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Agent Skill

Cdf Anomalygen skill

cdf-anomalygenNVIDIA/skillsskills/cdf-anomalygen/SKILL.md

Use when running the CDF AnomalyGen pipeline over a defect dataset — mask placement, fine-tuning, synthetic defect-image generation (SDG), evaluation, quality refinement, and pseudo-labeling — even when the user names only one stage.

3.7kApache-2.0

always on

54 tokname + description

once used

9,563 tokSKILL.md body

bundled files

10114 KB

repo stars

3.7kGitHub

Install the Cdf Anomalygen skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/NVIDIA/skills --skill cdf-anomalygen

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/NVIDIA/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -R /tmp/skills/skills/cdf-anomalygen ~/.claude/skills/cdf-anomalygen

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description54 tok
When the agent uses itThe SKILL.md instructions9,563 tok
Only if opened10 bundled files (114 KB)varies

Until it is used, cdf-anomalygen costs about 25× less than the median MCP server we measured (1,363 tokens on every turn). Installing all 150 skills from NVIDIA/skills would load 20,796 tokens of descriptions. Skills vs MCP study

Checks

  • The instructions are over 5,000 tokens, the Agent Skills specification's guideline for SKILL.md.
  • The instructions are over 500 lines; the specification suggests moving detail into reference files.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • Skill: cdf-anomalygen
  • Purpose
  • When to Use This Skill
  • Prerequisites
  • Inputs
  • Instructions
  • Step 1 — dataset (contract layout)
  • Step 2 — mask placement
  • Step 3 — fine-tuning (skip when generationonly)
  • Step 4 — generation / SDG (skip when finetuneonly)
  • Step 5 — evaluation (skip when finetuneonly)
  • Step 6 — quality refinement (skip when finetuneonly)
  • Step 7 — pseudo-labeling (skip when finetuneonly)
  • Verification
  • Examples
  • Troubleshooting

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