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/upload-parity-experiments

Create or reuse Hugging Face dataset PRs for `harborframework/parity-experiments` and upload Harbor parity/oracle result folders efficiently with sparse checkout, raw git pushes, and Git LFS.

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lhtb
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Claude Code

$ npx -y skills add zli12321/LHTB --skill upload-parity-experiments --agent claude-code

SKILL.md

upload-parity-experiments.SKILL.md
name: upload-parity-experiments
description: Create or reuse Hugging Face dataset PRs for `harborframework/parity-experiments` and upload Harbor parity/oracle result folders efficiently with sparse checkout, raw git pushes, and Git LFS.

Upload Parity Experiments

Use this skill to publish Harbor parity experiment outputs to the shared Hugging Face dataset and capture the resulting discussion URL for the adapter's `parity_pr` field.

Why This Skill Exists

  • `hf upload-large-folder` can be slow or unreliable for large parity bundles because it pushes through the Hub API commit loop.
  • A normal git clone of `harborframework/parity-experiments` is too expensive because the dataset is very large.
  • Hugging Face dataset PR refs are different from GitHub PR refs and are easy to misuse.
  • Files larger than 10 MiB must be Git LFS-tracked before pushing.

This skill avoids the full clone by fetching only the target PR ref with `--depth 1 --filter=blob:none` and checking out only the paths needed for the current adapter.

Prereqs

  • Ensure Hugging Face authentication is available with discussion-write permission. Either a classic `write` token or a fine-grained token with global `discussion.write` enabled at <https://huggingface.co/settings/tokens>. A read-only or narrowly-scoped token will cause `create_pr.py` to fail with HTTP 403.
  • Keep the target dataset fixed to `harborframework/parity-experiments` unless the user explicitly asks for another repo.
  • Accept any local upload source that already contains the final files the user wants to publish.

Preferred Workflow

1. Create or reuse a dataset PR. 2. Prepare a sparse local worktree for that PR ref. 3. Copy the local parity results into the sparse checkout. 4. Ensure every file larger than 10 MiB is Git LFS-tracked before committing. 5. Push directly to the PR ref with raw `git push`. 6. Share the discussion URL and record it as the adapter's `parity_pr`.

For large parity bundles, prefer raw git over `hf upload-large-folder`. The raw git path is materially faster and more reliable because it avoids the API-side commit loop and does not require cloning the entire parity dataset.

1. Create Or Reuse A Dataset PR

If the user already has a parity PR number, reuse it.

Otherwise, create one with the bundled helper:

uv run python scripts/create_pr.py create-pr \
  --title "Add parity experiments for <adapter_name>" \
  --description-file /path/to/pr-description.md

The script prints JSON including:

  • `pr_number`
  • `discussion_url`
  • `repo_id`

2. Prepare A Sparse PR Checkout

mkdir -p /tmp/parity-experiments-pr<number>
cd /tmp/parity-experiments-pr<number>

git init
git remote add origin git@hf.co:datasets/harborframework/parity-experiments
git config core.sparseCheckout true
git sparse-checkout init --cone
git sparse-checkout set adapters/<adapter_name>

git fetch --depth 1 --filter=blob:none origin refs/pr/<number>:pr/<number>
git checkout pr/<number>

This fetches only the PR ref and the requested paths instead of cloning the full dataset repo.

3. Copy The Local Results

If the local folder already contains the final repo-root layout, copy it as-is.

If the local folder only contains the adapter subtree, copy it into `adapters/<adapter_name>/`:

rsync -a --delete \
  --exclude '.git' \
  --exclude '.cache' \
  --exclude '.DS_Store' \
  /path/to/local-folder/ \
  adapters/<adapter_name>/

4. Ensure Large Files Use Git LFS

The repo-root `.gitattributes` already LFS-tracks common binary, model, archive, and media extensions (`*.bin`, `*.parquet`, `*.safetensors`, images, audio, video, `*.log`, `*.txt`, etc.). Most parity outputs are covered automatically and need no manual action. Scan the sparse checkout for files larger than 10 MiB to catch anything that slipped through:

python - <<'PY'
from pathlib import Path

for path in sorted(Path(".").rglob("*")):
    if path.is_file() and ".git" not in path.parts and path.stat().st_size > 10 * 1024 * 1024:
        print(path)
PY

If any file is flagged and is not already covered by root, write the LFS rule to `adapters/<adapter_name>/.gitattributes` — never to the repo-root `.gitattributes`, which is a shared merge-conflict hotspot. Run `git lfs track` inside the adapter directory so the rule is written with a relative pattern:

(cd adapters/<adapter_name> && git lfs track "<pattern>")
git add adapters/<adapter_name>/.gitattributes

Git LFS honors nested `.gitattributes` files, so rules added this way apply only to that adapter and never collide with other in-flight parity PRs.

5. Commit And Push

find . -name .DS_Store -delete
git add adapters/<adapter_name>
git commit -m "Add parity experiment artifacts for <adapter_name>"
GIT_SSH_COMMAND='ssh -o ServerAliveInterval=30 -o ServerAliveCountMax=10' \
  git push origin pr/<number>:refs/pr/<number>

6. Record The Discussion URL

Use this discussion URL in `parity_experiment.json`:

https://huggingface.co/datasets/harborframework/parity-experiments/discussions/<number>

Monitoring And Retry

When raw `git push` is running, check the live output first. Large pushes often spend a long time in:

  • `git-lfs pre-push`
  • single-object LFS uploads
  • packfile compression

Useful checks:

git ls-remote git@hf.co:datasets/harborframework/parity-experiments refs/pr/<number>
ps -p <pid> -o pid=,etime=,command=

If the push exits early:

  • rerun the exact same `git push` command first
  • if Hugging Face rejects a file larger than 10 MiB, add the needed `git lfs track` rules, re-add the file, and retry
  • if the connection broke mid-push, rerun the same push command before changing anything else

Fallback

Use `hf upload-large-folder` only as a fallback when raw git is unavailable or the user explicitly asks for it.

You can still create the PR with the bundled helper and upload to the PR revision:

hf upload-l
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Questions about upload-parity-experiments.

Which coding agents can use upload-parity-experiments?
upload-parity-experiments installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.