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 magnus919/agent-skills --skill tabpfngit clone --depth 1 https://github.com/magnus919/agent-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/magnus919/agent-skills/tabpfn)<a href="https://agentmods.dev/skills/magnus919/agent-skills/tabpfn"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/tabpfn/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/magnus919/agent-skills/tabpfn"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/tabpfn.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 | $0.00092 | $0.01254 |
| Opus 5.5 | $0.00037 | $0.00502 |
| Sonnet 5.5 | $0.00018 | $0.00251 |
| Haiku 4.5 | $0.00009 | $0.00125 |
Grade A, and why
tabpfn 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 4d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TabPFN
Deliver a pinned, task-evaluated TabPFN workflow rather than treating a sklearn-like
API as proof of compatibility, permission or predictive quality. TabPFN learns a
synthetic-data prior during pretraining and conditions on a labeled training table
at inference. Its ordinary fit is not necessarily dataset-specific gradient
training; explicit fine-tuning is a distinct operation.
When not to use
- For choosing among trees, TabPFN, TabICL, sequence models and LLMs, load ml-engineering.
- For statistical design/calibration, use data-scientist.
- For language-model SFT/LoRA/RL, use unsloth.
- TabICL has different models/APIs and terms. This skill does not operate it.
First decision and authority
Identify classification versus regression, local package versus hosted client, checkpoint generation, data schema and permission, commercial/production scope, selected accelerator and run destination. Do not silently choose the latest checkpoint: code and weight terms differ by release. A download may involve license acceptance and authentication; task authorization does not authorize accepting terms for the user or sending private tables to a hosted client.
This skill can install packages, download weights, cache labeled tables, run GPU work and write artifacts. Before first mutation:
Confirm the target, scope, and rollback path before acting. Read-only discovery may proceed without confirmation.
Use existing explicit authorization when it supplies these facts. Use isolated run directories and preserve the accepted environment/artifact. Do not delete models, change drivers or open a service as an incidental setup repair.
Guide routing
| Task | Read |
|---|---|
| Understand ICL, choose checkpoints, review applicability and limits | Model and capacity decisions |
| Install/authenticate, local versus hosted, license and offline boundaries | Installation and artifacts |
| Prepare/split data, preserve prediction-time features, calibrate and compare | Evaluation workflow |
| Fine-tune, reload, recover, resolve capacity/column/class failures | Adaptation and recovery |
| Check evidence quality, exact sources and unresolved contradictions | Source ledger |
What ships with it
13 files 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.
- evals/challenges.md 2.0 KB
- evals/evals.json 3.4 KB
- evals/triggers.json 298 B
- README.md 2.0 KB
- references/adaptation.md 2.4 KB
- references/evaluation.md 3.5 KB
- references/models.md 3.6 KB
- references/operations.md 3.7 KB
- references/sources.md 1.7 KB
- scripts/tabpfn_check.py 7.2 KB runs code
- scripts/test_tabpfn_check.py 3.7 KB runs code
- templates/run-record.md 1.2 KB
- templates/task-manifest.json 2.4 KB
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.
- 4d ago First seen · 112 lines · 92 tokens per session scan A 9def61ce7973
tabpfn is a skill published in the GitHub repository magnus919/agent-skills (133 stars, last pushed 2d ago), licensed MIT. It adds 92 tokens to every session and 1,254 once invoked, about $0.0004 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 2026-10-14.
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