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 eugenepyvovarov/mcpbundler-agent-skills-marketplace --skill openai-knowledgegit clone --depth 1 https://github.com/eugenepyvovarov/mcpbundler-agent-skills-marketplaceWrote 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/eugenepyvovarov/mcpbundler-agent-skills-marketplace/openai-knowledge)<a href="https://agentmods.dev/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/openai-knowledge"><img src="https://agentmods.dev/badge/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/openai-knowledge/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/eugenepyvovarov/mcpbundler-agent-skills-marketplace/openai-knowledge"><img src="https://agentmods.dev/badge/skills/eugenepyvovarov/mcpbundler-agent-skills-marketplace/openai-knowledge.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 | $0.00083 | $0.00464 |
| Opus 5.5 | $0.00033 | $0.00186 |
| Sonnet 5.5 | $0.00017 | $0.00093 |
| Haiku 4.5 | $0.00008 | $0.00046 |
Grade B, and why
openai-knowledge 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.
- Config file (`~/.codex/config.toml`): This is a copy
100% identical to openai-knowledge — 2 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.
What it actually says
OpenAI Knowledge
Overview
Use the OpenAI Developer Documentation MCP server to search and fetch exact docs (markdown), then base your answer on that text instead of guessing.
Workflow
1) Check whether the Docs MCP server is available
If the mcp__openaiDeveloperDocs__* tools are available, use them.
If you are unsure, run codex mcp list and check for openaiDeveloperDocs.
2) Use MCP tools to pull exact docs
- Search first, then fetch the specific page or pages.
mcp__openaiDeveloperDocs__search_openai_docs→ pick the best URL.mcp__openaiDeveloperDocs__fetch_openai_doc→ retrieve the exact markdown (optionally with ananchor).
- When you need endpoint schemas or parameters, use:
mcp__openaiDeveloperDocs__get_openapi_specmcp__openaiDeveloperDocs__list_api_endpoints
Base your answer on the fetched text and quote or paraphrase it precisely. Do not invent flags, field names, defaults, or limits.
3) If MCP is not configured, guide setup (do not change config unless asked)
Provide one of these setup options, then ask the user to restart the Codex session so the tools load:
- CLI:
codex mcp add openaiDeveloperDocs --url https://developers.openai.com/mcp
- Config file (
~/.codex/config.toml):- Add:
[mcp_servers.openaiDeveloperDocs] url = "https://developers.openai.com/mcp"
- Add:
Also point to: https://developers.openai.com/resources/docs-mcp#quickstart
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 · 45 lines · 83 tokens per session scan B 13cd63da9145
openai-knowledge is a skill published in the GitHub repository eugenepyvovarov/mcpbundler-agent-skills-marketplace (11 stars, last pushed 8mo ago), licensed MIT. It adds 83 tokens to every session and 464 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). It is 100% identical to openai-knowledge, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
gpt-image-2
A skill for generating or editing images with GPT Image 2 across local, host-provided, or advisory setups.
ml-kit-genai-prompt-api
Analyzes Android codebases to implement ML Kit GenAI Prompt API. Use this skill to send natural language requests on-device to Gemini Nano, use structured output with Prompt API, implement prefix caching, optimize the current prompt, or apply best practices.".