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 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/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/fmind/dot/kaggle"><img src="https://agentmods.dev/badge/skills/fmind/dot/kaggle.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 | $0.00016 | $0.01027 |
| Opus 5.5 | $0.00006 | $0.00411 |
| Sonnet 5.5 | $0.00003 | $0.00205 |
| Haiku 4.5 | $0.00002 | $0.00103 |
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 14d 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 — 53 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: use bounded list/file commands and
kaggle competitions submission-limits <slug> --jsonto inspect available data and submission limits. Read the competition's current rules separately withkaggle competitions pages list list <slug> --content --page-name rules(the repeatedlistis required by the CLI parser); these calls do not authorize accepting them. -
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 -
Track the timeline: after accepting rules, list the page names with
kaggle competitions pages list list <slug>, then read the deadlines with--page-name timeline(ordescriptionwhen no timeline page exists). Offer, or schedule when requested, Google Calendar reminders with gws: one week before the entry and team merger deadline, and on the final submission day. -
Inspect kernels before pushing:
kaggle kernels init -p <dir>writeskernel-metadata.json. Before an authorizedkaggle kernels push -p <dir>, inspect the upload directory, target ID, data sources,is_private, accelerator, internet access, and run timeout: pushing uploads code and starts remote execution. Keepis_private: trueunless public release was explicitly requested, and inspectkaggle quotabefore accelerator use. Verify withkaggle kernels status <owner>/<slug>and retrieve artifacts withkaggle kernels output <owner>/<slug> -p out/. -
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.
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.
- 14d ago Changed 8a0da8e8f641
- 15d ago Changed · -1 lines · -2 tokens per session 2c7c73258247
- 17d ago Changed 7deb607b4147
- 20d ago Changed · +1 lines d19b9e8e3296
- 23d ago Changed f5b051d27ffb
- 29d ago Changed 2fcb15a03b87
- 1mo ago Changed · +1 lines · -20 tokens per session c7882a5a3897
- 1mo ago Changed · +1 lines 779292798d7a
- 1mo ago Changed · -6 lines c08c2484c5a2
- 1mo ago First seen · 57 lines · 38 tokens per session scan A 20c2227c54e9
kaggle is a skill published in the GitHub repository fmind/dot (12 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 1,027 once invoked, about $0.0001 per session on Opus 5.5. 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.
dotfiles-bootstrap
Bootstrap a workstation with the dotfiles framework. Takes a GitHub user / owner+repo / explicit clone URL and runs dot init (which shells out to chezmoi) with the right safety prompts. Honors the active agent profile (ask / plan / apply / audit) so it defaults to dry-run in safer modes and full apply in apply.
vibe
Delegate a coding task to a cheap AI model (Mistral Vibe by default, but any provider Vibe knows about — DeepSeek, Gemini Flash, etc.) and supervise the result via git diff. Claude orchestrates, the cheap model codes. Claude consumes 500-1500 tokens per delegation regardless of how many file reads the delegate does…
prompt-engineering
Draft, review, or improve a prompt for an LLM or agent against a research-backed rubric — explicit instructions, instruction/data separation, output contracts, reasoning scaffolds, grounding, verification, and evals. Use when writing a prompt, auditing one that misbehaves, or explaining a prompting principle.
go-stack
Build Go projects, libraries, CLIs, TUIs, web apps, or ADK agents with the standard package layout and pinned tooling.