openai-assistants▌
jezweb/claude-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Build stateful chatbots with OpenAI Assistants API v2, including Code Interpreter, File Search (10k files), and Function Calling.
- ›Three core tools: Code Interpreter for Python execution and file processing, File Search for semantic RAG with vector stores (10,000 files max), and Function Calling for custom tool integration
- ›Manages four main objects—Assistants (configured AI with instructions), Threads (persistent conversation containers), Messages (with file attachments), and Runs (async
OpenAI Assistants API v2
Status: Production Ready (⚠️ Deprecated - Sunset August 26, 2026) Package: [email protected] Last Updated: 2026-01-21 v1 Deprecated: December 18, 2024 v2 Sunset: August 26, 2026 (migrate to Responses API)
⚠️ Deprecation Notice
OpenAI is deprecating Assistants API in favor of Responses API.
Timeline: v1 deprecated Dec 18, 2024 | v2 sunset August 26, 2026
Use this skill if: Maintaining legacy apps or migrating existing code (12-18 month window)
Don't use if: Starting new projects (use openai-responses skill instead)
Migration: See references/migration-to-responses.md
Quick Start
npm install [email protected]
import OpenAI from 'openai';
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
// 1. Create assistant
const assistant = await openai.beta.assistants.create({
name: "Math Tutor",
instructions: "You are a math tutor. Use code interpreter for calculations.",
tools: [{ type: "code_interpreter" }],
model: "gpt-5",
});
// 2. Create thread
const thread = await openai.beta.threads.create();
// 3. Add message
await openai.beta.threads.messages.create(thread.id, {
role: "user",
content: "Solve: 3x + 11 = 14",
});
// 4. Run assistant
const run = await openai.beta.threads.runs.create(thread.id, {
assistant_id: assistant.id,
});
// 5. Poll for completion
let status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
while (status.status !== 'completed') {
await new Promise(r => setTimeout(r, 1000));
status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
}
// 6. Get response
const messages = await openai.beta.threads.messages.list(thread.id);
console.log(messages.data[0].content[0].text.value);
Core Concepts
Four Main Objects:
- Assistants: Configured AI with instructions (max 256k chars in v2, was 32k in v1), model, tools, metadata
- Threads: Conversation containers with persistent message history (max 100k messages)
- Messages: User/assistant messages with optional file attachments
- Runs: Async execution with states (queued, in_progress, requires_action, completed, failed, expired)
Key API Patterns
Assistants
const assistant = await openai.beta.assistants.create({
model: "gpt-5",
instructions: "System prompt (max 256k chars in v2)",
tools: [{ type: "code_interpreter" }, { type: "file_search" }],
tool_resources: { file_search: { vector_store_ids: ["vs_123"] } },
});
Key Limits: 256k instruction chars (v2), 128 tools max, 16 metadata pairs
Threads & Messages
// Create thread with messages
const thread = await openai.beta.threads.create({
messages: [{ role: "user", content: "Hello" }],
});
// Add message with attachments
await openai.beta.threads.messages.create(thread.id, {
role: "user",
content: "Analyze this",
attachments: [{ file_id: "file_123", tools: [{ type: "code_interpreter" }] }],
});
// List messages
const msgs = await openai.beta.threads.messages.list(thread.id);
Key Limits: 100k messages per thread
Runs
// Create run with optional overrides
const run = await openai.beta.threads.runs.create(thread.id, {
assistant_id: "asst_123",
additional_messages: [{ role: "user", content: "Question" }],
max_prompt_tokens: 1000,
max_completion_tokens: 500,
});
// Poll until complete
let status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
while (['queued', 'in_progress'].includes(status.status)) {
await new Promise(r => setTimeout(r, 1000));
status = await openai.beta.threads.runs.retrieve(thread.id, run.id);
}
Run States: queued → in_progress → requires_action (function calling) / completed / failed / cancelled / expired (10 min max)
Streaming
const stream = await openai.beta.threads.runs.stream(thread.id, { assistant_id });
for await (const event of stream) {
if (event.event === 'thread.message.delta') {
process.stdout.write(event.data.delta.content?.[0]?.text?.value || '');
}
}
Key Events: thread.run.created, thread.message.delta (streaming co
How to use openai-assistants on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add openai-assistants
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches openai-assistants from GitHub repository jezweb/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate openai-assistants. Access the skill through slash commands (e.g., /openai-assistants) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★38 reviews- ★★★★★Camila Taylor· Dec 28, 2024
openai-assistants reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Camila Sethi· Dec 24, 2024
openai-assistants is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Shikha Mishra· Dec 12, 2024
Solid pick for teams standardizing on skills: openai-assistants is focused, and the summary matches what you get after install.
- ★★★★★Lucas Martinez· Dec 4, 2024
We added openai-assistants from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ava Park· Nov 23, 2024
Solid pick for teams standardizing on skills: openai-assistants is focused, and the summary matches what you get after install.
- ★★★★★Anaya Thompson· Nov 19, 2024
I recommend openai-assistants for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Yash Thakker· Nov 3, 2024
We added openai-assistants from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Dhruvi Jain· Oct 22, 2024
openai-assistants fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Ndlovu· Oct 14, 2024
openai-assistants has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Mei Ndlovu· Oct 10, 2024
Useful defaults in openai-assistants — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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