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Hf Upload
ASecurityUpload primitives for HuggingFace Soul persistence - file, folder, snapshot, JSONL append, and dataset card management with exponential backoff. Use when persisting agent learnings, snapshots, or semantic caches to HuggingFace.
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- Added September 7, 2026
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[](https://www.skillsdirectory.com/skills/richfrem-hf-upload-project-sanctuary)---
name: hf-upload
description: "Upload primitives for HuggingFace Soul persistence - file, folder, snapshot, JSONL append, and dataset card management with exponential backoff. Use when persisting agent learnings, snapshots, or semantic caches to HuggingFace."
allowed-tools: Bash, Read
dependencies: ["pip:huggingface_hub"]
---
# HuggingFace Upload Primitives
**Status:** Active
**Author:** Richard Fremmerlid
**Domain:** HuggingFace Integration
**Depends on:** `hf-init` (credentials must be configured first)
## Purpose
Provides consolidated upload operations for all HF-consuming plugins (Primary Agent, Orchestrator, etc.). All uploads include exponential backoff for rate-limit handling.
## Available Operations
| Function | Description | Remote Path |
|---|---|---|
| `upload_file()` | Upload a single file | Custom path |
| `upload_folder()` | Upload an entire directory | Custom prefix |
| `upload_soul_snapshot()` | Upload a sealed learning snapshot | `lineage/seal_<timestamp>_*.md` |
| `upload_semantic_cache()` | Upload RLM semantic cache | `data/rlm_summary_cache.json` |
| `append_to_jsonl()` | Append records to soul traces | `data/soul_traces.jsonl` |
| `ensure_dataset_structure()` | Create ADR 081 folders | `lineage/`, `data/`, `metadata/` |
| `ensure_dataset_card()` | Create/verify tagged README.md | `README.md` |
## Usage
### From Python (as a library)
```python
from hf_upload import upload_file, upload_soul_snapshot, append_to_jsonl
# Upload a single file
result = await upload_file(Path("my_file.md"), "lineage/my_file.md")
# Upload a sealed learning snapshot
result = await upload_soul_snapshot(Path("snapshot.md"), valence=-0.5)
# Append records to soul_traces.jsonl
result = await append_to_jsonl([{"type": "learning", "content": "..."}])
```
### Prerequisites
1. Run `hf-init` first to validate credentials and dataset structure
2. Requires `huggingface_hub` installed (`pip install huggingface_hub`)
3. Environment variables: `HUGGING_FACE_USERNAME`, `HUGGING_FACE_TOKEN`
## Error Handling
All operations return `HFUploadResult` with:
- `success: bool` — whether the upload succeeded
- `repo_url: str` — HuggingFace dataset URL
- `remote_path: str` — path within the dataset
- `error: str` — error message if failed
Rate-limited requests retry with exponential backoff (up to 5 attempts).
Files in this skill
- SKILL.md
- evals/evals.json
- references/acceptance-criteria.md
- references/fallback-tree.md
- scripts/hf_config.py
- scripts/hf_upload.py
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