Direct code generation via hosted LLM (Cerebras). Write a contract prompt, generate code, fix surgically. Part of speed-run pipeline.
Generates code files via hosted LLM using detailed contract prompts for rapid, multi-file implementation.
npx claudepluginhub 2389-research/claude-pluginsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
Direct code generation via hosted LLM. Claude writes the contract, Cerebras implements the code, files are written directly to disk.
Announce: "I'm using speed-run:turbo for hosted code generation."
Use turbo for:
Use Claude direct instead for:
| Aspect | Claude Direct | Turbo (Hosted LLM) |
|---|---|---|
| Speed | ~10s | ~0.5s |
| Token Cost | Higher | ~90% savings |
| First-pass Quality | ~100% | 80-95% |
| Fixes Needed | 0 | 0-2 typical |
Structure your prompt with exact specifications:
Build [X] with [tech stack].
## DATA CONTRACT (use exactly these models):
[Pydantic models / interfaces with exact field names and types]
Example:
class Task(BaseModel):
id: str
title: str
completed: bool = False
created_at: datetime
class TaskCreate(BaseModel):
title: str
## API CONTRACT (use exactly these routes):
POST /tasks -> Task # Create task
GET /tasks -> list[Task] # List all tasks
GET /tasks/{id} -> Task # Get single task
DELETE /tasks/{id} -> dict # Delete task
POST /reset -> dict # Reset state (for testing)
## ALGORITHM:
1. [Step-by-step logic for the implementation]
2. [Include state management details]
3. [Include edge case handling]
## RULES:
- Use FastAPI with uvicorn
- Store data in [storage mechanism]
- Return 404 for missing resources
- POST /reset must clear all state and return {"status": "ok"}
mcp__speed-run__generate_and_write_files
prompt: [contract prompt]
output_dir: [target directory]
Returns only metadata (files written, line counts). Claude never sees the generated code.
Run the test suite against generated code.
For failures, use Claude Edit tool for surgical fixes (typically 1-4 lines each).
Common fixes:
| Error Type | Frequency | Fix Complexity |
|---|---|---|
| Missing utility functions | Occasional | 4 lines |
| Logic edge cases | Occasional | 1-2 lines |
| Import ordering | Rare | 1 line |
Repeat Steps 3-4 until all tests pass. Even with fixes, total token cost is much lower than Claude generating everything.
| Variable | Default | Description |
|---|---|---|
CEREBRAS_API_KEY | (required) | Your API key |
CEREBRAS_MODEL | gpt-oss-120b | Model to use |
Available models:
| Model | Price (in/out) | Speed | Notes |
|---|---|---|---|
gpt-oss-120b | $0.35/$0.75 | 3000 t/s | Default - best value, clean output |
llama-3.3-70b | $0.85/$1.20 | 2100 t/s | Reliable fallback |
qwen-3-32b | $0.40/$0.80 | 2600 t/s | Has verbose <think> tags |
llama3.1-8b | $0.10/$0.10 | 2200 t/s | Cheapest, may need more fixes |
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