Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/profsynapse/synaptic-tuner/dataset-publishingnpx skills add ProfSynapse/Synaptic-Tuner --skill dataset-publishinggit clone --depth 1 https://github.com/ProfSynapse/Synaptic-TunerWrote 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.
[](https://agentmods.dev/skills/profsynapse/synaptic-tuner/dataset-publishing)<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="Measured on agentmods" height="20"></a>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 | $0.00069 | $0.00781 |
| Opus 5 | $0.00034 | $0.00391 |
| Sonnet 5 | $0.00014 | $0.00156 |
| Haiku 4.5 | $0.00007 | $0.00078 |
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 4d ago.
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.
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_pathrepo_id
It also auto-uploads a matching metadata sidecar if present:
dataset.jsonldataset.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_TOKENfrom 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.jsonsidecars for dotted filenames correctly
Recommended Workflow
- Build or filter the dataset locally.
- Run
--dry-runfirst. - 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
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.
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.
- 4d ago First seen · 80 lines · 69 tokens per session scan A 5048ccaa3c9b
dataset-publishing is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 781 once invoked, about $0.0003 per session on Opus 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.
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