dataset-curation
Curate generated images into a clean training dataset, validate metadata, remove duplicates, and sync manifests/assets to Hugging Face.
Skill by bl1nk42
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/bl1nk42-dataset-curation
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: ml
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/dataset-curation
What's inside
- Skill name
dataset-curation- Description
- Curate generated images into a clean training dataset, validate metadata, remove duplicates, and sync manifests/assets to Hugging Face.
- License
- Apache-2.0
- Instructions
- 8 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Dataset Curation Use this skill when the user asks to collect, clean, label, export, or publish image-generation training data. Never publish automatically. First inspect the manifest with `export_dataset_manifest`, validate that each row has an executed prompt, raw concept, image path, model, style, tags, and quality score, then check duplicates and negative votes. Use `save_training_pair` for a local append-only record. Use `sync_hf_dataset` only after the user explicitly asks to sync or publish and the repository/token are configured. Prefer a dataset repository for CSV/JSONL and assets; use a model repository for model weights or LoRA files. Report repository URL and commit result. Recommended lifecycle: collect -> validate -> deduplicate -> review negatives -> export manifest -> sync to Hugging Face.
From bl1nk42/image-gen, Apache-2.0.
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Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 25, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/bl1nk42/image-gen
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Restore blurry, damaged, faded, or vintage photos while preserving identity, pose, composition, clothing, and historical character.
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prompt-engineer
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Design, refactor, evaluate, and version prompts for image generation, image analysis, style learning, and structured agent tools.
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memory-recall
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Retrieve relevant project decisions, prior prompts, style preferences, debugging notes, and past outputs with source traceability.
SkillAFreshness: A★ 2Claude Code +9 - 0.598score
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Apply the Nothing-inspired monochrome industrial design system to interfaces, dashboards, image prompts, and generated visual references.
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Plan and execute professional AI video projects including commercials, explainers, short films, social clips, and image-to-video sequences.
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Cite this page
dataset-curation skill. Agent Mart. https://agentmart.dev/skills/bl1nk42-dataset-curation (last updated 2026-10-24)