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.
git clone --depth 1 https://github.com/stellarshenson/claude-code-pluginsWrote 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/commands/stellarshenson/claude-code-plugins/dataset)<a href="https://agentmods.dev/commands/stellarshenson/claude-code-plugins/dataset"><img src="https://agentmods.dev/badge/commands/stellarshenson/claude-code-plugins/dataset/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.
<a href="https://agentmods.dev/commands/stellarshenson/claude-code-plugins/dataset"><img src="https://agentmods.dev/badge/commands/stellarshenson/claude-code-plugins/dataset.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00032 | $0.00624 |
| Opus 5.5 | $0.00013 | $0.00250 |
| Sonnet 5.5 | $0.00006 | $0.00125 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
dataset 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 21d 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 — 24 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dataset
Read this plugin's skill file skills/dataset/SKILL.md first - it is the single source of truth for the two-artifact layout, the admission gate, the sidecar shape, and the fetcher contract. Do NOT duplicate its content here. The examples/ sidecars and fetcher skeleton win on any conflict.
What to do
- Read this plugin's skill file
skills/dataset/SKILL.md, then the closestexamples/artifact -dataset-ragtruth.mdfor a public corpus,dataset-edgar-restricted.mdfor a restricted one,fetch_datasets.pyfor the fetcher skeleton - Location gate (before creating anything) - propose
data/external/<task>-datasets/plus the alternatives visible on disk; WAIT for the answer, then reuse the confirmed location for the rest of that task without asking again - Admission gate before any download - licence permits the intended use (commercial, training and redistribution are three separate permissions), the corpus carries what the task consumes, the task-shape mapping writes as a one-line rewrite, no provenance overlap with the evaluation set. Name which filter a rejected corpus failed
- Sidecar first - the spec dict in
scripts/fetch_<task>_datasets.pyis the single source of truth;--dry-runrenders every sidecar and fetches nothing, so licences and sizes are read before a byte costs bandwidth. Never hand-write or hand-edit a sidecar - Fetch - named args fetch one corpus, no args fetch all; a failed corpus prints
SKIP <id>: <error>and continues; stage, archive, remove staging; the archive contains its own sidecar - Gitignore - whitelist the sidecars, ignore everything else under the folder; verify with
git check-ignore -von one archive and one sidecar before the first commit - Restricted corpus - the three extra sidecar bullets (restriction clauses, reason, exclusion mechanism); no archive at all where redistribution is forbidden; credentials come from the environment or the vault at fetch time - never the spec, the sidecar, or the repo
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.
- 21d ago Changed 16b0b4e9a56c
- 1mo ago First seen · 24 lines · 32 tokens per session scan A d3e000827d7f
dataset is a command published in the GitHub repository stellarshenson/claude-code-plugins (4 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 624 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
laravel-ai-sdk
Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
vlm-ocr-evaluation
Run the vlm-ocr skill in its evaluate phase: compare candidate OCR systems against a stratified human-transcribed ground-truth sample and pick a model on measured CER/WER before committing to a bulk run.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.