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 vasilyu1983/AI-Agents-public --skill ai-data-curation-pretraininggit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-data-curation-pretraining)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-data-curation-pretraining"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-data-curation-pretraining/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/vasilyu1983/ai-agents-public/ai-data-curation-pretraining"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-data-curation-pretraining.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.04614 |
| Opus 5 | $0.00024 | $0.02307 |
| Sonnet 5 | $0.00010 | $0.00923 |
| Haiku 4.5 | $0.00005 | $0.00461 |
Grade A, and why
ai-data-curation-pretraining 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 8d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pretraining Data Curation — Functional Reference Skill
Domain: Building web-scale and synthetic pretraining corpora, running controlled data ablations. Distinct from applications-layer retrieval (RAG) and general data engineering.
No theory. No generic pipeline intros. Focus on stage-by-stage decisions, heuristic thresholds, tooling choices, and ablation protocol.
ASCII Flow
CommonCrawl WARCs
|
v
[Extract] trafilatura / datatrove HTMLExtractor
raw text + metadata (URL, timestamp, content-type)
|
v
[Language ID] fastText lid.176.bin
keep target language(s), threshold ≥ 0.65
|
v
[Quality Filter — Heuristic] Gopher / C4 rules
symbol-to-word ratio, fraction lines ending ellipsis,
stopword density, word count bounds, mean word length
|
v
[Quality Filter — Classifier] FineWeb-Edu edu-score / custom
trained on human labels; outperforms heuristics on recall
|
v
[Near-Dedup] MinHash + LSH banding (datasketch)
n-gram shingles -> MinHash signature -> band partitioning
|
v
[Exact-Substring Dedup] suffix-array substring match
remove exact repeated sequences across documents
|
v
[Decontamination] n-gram match against eval benchmarks
FAIL LOUD — contaminated eval numbers are the field's #1 silent failure
|
v
[PII / Safety Scrub] regex + classifier
email, phone, SSN, credit card patterns; hate/CSAM removal
|
v
[Tokenize + Shard] HF tokenizers / tiktoken; Parquet shards
|
v
[Domain Mix + Weight] dolma toolkit / custom sampling
web / books / code / math / synthetic — proportions are a research lever
|
v
[Train + Eval] nanotron / lighteval / lm-evaluation-harness
ablation output: eval delta per pipeline stage
When to Use This Skill
Activate when the task involves:
- Finding existing high-quality datasets for pretraining or fine-tuning before building from scratch (see Dataset Discovery reference)
- Sourcing and filtering CommonCrawl WARCs or other web-scale corpora
- Implementing or debugging any stage of the curation pipeline above
- Designing quality filters (heuristic or classifier-based)
- Running MinHash / LSH deduplication or exact-substring dedup
- Decontaminating a dataset against evaluation benchmarks
- Generating synthetic pretraining data (Cosmopedia, Self-Instruct, Evol-Instruct, Nemotron)
- Designing and executing controlled data ablations
- Writing datasheets (Gebru et al.) for a curated dataset
- Understanding open recipe datasets: FineWeb, Dolma, The Pile, RedPajama, SlimPajama, C4, RefinedWeb, OLMo
What ships with it
7 files 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.
- 8d ago Changed · +2 lines 24c0e7480fe6
- 12d ago First seen · 253 lines · 48 tokens per session scan A dc898c11390c
ai-data-curation-pretraining is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 9d ago), licensed MIT. It adds 48 tokens to every session and 4,614 once invoked, about $0.0002 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.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
dbt-transformation-patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.