code-competition-artifact-pipeline

code-competition-artifact-pipeline is a skill for Claude Code, Codex from topprismdata/cultivating-ml-agent. It costs 160 tokens per session (892 once invoked), scanned A, original, MIT.

A guide for preparing Kaggle Code Competition notebooks that must run without internet access.

In plain words
What is it for?
Use it to inspect a notebook’s artifact search code, find the required Kaggle datasets, and attach the files needed for the notebook to run.
Why use it?
It helps diagnose missing files or packages when a reused notebook depends on external model files, feature data, or Python packages.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to inspect a notebook’s artifact search code, find the required Kaggle datasets, and attach the files needed for the notebook to run.

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Install with agentmods
npx agentmods add skills/topprismdata/cultivating-ml-agent/code-competition-artifact-pipeline
Install

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.

Any agent
npx skills add topprismdata/cultivating-ml-agent --skill code-competition-artifact-pipeline
Clone the repo
git clone --depth 1 https://github.com/topprismdata/cultivating-ml-agent

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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<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>
Per session 160 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 892 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash e6cd0f6adff6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

skills/examples/code-competition-artifact-pipeline/SKILL.md · 101 lines

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

Read the full file on GitHub · 101 lines

Changes

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.

  1. 10d ago First seen · 101 lines · 160 tokens per session scan A e6cd0f6adff6

Subscribe to this mod's changes

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.

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