AI coding agents like Cursor and Claude Code can use your Pioneer account directly — starting training jobs, checking model status, running inference, and managing datasets — if you give them the right context. Agent Skills is a SKILL.md file that provides a coding agent with complete, structured knowledge of the Pioneer API. Once installed, your agent can handle Pioneer tasks end-to-end without you needing to look up endpoints or copy-paste API keys.
How to install
Copy the SKILL.md content
Copy the full SKILL.md content from the code block in the section below.
Save the file to your project
Save the file to .claude/skills/pioneer-api/SKILL.md in your project root. This is the standard location for Claude Code skills. If you use a different agent, check its documentation for where to place skill files.your-project/
└── .claude/
└── skills/
└── pioneer-api/
└── SKILL.md
Generate your API key
Go to pioneer.ai → Settings → API Keys and create a new key. Give it a name that identifies the project or agent using it. Set your API key as an environment variable
Set PIONEER_API_KEY (or whichever variable name your agent reads) in the environment your agent runs in. For example:export PIONEER_API_KEY="your-api-key-here"
Never commit your API key to version control. Add .env to your .gitignore and use environment variable injection in CI/CD environments.
SKILL.md content
Copy this entire file into .claude/skills/pioneer-api/SKILL.md:
---
name: pioneer-api
description: Interact with the Pioneer API to manage datasets, training jobs, evaluations, and run inference. Use when the user wants to call the Pioneer API, manage ML models, start training runs, run inference, or integrate Pioneer into their workflow.
---
# Pioneer API
Base URL: https://api.pioneer.ai
Auth header: X-API-Key: YOUR_API_KEY
Get your API key: pioneer.ai → Settings → API Keys.
## Inference (Pioneer format)
POST /inference — run predictions against a fine-tuned or base model
GET /base-models — list available models (supports ?supports_inference=true&task_type=decoder filters)
```bash
curl -X POST https://api.pioneer.ai/inference \
-H "X-API-Key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model_id": "job_abc123",
"text": "Apple announced the MacBook Pro at WWDC in Cupertino.",
"schema": {
"entities": ["organization", "product", "event", "location"]
},
"threshold": 0.5
}'
```
The schema is a dict with optional keys:
- entities — list of entity type strings (NER)
- classifications — list of {task, labels} objects (text classification)
- structures — dict of structure definitions (JSON extraction)
- relations — list of relation definitions
For decoder models, use "task": "generate" with a "messages" array instead of "text" and "schema":
{
"model_id": "job_abc123",
"task": "generate",
"messages": [{"role": "user", "content": "Summarize this: ..."}]
}
model_id is the job_id returned from POST /felix/training-jobs,
or a base model ID like "fastino/gliner2-base-v1".
## Inference (OpenAI-compatible)
POST /v1/chat/completions — OpenAI-compatible chat completions
POST /v1/completions — OpenAI-compatible text completions
```bash
curl -X POST https://api.pioneer.ai/v1/chat/completions \
-H "X-API-Key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "job_abc123",
"messages": [{"role": "user", "content": "Extract entities from: Apple launched the iPhone."}],
"schema": {"entities": ["organization", "product"]}
}'
```
## Inference (Anthropic-compatible)
POST /v1/messages — Anthropic-compatible messages
```bash
curl -X POST https://api.pioneer.ai/v1/messages \
-H "X-API-Key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "job_abc123",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Extract entities from: Apple launched the iPhone."}],
"schema": {"entities": ["organization", "product"]}
}'
```
## Inference History
GET /inferences — list past inferences
GET /inferences/:id — get a specific inference result
POST /inferences/:id/feedback — submit feedback on a result
## Datasets
GET /felix/datasets — list all datasets
GET /felix/datasets/:name — get dataset details
DELETE /felix/datasets/:name — delete a dataset
```bash
curl https://api.pioneer.ai/felix/datasets \
-H "X-API-Key: YOUR_API_KEY"
```
## Training Jobs
POST /felix/training-jobs — start a training job
GET /felix/training-jobs — list training jobs
GET /felix/training-jobs/:id — get job status and metrics
POST /felix/training-jobs/:id/stop — stop a running job
```bash
curl -X POST https://api.pioneer.ai/felix/training-jobs \
-H "X-API-Key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model_name": "my-ner-model",
"base_model": "fastino/gliner2-base-v1",
"datasets": [{"name": "my-dataset"}],
"training_type": "lora",
"nr_epochs": 5,
"learning_rate": 5e-5
}'
```
base_model is required. Valid values: a supported model ID returned by GET /base-models
(e.g. fastino/gliner2-base-v1, Qwen/Qwen3-8B, meta-llama/Llama-3.1-8B-Instruct)
or a checkpoint UUID from a previous training job.
Response: { "id": "uuid-of-training-job", "status": "requested" }
Job status values: requested | running | complete | failed | stopped
Metrics (on COMPLETED): { "f1": 0.94, "precision": 0.96, "recall": 0.92 }
## Evaluations
POST /felix/evaluations — run an evaluation
GET /felix/evaluations — list evaluations
GET /felix/evaluations/:id — get evaluation results
```bash
curl -X POST https://api.pioneer.ai/felix/evaluations \
-H "X-API-Key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"base_model": "job_abc123",
"dataset_name": "my-eval-dataset"
}'
```
Results include: f1, precision, recall, per_entity breakdown
## Errors
401 — invalid or missing API key
402 — insufficient credits
404 — resource not found
422 — validation error (check request body)
500 — server error
Next steps
