scillm Tools
LLM completions via scillm/Chutes.ai (per SCILLM_PAVED_PATH_CONTRACT.md).
Two Main Patterns
| Pattern | Tool | Model | Use Case |
|---|---|---|---|
| VLM | vlm.py | $CHUTES_VLM_MODEL | Image/figure/table description |
| Text | batch.py | $CHUTES_TEXT_MODEL | Requirements extraction, summarization |
Tools
| Tool | Purpose |
|---|---|
vlm.py | VLM (multimodal) image description |
batch.py | Text LLM completions (single and batch) |
prove.py | Lean4 theorem proving via certainly |
vlm.py - VLM (Multimodal) Completions
Quick Start
# Describe an image
python .pi/skills/scillm/vlm.py describe /path/to/image.png
# With custom prompt
python .pi/skills/scillm/vlm.py describe /path/to/image.png --prompt "What table headers do you see?"
# JSON output
python .pi/skills/scillm/vlm.py describe /path/to/image.png --json
# Batch describe images
python .pi/skills/scillm/vlm.py batch --input images.jsonl
Commands
Describe single image:
python .pi/skills/scillm/vlm.py describe <image> [--prompt PROMPT] [--json] [--model MODEL]
Batch describe:
python .pi/skills/scillm/vlm.py batch \
--input images.jsonl \
--output results.jsonl \
--concurrency 6
Input Format (Batch)
JSONL with image paths:
{"path": "/path/to/image1.png", "prompt": "Describe this table"}
{"path": "/path/to/image2.png"}
Environment Variables
| Variable | Default |
|---|---|
CHUTES_VLM_MODEL | Qwen/Qwen3-VL-235B-A22B-Instruct |
CHUTES_API_BASE | required |
CHUTES_API_KEY | required |
batch.py - LLM Completions
Quick Start
# Single completion
python .pi/skills/scillm/batch.py single "What is 2+2?"
# Single with JSON response
python .pi/skills/scillm/batch.py single "Return {answer: number}" --json
# Batch from file
python .pi/skills/scillm/batch.py batch --input prompts.jsonl --json
Commands
Single completion:
python .pi/skills/scillm/batch.py single "Your prompt" [--json] [--model MODEL]
Batch completions:
python .pi/skills/scillm/batch.py batch \
--input prompts.jsonl \
--output results.jsonl \
--json \
--concurrency 6
Input/Output Format
Input JSONL (one per line):
{"prompt": "Summarize..."}
{"prompt": "Translate..."}
Output JSONL:
{"index": 0, "content": "...", "ok": true}
{"index": 1, "error": "timeout", "status": 408}
Environment Variables
| Variable | Required |
|---|---|
CHUTES_API_BASE | Yes |
CHUTES_API_KEY | Yes |
CHUTES_MODEL_ID | Yes |
prove.py - Lean4 Theorem Proving
Quick Start
# Prove a claim
python .pi/skills/scillm/prove.py "Prove that n + 0 = n"
# With tactic hints
python .pi/skills/scillm/prove.py "Prove n < n + 1" --tactics omega
# Check availability
python .pi/skills/scillm/prove.py --check
Commands
Prove a claim:
python .pi/skills/scillm/prove.py "Your claim" [--tactics simp,omega] [--timeout 120]
Check if ready:
python .pi/skills/scillm/prove.py --check
Output Format
Success:
{
"ok": true,
"lean4_code": "theorem add_zero (n : ℕ) : n + 0 = n := by simp",
"compile_ms": 7406
}
Failure:
{
"ok": false,
"diagnosis": "mathematically false",
"suggestion": "Change to 'Prove that 2 + 2 = 4'"
}
Tactic Hints
| Tactic | Use for |
|---|---|
simp | Identities, simplification |
omega | Integer arithmetic |
ring | Polynomial algebra |
linarith | Linear inequalities |
Prerequisites
- lean_runner container running
- OPENROUTER_API_KEY set
- scillm[certainly] installed
Importable API (For Other Skills)
The quick_completion function can be imported by sibling skills:
# Add scillm to path (for sibling skills)
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "scillm"))
from batch import quick_completion
# Simple completion
result = quick_completion("What is 2+2?")
# With JSON mode
result = quick_completion("Extract {name, age}", json_mode=True)
# With system prompt
result = quick_completion(
prompt="Translate to French: Hello",
system="You are a translator",
temperature=0.3,
)
Parameters:
| Param | Type | Default | Description |
|---|---|---|---|
prompt | str | required | User prompt |
model | str | env var | Model ID |
json_mode | bool | False | Request JSON response |
max_tokens | int | 1024 | Max tokens |
temperature | float | 0.2 | Sampling temperature |
timeout | int | 30 | Request timeout (s) |
system | str | None | System prompt |
Approved Import Surfaces (Proxy-First)
All LLM calls route through the scillm proxy on :4001 for global concurrency/budget control.
# Simple one-off calls (default to proxy automatically)
from scillm.paved import chat, chat_json, analyze_image, analyze_image_json
answer = await chat("What is 2+2?")
data = await chat_json('Return {"name": "Alice", "age": 25}')
desc = await analyze_image("/path/to/image.png", "Describe this")
# Batch fan-out (MUST use proxy api_base)
from scillm.batch import parallel_acompletions_iter
async for ev in parallel_acompletions_iter(
reqs,
api_base=os.environ.get("SCILLM_API_BASE", "http://localhost:4001"),
api_key=os.environ.get("SCILLM_PROXY_KEY", "sk-dev-proxy-123"),
concurrency=6,
):
...
# Lean4 proofs (separate from Chutes, no proxy needed)
from scillm.integrations.certainly import prove_requirement
result = await prove_requirement("Prove n + 0 = n", tactics=["simp"])
Forbidden in downstream projects: Direct acompletion(api_base=CHUTES_API_BASE) calls that bypass the proxy. See SCILLM_PAVED_PATH_CONTRACT.md for full rules.
