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 skills add Hipson47/Hipson --skill dataset-buildergit clone --depth 1 https://github.com/Hipson47/HipsonWrote 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/hipson47/hipson/dataset-builder)<a href="https://agentmods.dev/skills/hipson47/hipson/dataset-builder"><img src="https://agentmods.dev/badge/skills/hipson47/hipson/dataset-builder/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/skills/hipson47/hipson/dataset-builder"><img src="https://agentmods.dev/badge/skills/hipson47/hipson/dataset-builder.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 | $0.00030 | $0.01035 |
| Opus 5.5 | $0.00012 | $0.00414 |
| Sonnet 5.5 | $0.00006 | $0.00207 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
dataset-builder 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 1mo 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer Vision Dataset Builder
Purpose
Create reproducible CV dataset artifacts from owned or licensed media. Make provenance, class definitions, annotation formats, split strategy, validation, augmentation, versioning, and privacy decisions explicit before training.
Use When
- Extracting frames from video or images from an approved source.
- Defining or converting YOLO, COCO, or another documented annotation format.
- Creating train/validation/test splits, class maps, dataset manifests, or quality reports.
Do Not Use When
- Media ownership, consent, license, or intended use is unknown.
- The task is model training/inference without dataset changes.
- The request asks to scrape private media, bypass access controls, or infer sensitive labels.
Inputs
- Source inventory, ownership/consent/license, retention, and allowed uses.
- Dataset task, class ontology, inclusion/exclusion rules, and annotation format.
- Sampling policy, duplicate policy, split groups, ratios, seed, and version ID.
- Image constraints, labeling tool/export source, augmentation policy, and destination outside the Hipson repository.
Default Stack
- Python, OpenCV, NumPy, and standard JSON/CSV/YAML parsers in a project-owned environment.
- Local filesystem and content hashes for the baseline; Roboflow, Supervision, and Hugging Face remain optional integrations.
- YOLO or COCO only when the downstream model contract requires it.
- Group-aware deterministic splits and augmentation after splitting.
Workflow
- Inventory sources and record owner, license, consent, collection method, allowed use, retention, and sensitive-content handling. Stop if rights are unresolved.
- Freeze a versioned class map and annotation policy with positive, negative, ignored, occluded, truncated, and ambiguous examples.
- Extract frames deterministically by timestamp, interval, or scene rule. Use collision-safe identifiers derived from stable source IDs, never private paths.
- Validate decoding, dimensions, channels, corrupt files, exact/near duplicates, empty labels, unknown classes, normalized coordinate bounds, and polygon shape.
- Split by source group, subject, site, session, or video before augmentation to prevent adjacent frames or related subjects from leaking across partitions.
- Apply augmentation only to training data and record the transform policy, random seed, tool/library version, and whether labels were transformed.
- Convert formats through one deterministic adapter and round-trip a sample. Preserve the canonical source annotations separately.
- Write a manifest with hashes, counts, class distribution, split statistics, rejected items, schema version, and unresolved quality risks.
- End with
vision-verifier; do not claim dataset suitability from file counts alone.
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.
- 1mo ago First seen · 113 lines · 30 tokens per session scan A 0ee348230e01
dataset-builder is a skill published in the GitHub repository Hipson47/Hipson (4 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 1,035 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.
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