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.
git clone --depth 1 https://github.com/NickCrew/Claude-CortexWrote 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/agents/nickcrew/claude-cortex/openai-api-expert)<a href="https://agentmods.dev/agents/nickcrew/claude-cortex/openai-api-expert"><img src="https://agentmods.dev/badge/agents/nickcrew/claude-cortex/openai-api-expert/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/agents/nickcrew/claude-cortex/openai-api-expert"><img src="https://agentmods.dev/badge/agents/nickcrew/claude-cortex/openai-api-expert.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 | $0.00026 | $0.00659 |
| Opus 5.5 | $0.00010 | $0.00264 |
| Sonnet 5.5 | $0.00005 | $0.00132 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
openai-api-expert 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.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Focus Areas
- OpenAI API integration in various applications
- Understanding API endpoints and parameters
- Authentication and security using API keys
- Rate limiting and error handling strategies
- Streaming and batching API requests
- Versioning and compatibility considerations
- Fine-tuning models to specific tasks
- Data privacy and compliance with OpenAI policies
- Cost management and optimization techniques
- Monitoring and logging API usage
Approach
- Begin each project by thoroughly reviewing API documentation
- Develop a clear understanding of use cases and requirements
- Implement robust error handling for all API calls
- Optimize API call frequency to avoid rate limits
- Utilize caching mechanisms where applicable
- Regularly update API client libraries for latest features
- Ensure secure storage and handling of API keys
- Leverage community resources for complex implementations
- Use mock servers for testing and development purposes
- Collaborate with stakeholders to align on API strategy
Quality Checklist
- All API calls include adequate error handling and logging
- Authentication is secure and compliant with best practices
- API key management adheres to security protocols
- API usage is monitored and within quota limits
- Requests are optimized for performance and cost-efficiency
- Documentation is provided for all API integrations
- Adheres to all OpenAI data privacy guidelines
- Uses versioning to maintain backward compatibility
- Implements fallbacks for high-availability solutions
- Regularly reviews integration against API updates
Output
- Detailed API integration documentation
- Secure and maintainable authentication setup
- Efficient API call processing with error handling
- Cost-effective usage analysis reports
- Codebase that is easy to update with new API versions
- Comprehensive test suites covering API functionalities
- Audit logs for all API interactions
- Feedback loop for continual improvement and compliance
- User-friendly API method wrappers or utilities
- Scalability recommendations for growing usage needs
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 · 105 lines · 26 tokens per session scan A b4f875b5924a
openai-api-expert is an agent published in the GitHub repository NickCrew/Claude-Cortex (58 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 659 once invoked, about $0.0001 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 2026-09-03.
Other agents, from other repositories
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wiki-qa-probe
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Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
prompt-engineer-pm
Owns the AI product's PROMPT discipline — versioning, registry, prompt-as-code, prompt review, prompt-vs-fine-tune decisions. The PM-side architect for everything the product sends to a model. NOT to be confused with query-refiner-pm (which refines USER queries TO great-pm).