restating the code question you are about to dispatch (and, only if genuinely ambiguous, the alternative reading), then proceed:
Reading this as: «one-line restatement» (alt: «other reading», if any) — proceeding to consult; interrupt now to correct the framing.
Emit-and-proceed — do not ask-and-wait (the orchestrator can't reliably detect its own misframing). One line, and it guards the whole dispatch against a wrong-framing run.
Wrap the user's question with structured output requirements:
[USER_QUESTION]
=== Analysis Guidelines ===
**Structure your response with:**
1. **Summary:** 2-3 sentence overview
2. **Key Findings:** bullet points of discoveries
3. **Evidence:** file paths with line numbers (format: `file:line` or `file:start-end`)
4. **Confidence:** High/Medium/Low with reasoning
5. **Limitations:** what couldn't be determined
**Line Number Requirements:**
- ALWAYS include specific line numbers when referencing code
- Use format: `path/to/file.ext:42` or `path/to/file.ext:42-58`
- For multiple references: list each on a SEPARATE line with its own file path
(avoid comma-separated multi-citation like `file.ts:45, 67, 98`)
- Include brief code snippets for key findings
**Examples of good citations:**
- "The authentication check at `src/auth/validate.ts:127-134`"
- "Configuration loaded from `config/settings.json:15`"
- "Error handling in `lib/errors.ts:45`, `lib/errors.ts:67-72`, and `lib/errors.ts:98`"
**Citations Index (required):** end your response with a fenced block, one line per
Key Finding (repeat each block entry's `file:line` inline in the finding as usual):
```citations
<finding #> — path/to/file.ext:LINE[-END]
```
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.
- 3d ago First seen · 395 lines · 44 tokens per session scan B 877b23ca8f0f
consult-codex is a skill published in the GitHub repository centminmod/my-claude-code-setup (2,614 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 5,034 once invoked, about $0.0002 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.
Other skills, from other repositories
memory-bank
Token-efficient persistent memory system for Claude Code that saves 67% tokens on session warm-up (verified with tiktoken). Layered architecture with progressive loading, compact encoding, branch-aware context, smart compression, session diffing, conflict detection, session continuation protocol, and recovery mode.…
building-chatgpt-apps
Guides creation of ChatGPT Apps with interactive widgets using OpenAI Apps SDK and MCP servers. Use when building ChatGPT custom apps with visual UI components, embedded widgets, or rich interactive experiences. Covers widget architecture, MCP server setup with FastMCP, response metadata, and Developer Mode…
memory-systems
Design and implement memory architectures for agent systems. Use when building agents that need to persist state across sessions, maintain entity consistency, or reason over structured knowledge.
multi-agent-patterns
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.
scaffolding-openai-agents
Builds AI agents using OpenAI Agents SDK with async/await patterns and multi-agent orchestration. Use when creating tutoring agents, building agent handoffs, implementing tool-calling agents, or orchestrating multiple specialists. Covers Agent class, Runner patterns, function tools, guardrails, and streaming…
tool-design
Design tools that agents can use effectively. Use when creating new tools for agents, debugging tool-related failures, or optimizing existing tool sets.