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 Kaggle/kaggle-skills --skill hackathon-judginggit clone --depth 1 https://github.com/Kaggle/kaggle-skillsWrote 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/kaggle/kaggle-skills/hackathon-judging)<a href="https://agentmods.dev/skills/kaggle/kaggle-skills/hackathon-judging"><img src="https://agentmods.dev/badge/skills/kaggle/kaggle-skills/hackathon-judging.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Privilege Escalation · line 41 Skill requests more permissions than appear necessary for its stated functionality. Review if elevated access is justified.Fix: Request only the minimum permissions required. Document why each permission is needed. Remove broad permissions like '*' or 'all'.
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 | $0.00082 | $0.02265 |
| Opus 5 | $0.00041 | $0.01132 |
| Sonnet 5 | $0.00016 | $0.00453 |
| Haiku 4.5 | $0.00008 | $0.00227 |
Grade A, and why
hackathon-judging 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hackathon Judging
Overview
The hackathon-judging skill should be invoked whenever a host is trying to grade a Hackathon with the help of LLMs and/or AI agents. This skill guides the Host through the process of retrieving every Kaggle hackathon writeup for a competition, extracting the linked project artifacts, and preparing a complete evidence set to be used for grading.
Additionally, the skill assists the Host in using LLM-based classifiers and pairwise comparisons to generate comparative rankings and bell-shaped grading curves based on the generated evidence sets.
Use Cases
This skill should be invoked whenever a host is trying to grade a public Kaggle Hackathon with the help of LLMs and/or AI agents.
MCP Endpoints
Public Hackathon submissions can be accessed via Kaggle's MCP server. Below is a summary of the MCP tools available for Hackathon Hosts and Participants:
| Role | get_hackathon_overview |
list_hackathon_tracks |
get_hackathon_write_up |
list_hackathon_write_ups |
download_hackathon_write_ups (CSV export) |
|---|---|---|---|---|---|
| Logged-out (anonymous) | ✅ | ✅ | ❌ | ❌ | ❌ |
| Logged-in user (no hackathon affiliation) | ✅ | ✅ | ✅ | ❌ | ❌ |
| Rules acceptor (joined, no writeup yet) | ✅ | ✅ | ✅ | ❌ | ❌ |
| Submitter (team member with a writeup) | ✅ | ✅ | ✅ | ✅ | ❌ |
| Hackathon judge | ✅ | ✅ | ✅ | ✅ | ❌ |
| Hackathon host | ✅ | ✅ | ✅ | ✅ | ✅ |
| Kaggle site admin | ✅ | ✅ | ✅ | ✅ | ✅ |
MCP Prerequisites
- API Token: Kaggle API token available as
KAGGLE_API_TOKENor in~/.kaggle/access_token. - Authorization: MCP client configured to call https://www.kaggle.com/mcp with
Authorization: Bearer <token>. - Target: A hackathon competition slug (e.g.,
meta-kaggle-hackathon). - Permissions: For host-gated workflows (
list_hackathon_write_upsordownload_hackathon_write_ups), use an account with the required host or judge access.
Installation: Install the Kaggle MCP server in a client configuration like this:
What ships with it
1 file 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 First seen · 183 lines · 82 tokens per session scan A d0c423f085fa
hackathon-judging is a skill published in the GitHub repository Kaggle/kaggle-skills (64 stars, last pushed 24d ago), licensed Apache-2.0. It adds 82 tokens to every session and 2,265 once invoked, about $0.0004 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.
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