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 topprismdata/cultivating-ml-agent --skill code-competition-artifact-pipelinegit clone --depth 1 https://github.com/topprismdata/cultivating-ml-agentWrote 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/topprismdata/cultivating-ml-agent/code-competition-artifact-pipeline)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/code-competition-artifact-pipeline"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/code-competition-artifact-pipeline/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/topprismdata/cultivating-ml-agent/code-competition-artifact-pipeline"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/code-competition-artifact-pipeline.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 | $0.00160 | $0.00892 |
| Opus 5 | $0.00080 | $0.00446 |
| Sonnet 5 | $0.00032 | $0.00178 |
| Haiku 4.5 | $0.00016 | $0.00089 |
Grade A, and why
code-competition-artifact-pipeline 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 10d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Competition Artifact Pipeline
Problem
Kaggle Code Competitions (no internet, notebook-only submission) often have community baselines that depend on external artifact datasets:
- Pre-trained model weights (pickle/onnx)
- Feature matrices (CSV/numpy)
- Custom library wheels (.whl)
Forking these baselines fails unless you identify and attach ALL required datasets. The error messages are often cryptic ("file not found", "module not found").
Solution
Step 1: Read the Notebook's find_artifacts() Function
Most community baselines have a find_artifacts() or similar function that
searches /kaggle/input/ for specific directory structures:
def find_artifacts():
candidates = [
"/kaggle/input/datasets/author/artifact-name",
"/kaggle/input/artifact-name",
]
# ... searches for repo/, weights/, wheels/, data/train.csv
This reveals: exact dataset names, expected file structure, and required Python packages.
Step 2: Search for Artifact Datasets
kaggle datasets list --search "competition-name artifacts"
kaggle datasets list --search "author-name"
Look for high-download datasets (500+ downloads = canonical bundle).
Step 3: Attach ALL Required Datasets
In kernel-metadata.json:
{
"dataset_sources": [
"author/competition-artifacts",
"author/library-wheel"
]
}
Common missing datasets (from real failures):
| Competition | Missing Dataset | Error |
|---|---|---|
| ROGII | ravaghi/wellbore-geology-prediction-artifacts | "data/train.csv not found" |
| ROGII | phongnguyn23021656/koolbox-offline | "No module named 'koolbox'" |
| Biohub | thibautgoldsborough/cellmot-baseline-artifacts | "repo/ not found" |
| NeuroGolf | (embedded in notebook, no external needed) | — |
Step 4: Pipeline-Trim for Crashing Components
If the notebook has multiple pipelines (A/B) and one crashes:
- Pipeline A succeeds but Pipeline B errors → kernel ERROR → no submission scored
- Fix: delete Pipeline B cells, keep only Pipeline A + final submission write
- Alternative: wrap Pipeline B in try/except
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.
- 10d ago First seen · 101 lines · 160 tokens per session scan A e6cd0f6adff6
code-competition-artifact-pipeline is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 12d ago), licensed MIT. It adds 160 tokens to every session and 892 once invoked, about $0.0008 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 2026-08-31.
Other skills, from other repositories
huggingface-hub
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.
tensorboard
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit.
mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform.
datachain-knowledge
Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/…
prompt-scanner
A scanner for text sent to an AI agent, looking for prompt injection and jailbreak attempts. Prompt injection is text that tries to override an agent's instructions; a jailbreak tries to bypass its safety limits.
install-openviking-memory
Install and configure the OpenViking long-term memory plugin for OpenClaw via natural conversation. Once installed, the plugin automatically captures facts from chats and recalls relevant context before each reply (auto-capture + auto-recall, cross-session). Covers prerequisites, install through OpenClaw's plugin…