Agent skill

scillm

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.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/scillm-grahama1970-agent-skills

Metadata

Additional technical details for this skill

short description
scillm (VLM, text batch, Lean4 proofs)

SKILL.md

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.

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