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 agentmods add skills/fmind/dot/kagglenpx skills add fmind/dot --skill kagglegit clone --depth 1 https://github.com/fmind/dotWrote 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/fmind/dot/kaggle)<a href="https://agentmods.dev/skills/fmind/dot/kaggle"><img src="https://agentmods.dev/badge/skills/fmind/dot/kaggle.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.00852 |
| Opus 5 | $0.00019 | $0.00426 |
| Sonnet 5 | $0.00008 | $0.00170 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
kaggle 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 yesterday.
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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kaggle CLI
Use kaggle for competition, dataset, kernel, and model operations from the shell. The official Kaggle skills document every command and metadata file; this skill owns authentication, download scope, and the authority boundary around submissions and publications.
Workflow
-
Resolve the account:
kaggle auth login(OAuth) orKAGGLE_API_TOKENin the environment; the legacy~/.kaggle/kaggle.jsonstill works. Never runkaggle auth print-access-tokenduring ordinary work. -
Read before writing:
kaggle competitions list,kaggle competitions files <slug>,kaggle datasets files <owner>/<name>, andkaggle competitions submission-limits <slug> --jsoncost nothing and reveal the rules in force. -
Download into an ignored directory: accept the competition rules on the website first (the CLI returns 403 otherwise).
kaggle competitions download <slug> -p data/ kaggle datasets download <owner>/<name> -p data/ --unzip -
Kernels as code:
kaggle kernels init -p <dir>writeskernel-metadata.json;kaggle kernels push -p <dir>publishes it;kaggle kernels status <owner>/<slug>andkaggle kernels output <owner>/<slug> -p out/retrieve the run. -
Submit with authority: a submission counts against the daily limit and shows on the leaderboard, so confirm the competition, file, and message first, then verify.
kaggle competitions submit <slug> -f submission.csv -m "<message>" kaggle competitions submissions <slug> -
Publish with authority:
kaggle datasets create -p <dir>andkaggle datasets version -p <dir> -m "<message>"are private by default (--publicflips it); confirm the license and visibility in the metadata before the first push.
Gotchas
- Pinned version: in a project that pins
kaggle, calluv run kaggleso the pinned version runs instead of the global shim. - Quota:
kaggle quotashows the accelerator budget before a kernel push with--accelerator. - Scripts: pass
-Wto silence the out-of-date warning so JSON output stays parseable.
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.
- yesterday First seen · 57 lines · 38 tokens per session scan A 20c2227c54e9
kaggle is a skill published in the GitHub repository fmind/dot (4 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 852 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 2026-09-03.
Other skills, from other repositories
agent-evaluation
Evaluate stochastic LLM/RAG/model/retrieval/tool agents in trials. Compare baseline/candidate on development/sealed holdouts with calibrated deterministic/model/trace graders; measure reliability, variance, leakage, safety, and cost.
prompt-design
Design production LLM or agent prompt stacks: instructions, tool contracts, examples, outputs, and runtime context. Use for precedence, conflicts, dynamic or untrusted context; prove behavior with agent-evaluation.
go-stack
Build Go projects, libraries, CLIs, TUIs, web apps, or ADK agents with the standard package layout and pinned tooling.
python-stack
Build typed Python projects with uv, Ruff, ty, pytest, Litestar, and Typer. Use for packages, CLIs, web apps, tests, typing, or API verification.
k8s-local
Create and manage local Kubernetes clusters (k3d or kind) and deploy to them with kubectl, helm, helmfile, and skaffold. Use for local k8s cluster setup, dev loops, and debugging.
chezmoi
Manage chezmoi dotfiles: source naming, Go templates, age-encrypted secrets, and the edit-source then apply/diff workflow.