Your localLLM repo provides a full local LLM toolchain on Apple Silicon: setup scripts, a rich CLI chat launcher, benchmarks, and diagnostics. The operational path is: install Ollama, ensure the service is running, initialize the venv, pull models, then launch chat or benchmarks.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlocal-llm-opsExecute the skills CLI command in your project's root directory to begin installation:
Fetches local-llm-ops from bobmatnyc/claude-mpm-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate local-llm-ops. Access via /local-llm-ops in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Your localLLM repo provides a full local LLM toolchain on Apple Silicon: setup scripts, a rich CLI chat launcher, benchmarks, and diagnostics. The operational path is: install Ollama, ensure the service is running, initialize the venv, pull models, then launch chat or benchmarks.
./setup_chatbot.sh
./chatllm
If no models are present:
ollama pull mistral
brew install ollamabrew services start ollama./setup_chatbot.shcurl http://localhost:11434/api/version./chatllm (primary launcher)./chat or ./chat.py (alternate launchers)./install_aliases.sh then llm, llm-code, llm-fastTask modes:
./chat -t coding -m codellama:70b
./chat -t creative -m llama3.1:70b
./chat -t analytical
Benchmarks are scripted in scripts/run_benchmarks.sh:
./scripts/run_benchmarks.sh
This runs bench_ollama.py with:
benchmarks/prompts.yamlbenchmarks/models.yamlRun the built-in diagnostic script when setup fails:
./diagnose.sh
Common fixes:
./setup_chatbot.shollama is in PATHollama pull mistral.venv~/.localllm/http://localhost:11434toolchains/universal/infrastructure/dockerPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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local-llm-ops reduced setup friction for our internal harness; good balance of opinion and flexibility.
local-llm-ops is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend local-llm-ops for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: local-llm-ops is the kind of skill you can hand to a new teammate without a long onboarding doc.
local-llm-ops fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: local-llm-ops is focused, and the summary matches what you get after install.
Registry listing for local-llm-ops matched our evaluation — installs cleanly and behaves as described in the markdown.
We added local-llm-ops from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
local-llm-ops reduced setup friction for our internal harness; good balance of opinion and flexibility.
local-llm-ops fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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