Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
RamaLama CLI
Run and interact with AI agents.
skill openclawclawhub Free
Run and interact with AI agents.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- references/models.md, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ieaves
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Ramalama CLI
Use when an alternative AI agent is better suited to a task. For example, working with sensitive data or solving simple tasks with a cheap and local agent, or accessing specialist models with unique capabilities.
Overview
Use this skill to execute ramalama tasks in a consistent, low-risk workflow. Prefer local discovery (--help, local config files, existing project scripts) before making assumptions about flags or runtime defaults. Prefer ramalama when tasks need: flexible model sourcing (hf://, oci://, rlcr://, url://) containerized local inference with runtime/network/device controls RAG data packaging and serving benchmark/perplexity evaluation model conversion and registry push/pull flows
Preflight
Run these checks before first invocation in a session: ramalama version podman info >/dev/null 2>&1 || docker info >/dev/null 2>&1 ramalama run --help If serving on default port, verify availability: lsof -i :8080
Decision Matrix
One-shot inference: ramalama run <model> "<prompt>" Interactive chat loop: ramalama run <model> Serve OpenAI-compatible endpoint: ramalama serve <model> Query an existing endpoint: ramalama chat --url <url> "<prompt>" Build knowledge bundle from files/URLs: ramalama rag <paths...> <destination> Evaluate model performance/quality: ramalama bench <model> and ramalama perplexity <model> Inspect/source lifecycle operations: inspect, pull, push, convert, list, rm
Usage
Start with top-level discovery: ramalama --help ramalama version Apply global options before the subcommand when needed: ramalama [--debug|--quiet] [--dryrun] [--engine podman|docker] [--nocontainer] [--runtime llama.cpp|vllm|mlx] [--store <path>] <subcommand> ... Use command-level help before invoking unknown flags: ramalama <subcommand> --help
1) One-shot run
ramalama run granite3.3:2b "Summarize this in 3 bullets: <text>"
2) Detached service + API call
ramalama serve -d granite3.3:2b curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model":"granite3.3:2b","messages":[{"role":"user","content":"Hello"}]}'
3) Direct Hugging Face source
ramalama serve hf://unsloth/gemma-3-270m-it-GGUF
4) RAG package then query
ramalama rag ./docs my-rag ramalama run --rag my-rag granite3.3:2b "What are the auth requirements?"
5) Benchmark and list benchmark history
ramalama bench granite3.3:2b ramalama benchmarks list
Reliability Defaults
For agent automation, prefer explicit and deterministic flags: ramalama --engine podman run -c 4096 --pull missing granite3.3:2b "<prompt>" Recommended defaults: set --engine explicitly when environment is mixed start with smaller -c/--ctx-size on constrained hosts use --pull missing for faster repeat runs use one-shot non-interactive invocation for scripts
Troubleshooting
Docker socket unavailable: verify Docker is running, or use --engine podman Podman socket unavailable: check podman machine list and start machine if needed timed out during startup: inspect container logs: podman logs <container> reduce context (-c 4096) and retry memory allocation failure: use a smaller model and/or lower context size port conflict on 8080: choose alternate port via -p <port>
Notes
serve exposes an OpenAI-compatible endpoint for external clients. Prefer JSON output flags where available (list --json, inspect --json) for robust parsing in automation. Use ramalama chat --url <endpoint> when the model is already served elsewhere.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- references/models.md