Agent skill · AI & Agents

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.

majiayu000github.com/majiayu000GitHub ↗
claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill scillm --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: none
Allowed tools: BashRead
Path: skills/ai-llm/scillm/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the 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

What's inside
Steps it walks through
  1. Two Main Patterns
  2. Tools
  3. vlm.py - VLM (Multimodal) Completions
  4. Quick Start
  5. Commands
  6. Input Format (Batch)
  7. Environment Variables
  8. batch.py - LLM Completions
  9. Input/Output Format
  10. prove.py - Lean4 Theorem Proving
  11. Output Format
  12. Tactic Hints
  13. Prerequisites
  14. Importable API (For Other Skills)
Ships with 1 file
  • metadata.json
Commands it runs
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
python .agents/skills/scillm/vlm.py describe <image> [--prompt PROMPT] [--json] [--model MODEL]
python .agents/skills/scillm/vlm.py batch \
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About this skill
What does the scillm skill do?

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.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill scillm --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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