What is dataset-publishing?

A command-line tool for publishing JSONL datasets to a Hugging Face dataset repository. JSONL is a text format where each line is one JSON record, and Hugging Face hosts datasets for sharing and machine-learning work.

Use it to dry-run or perform uploads, publish filtered dataset versions, add metadata sidecars, rename repository files, or skip metadata.

It avoids manually uploading the dataset and its matching metadata file or renaming files in the repository.

How ProfSynapse/Synaptic-Tuner configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to ProfSynapse/Synaptic-Tuner. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/ProfSynapse/Synaptic-Tuner/main/.agents/skills/dataset-publishing/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/ProfSynapse/Synaptic-Tuner

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/dataset-publishing/github.svg)](https://agentmods.dev/skills/profsynapse/synaptic-tuner/dataset-publishing)

Your own site

<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/dataset-publishing"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/dataset-publishing/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/dataset-publishing"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/dataset-publishing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 781 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00069	$0.00781
Opus 5.5	$0.00028	$0.00312
Sonnet 5.5	$0.00014	$0.00156
Haiku 4.5	$0.00007	$0.00078

Measured 2mo ago against content hash 5048ccaa3c9b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dataset-publishing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/publish_dataset_to_hf.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/dataset-publishing/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dataset Publishing

Publish a local dataset JSONL to a Hugging Face dataset repo with the skill-owned script: python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py

The script accepts:

dataset_path
repo_id

It also auto-uploads a matching metadata sidecar if present:

dataset.jsonl
dataset.metadata.json

Quick Reference

Task	Command
Dry-run a dataset upload	`python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py DATASET.jsonl namespace/repo --dry-run`
Upload dataset + sidecar	`python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py DATASET.jsonl namespace/repo`
Upload under a new repo filename	`python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py DATASET.jsonl namespace/repo --path-in-repo new_name.jsonl`
Upload with explicit metadata file	`python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py DATASET.jsonl namespace/repo --metadata-path DATASET.metadata.json`
Skip metadata sidecar	`python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py DATASET.jsonl namespace/repo --no-metadata`

Defaults

Reads HF_TOKEN from the environment or repo .env
Creates the target dataset repo if needed
Uploads the dataset file to path_in_repo = basename(dataset_path)
Auto-detects *.metadata.json sidecars for dotted filenames correctly

Recommended Workflow

Build or filter the dataset locally.
Run --dry-run first.
Run the real upload command.
Point the next experiment spec at the uploaded HF dataset file.

Common Patterns

Upload a filtered SFT dataset:

python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py \
  Datasets/synthchat/my_filtered_dataset.jsonl \
  professorsynapse/claudesidian-synthetic-dataset \
  --dry-run

python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py \
  Datasets/synthchat/my_filtered_dataset.jsonl \
  professorsynapse/claudesidian-synthetic-dataset

Read the full file on GitHub · 80 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

scripts/publish_dataset_to_hf.py 7.0 KB runs code

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 80 lines · 69 tokens per session scan A 5048ccaa3c9b

Subscribe to this mod's changes

dataset-publishing is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (28 stars, last pushed 3d ago), licensed MIT. It adds 69 tokens to every session and 781 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AI

dataset-publishing next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
dataset-publishingProfSynapse/Synaptic-Tuner	Skill	28	3d ago	MIT	A	69	Claude Code
agent-platform-model-registrygoogle/skills	Skill	22k	yesterday	Apache-2.0	A	60	Claude Code, Codex
foundry-config-setupmicrosoft/agent-framework	Skill	15k	yesterday	MIT	A	65	Claude Code, Codex
google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills	Skill	22k	yesterday	Apache-2.0	A	132	Claude Code, Codex
training-checkwanshuiyin/Auto-claude-code-research-in-sleep	Skill	19k	3d ago	MIT	A	35	Claude Code
nemo-automodel-launcher-configNVIDIA/skills	Skill	3.9k	yesterday	Apache-2.0	A	30	Claude Code
finetuningmicrosoft/skills	Skill	3.2k	yesterday	MIT	A	132	Claude Code

Synaptic-Tuner: Skill for Claude Code