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 Jignesh-Ponamwar/skills-mcp --skill openai-apigit clone --depth 1 https://github.com/Jignesh-Ponamwar/skills-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/jignesh-ponamwar/skills-mcp/openai-api)<a href="https://agentmods.dev/skills/jignesh-ponamwar/skills-mcp/openai-api"><img src="https://agentmods.dev/badge/skills/jignesh-ponamwar/skills-mcp/openai-api/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/jignesh-ponamwar/skills-mcp/openai-api"><img src="https://agentmods.dev/badge/skills/jignesh-ponamwar/skills-mcp/openai-api.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 | $0.00110 | $0.02406 |
| Opus 5 | $0.00055 | $0.01203 |
| Sonnet 5 | $0.00022 | $0.00481 |
| Haiku 4.5 | $0.00011 | $0.00241 |
Grade A, and why
openai-api 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 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.
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 — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenAI API Skill
Step 1: Choose the Right Model
| Model | Best For | Context |
|---|---|---|
gpt-4o |
Multimodal (text + vision), balanced | 128K tokens |
gpt-4o-mini |
Fast, cheap, most tasks | 128K tokens |
o3 |
Complex multi-step reasoning | 200K tokens |
o4-mini |
Efficient reasoning at low cost | 200K tokens |
text-embedding-3-large |
High-quality embeddings (3072-dim) | - |
text-embedding-3-small |
Affordable embeddings (1536-dim) | - |
dall-e-3 |
High-quality image generation | - |
whisper-1 |
Audio transcription | - |
tts-1-hd |
High-quality text-to-speech | - |
Step 2: Setup
pip install openai # Python
npm install openai # Node.js / TypeScript
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) # never hardcode
import OpenAI from 'openai'
const client = new OpenAI() // reads OPENAI_API_KEY from env automatically
Step 3: Chat Completions
# Python
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain recursion with an example."},
],
temperature=0.7,
max_tokens=1024,
)
print(response.choices[0].message.content)
// TypeScript
const response = await client.chat.completions.create({
model: 'gpt-4o',
messages: [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: 'Explain recursion with an example.' },
],
temperature: 0.7,
max_tokens: 1024,
})
console.log(response.choices[0].message.content)
Step 4: Streaming
# Python
with client.chat.completions.stream(
model="gpt-4o",
messages=[{"role": "user", "content": "Write a poem about the sea"}],
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)
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 · 355 lines · 110 tokens per session scan A b5b50a08e7af
openai-api is a skill published in the GitHub repository Jignesh-Ponamwar/skills-mcp (7 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 110 tokens to every session and 2,406 once invoked, about $0.0006 per session on Opus 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 2026-08-31.
Other skills, from other repositories
feature-engineering
When building training datasets, designing feature pipelines, or debugging training-serving skew.
streaming-patterns
When designing Kafka consumers/producers or implementing real-time pipelines.
inference-serving
When deploying a model to an API endpoint or optimizing inference latency.
gemini-api-integration
Use when integrating Google Gemini API into projects. Covers model selection, multimodal inputs, streaming, function calling, and production best practices.
gemini-api-dev
The Gemini API provides access to Google's most advanced AI models. Key capabilities include.
llm-gateway-routing
Canonical LLM gateway/routing/proxy playbook: multi-provider failover, token reduction proxies, and cost wrappers.