Help us improve
Share bugs, ideas, or general feedback.
From tunaLlama
Delegate token-heavy coding work to a local LLM (Ollama / LM Studio) via tunaLlama. Use this when the user asks for code generation, file review, refactoring, or any task where the output would be long. Saves tokens by running heavy generation locally while you maintain oversight.
npx claudepluginhub hang-in/tunallama --plugin tunaLlamaHow this skill is triggered — by the user, by Claude, or both
Slash command
/tunaLlama:delegate-to-ollamaThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You have access to `tuna_*` MCP tools backed by a local LLM. Use them when:
Dispatches local LLM agents for code exploration, single-file review, and multi-file analysis. Manages full lifecycle: availability check, model loading, execution, unloading via craft-skills scripts.
Delegates complex code generation, refactoring, architectural analysis, and review tasks to OpenAI's Codex CLI (GPT-5.3-codex models) via safe workflows with sandboxing and approvals. Activates on explicit triggers like 'use codex' or 'codex exec'.
Consults 100+ external AI models via LiteLLM for architectural reviews, security audits, deep code analysis, or extended reasoning on codebases. Runs async with session management and CLI status checks.
Share bugs, ideas, or general feedback.
You have access to tuna_* MCP tools backed by a local LLM. Use them when:
The user asks for code generation and you have clear requirements.
Use tuna_generate_code instead of generating the code yourself.
The user asks to review or analyze a file.
Use tuna_review_file (passing the path) instead of reading the file first.
The file content stays out of your context — major token savings.
The user asks for refactoring or test writing with a defined scope.
Use tuna_refactor_code or tuna_write_tests.
The user asks a question about multiple files.
Use tuna_analyze_files so file contents bypass your context.
tuna_* tool.tuna_fix_code with the error description.Before starting non-trivial work in a familiar codebase, consider calling
tuna_recall with keywords from the current request. Past delegations on the
same codebase often surface useful prior decisions. Korean queries work — the
backend uses Kiwi morpheme indexing.