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Download models and weights from HuggingFace. Use when downloading models, fetching safetensors or GGUFs, running "hf download", cloning model repos, or pulling weights from HuggingFace.
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Basado en la clasificación ocupacional SOC
Sync this Claude Code config against new release notes — find config features added since the pinned baseline that this config should adopt. Use when Claude Code has been updated, when the user asks what's new / what changed / whether to adopt new settings or hooks, after a version bump, or when checking config currency against the latest release. Reports adoptable settings/hooks/env-vars/permissions as a risk-tiered proposal, then bumps the baseline. Do NOT use for auditing existing config quality, debugging hooks, or organizing rules — that is `maintaining-claude-code`.
Monitor and watch GitHub Actions CI/CD pipeline runs in real time. Use when the user has pushed code and wants to watch the build, check CI status, see if tests passed, monitor a pipeline, or wait for a workflow run to complete. Also use after any `jj git push` or `git push` command. Reports pass/fail with failed job logs. Do NOT use for writing or editing CI workflow YAML files, optimizing CI config, or debugging CI configuration — only for monitoring active runs.
Reorganize and clean up messy commit history on a feature branch into logical, reviewable commits. Use when cleaning up commits, preparing a branch for review, separating formatting from logic changes, fixing broken intermediate states, or squashing WIP commits.
Audit and improve Claude Code hooks, rules, and settings.json. Use when adding/debugging a hook, organizing rules, auditing settings.json (permissions, env vars, stale flags), or deciding between hook vs skill vs rule vs CLAUDE.md. For SKILL.md authoring use the skill-creator plugin; for CLAUDE.md audits use the claude-md-improver plugin.
Execute a structured test plan and emit a concise results document (JSON + Markdown). Use when running test plans, executing QA workflows, verifying acceptance criteria systematically, or producing test results documentation.
Search past Claude Code sessions to recall prior solutions, commands, fixes, and decisions. Use when the user references earlier work ("how did I fix", "last time we", "pick up where we left off", "a few months ago", "what was that … I set up", "didn't I once").
| name | downloading-hf-models |
| description | Download models and weights from HuggingFace. Use when downloading models, fetching safetensors or GGUFs, running "hf download", cloning model repos, or pulling weights from HuggingFace. |
Always use uv run with the HuggingFace Hub package as an inline dependency:
uv run --with huggingface_hub hf download <repo> <file> --local-dir <path>
pip install huggingface_hub && huggingface-cli download ...uv run hf download ... (missing --with)uv run --with "huggingface_hub[cli]" ... (the [cli] extra doesn't exist, the CLI is included in the base package)uv run --with huggingface_hub hf download <org>/<repo> <filename> --local-dir <destination>
uv run --with huggingface_hub hf download <org>/<repo> <file1> <file2> --local-dir <destination>
uv run --with huggingface_hub hf download <org>/<repo> --include "*.gguf" --local-dir <destination>
Warning: Large GGUF models are often split into parts (e.g., model.gguf.part-000, .part-001, etc.). Use --include "*.gguf*" (note the trailing *) to match both single GGUFs and split parts. Use --dry-run first to check what files will be downloaded.
uv run --with huggingface_hub hf download <org>/<repo> --local-dir <destination>
HF_TOKEN environment variable is used automatically for gated models--local-dir places files directly in the target directory (no nested .cache structure)