plugins/huggingface-utils/skills/hf-init/SKILL.md
Initialize HuggingFace integration - validates .env variables, tests API connectivity, and ensures the dataset repository structure exists. Use when onboarding a new project to HuggingFace or when credentials change.
npx skillsauth add richfrem/agent-plugins-skills hf-initInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill requires Python 3.8+ and standard library only. No external packages needed.
To install this skill's dependencies:
pip-compile ./requirements.in
pip install -r ./requirements.txt
See ./requirements.txt for the dependency lockfile (currently empty — standard library only).
Status: Active Author: Richard Fremmerlid Domain: HuggingFace Integration
Sets up everything needed for HuggingFace persistence. Run this once when onboarding a new project, or whenever credentials change.
.env variables are setlineage/, data/, metadata/)| Variable | Required | Description |
|:---------|:---------|:------------|
| HUGGING_FACE_USERNAME | ✅ Yes | Your HF username |
| HUGGING_FACE_TOKEN | ✅ Yes | API token (set in ~/.zshrc, NOT .env) |
| HUGGING_FACE_REPO | ✅ Yes | Model repo name |
| HUGGING_FACE_DATASET_PATH | ✅ Yes | Dataset repo name |
| HUGGING_FACE_TAGS | ❌ No | Comma-separated discovery tags for dataset card |
| HUGGING_FACE_PROJECT_NAME | ❌ No | Pretty name for dataset card heading |
| SOUL_VALENCE_THRESHOLD | ❌ No | Moral/emotional charge filter (default: -0.7) |
python ./hf_config.py
python ./hf_init.py
python ./hf_init.py --validate-only
# Token goes in shell profile (never committed):
export HUGGING_FACE_TOKEN=hf_xxxxxxxxxxxxx
# Project vars go in .env:
HUGGING_FACE_USERNAME=<your-username>
HUGGING_FACE_REPO=<your-model-repo>
HUGGING_FACE_DATASET_PATH=<your-dataset-repo>
# Optional customization:
HUGGING_FACE_TAGS=reasoning-traces,cognitive-continuity,your-project-tag
HUGGING_FACE_PROJECT_NAME=My Project Soul
# Run init
python ./hf_init.py
tools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.