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BD tabpfn

Operate and evaluate TabPFN for local tabular classification, regression and dataset-specific fine-tuning: checkpoint selection, weight licensing, environment setup, training-context leakage, calibration, resource budgeting and reproducible inference artifacts. Use for TabPFN package or checkpoint workflows. Do not use for generic model selection, operating TabICL, or language-model fine-tuning; use ml-engineering, the selected tool, or unsloth respectively.

magnus919/agent-skills Agent Skills author: magnus919 MIT 14 files · 2 scripts body ≈ 1 364 tokens Open the sourcegithub.com↗ analyzed 19 d ago

Operate and evaluate TabPFN for local tabular classification, regression and dataset-specific fine-tuning: checkpoint selection, weight licensing, environment…

As a process D 47/100 · Unfinished process — References files that are not bundled: ../ml-engineering/SKILL.md, ../data-scientist/SKILL.md, ../unsloth/SKILL.md

AnalyzerData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
D
47/100
Unfinished process
References files that are not bundled: ../ml-engineering/SKILL.md, ../data-scientist/SKILL.md, ../unsloth/SKILL.md
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../ml-engineering/SKILL.md
  • warning missing-ref reference to a missing file: ../data-scientist/SKILL.md
  • warning missing-ref reference to a missing file: ../unsloth/SKILL.md

Process rating: all ten parameters 47/100

Will not run. References files that are not bundled: ../ml-engineering/SKILL.md, ../data-scientist/SKILL.md, ../unsloth/SKILL.md
  • 0Tools and files. 3 referenced file(s) missing: ../ml-engineering/SKILL.md, ../data-scientist/SKILL.md, ../unsloth/SKILL.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1364 tokens
  • low No test case covers injection arriving through data

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +3Output format is not stated: the model decides each time
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 462: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.