Back to skills
SKILL.md
Scillm
ASecurityLLM completions (text and VLM) via scillm/Chutes.ai. Two main patterns: (1) VLM for image/figure/table description, (2) Text for batch extraction, summarization, JSON extraction. Also supports Lean4 theorem proving.
- 666 stars
- 0 votes
- 0 copies
- 2 views
- Added September 2, 2026
Works with
Security analysis
100/100npx -y skills add majiayu000/claude-skill-registry --skill scillm --agent claude-codeAre you the author of Scillm?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/majiayu000-scillm-claude-skill-registry)---
name: scillm
description: >
LLM completions (text and VLM) via scillm/Chutes.ai. Two main patterns:
(1) VLM for image/figure/table description,
(2) Text for batch extraction, summarization, JSON extraction.
Also supports Lean4 theorem proving.
allowed-tools: Bash, Read
triggers:
- batch LLM calls
- parallel completions
- describe image
- describe figure
- describe table
- VLM call
- multimodal
- prove mathematically
- formal verification
- Lean4 proof
- extract JSON from
- verify this claim
metadata:
short-description: scillm (VLM, text batch, Lean4 proofs)
---
# 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
```bash
# Describe an image
python .agents/skills/scillm/vlm.py describe /path/to/image.png
# With custom prompt
python .agents/skills/scillm/vlm.py describe /path/to/image.png --prompt "What table headers do you see?"
# JSON output
python .agents/skills/scillm/vlm.py describe /path/to/image.png --json
# Batch describe images
python .agents/skills/scillm/vlm.py batch --input images.jsonl
```
### Commands
**Describe single image:**
```bash
python .agents/skills/scillm/vlm.py describe <image> [--prompt PROMPT] [--json] [--model MODEL]
```
**Batch describe:**
```bash
python .agents/skills/scillm/vlm.py batch \
--input images.jsonl \
--output results.jsonl \
--concurrency 6
```
### Input Format (Batch)
JSONL with image paths:
```json
{"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
```bash
# Single completion
python .agents/skills/scillm/batch.py single "What is 2+2?"
# Single with JSON response
python .agents/skills/scillm/batch.py single "Return {answer: number}" --json
# Batch from file
python .agents/skills/scillm/batch.py batch --input prompts.jsonl --json
```
### Commands
**Single completion:**
```bash
python .agents/skills/scillm/batch.py single "Your prompt" [--json] [--model MODEL]
```
**Batch completions:**
```bash
python .agents/skills/scillm/batch.py batch \
--input prompts.jsonl \
--output results.jsonl \
--json \
--concurrency 6
```
### Input/Output Format
Input JSONL (one per line):
```json
{"prompt": "Summarize..."}
{"prompt": "Translate..."}
```
Output JSONL:
```json
{"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
```bash
# Prove a claim
python .agents/skills/scillm/prove.py "Prove that n + 0 = n"
# With tactic hints
python .agents/skills/scillm/prove.py "Prove n < n + 1" --tactics omega
# Check availability
python .agents/skills/scillm/prove.py --check
```
### Commands
**Prove a claim:**
```bash
python .agents/skills/scillm/prove.py "Your claim" [--tactics simp,omega] [--timeout 120]
```
**Check if ready:**
```bash
python .agents/skills/scillm/prove.py --check
```
### Output Format
**Success:**
```json
{
"ok": true,
"lean4_code": "theorem add_zero (n : ℕ) : n + 0 = n := by simp",
"compile_ms": 7406
}
```
**Failure:**
```json
{
"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
1. **lean_runner container** running
2. **OPENROUTER_API_KEY** set
3. **scillm[certainly]** installed
---
## Importable API (For Other Skills)
The `quick_completion` function can be imported by sibling skills:
```python
# 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 |
---
## Python API (Direct scillm)
For more control, use scillm directly:
```python
# Single completion (for one-off calls)
from scillm import acompletion
resp = await acompletion(model=..., messages=[...], api_base=..., api_key=...)
# Batch completions (for parallel processing)
from scillm import parallel_acompletions
reqs = [{"model": MODEL, "messages": [...]}]
results = await parallel_acompletions(reqs, api_base=..., api_key=...)
# Lean4 proofs
from scillm.integrations.certainly import prove_requirement
result = await prove_requirement("Prove n + 0 = n", tactics=["simp"])
```
See SCILLM_PAVED_PATH_CONTRACT.md for full reference.
Attribution
Comments
Loading comments…
Newsletter
Get the weekly list of safe new skills, and the ones we pulled.