What is grill-me-data-science?

A follow-up planning skill for data-science projects, where the best model, inputs, and data preparation may need to be discovered through experiments. It adds workflow, measurement, and experiment-recording guidance to an existing destination plan.

Use it after a general destination plan to define staged experiments, exploration limits, evaluation measures, and experiment logs.

Software projects often define success in advance, but data-science work may require testing alternatives before the right approach is known. This skill helps make that discovery process explicit.

How haakonbull/autosprint configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to haakonbull/autosprint. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/haakonbull/autosprint/master/.claude/skills/grill-me-data-science/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/haakonbull/autosprint

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/haakonbull/autosprint/grill-me-data-science/github.svg)](https://agentmods.dev/skills/haakonbull/autosprint/grill-me-data-science)

Your own site

<a href="https://agentmods.dev/skills/haakonbull/autosprint/grill-me-data-science"><img src="https://agentmods.dev/badge/skills/haakonbull/autosprint/grill-me-data-science/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/haakonbull/autosprint/grill-me-data-science"><img src="https://agentmods.dev/badge/skills/haakonbull/autosprint/grill-me-data-science.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,720 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00076	$0.01720
Opus 5.5	$0.00030	$0.00688
Sonnet 5.5	$0.00015	$0.00344
Haiku 4.5	$0.00008	$0.00172

Measured 2mo ago against content hash 0bb10e7680f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

grill-me-data-science 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 2mo 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.

.claude/skills/grill-me-data-science/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Second-pass grilling that complements grill-destination. Run this after the general destination interview is complete (or after confirming autosprint/destination.md already has the usual sections filled in). The goal is to add data-science-specific content to the same autosprint/destination.md under the existing ## Desired behavior and ## Success criteria sections, and — if the user agrees — a new ## Experiment log reference to autosprint/data_science_results.md.

Data-science projects have a property normal code projects don't: you don't know in advance what "done" looks like. The correct model, the correct features, the correct preprocessing — all of those are discovered, not specified. destination.md must therefore describe the process as well as the outcome.

Before starting

Read autosprint/destination.md. If it's missing or has only the seed content, stop and tell the user: "Run grill-destination first — this skill layers on top of that one."
Read autosprint/adr.md if it exists — some staged-workflow decisions may already be recorded there (e.g. "use MLflow for experiment tracking"). Don't re-ask those.
Quote the current ## Purpose and ## Desired behavior sections back to the user and confirm this is a data-science project before starting. A project that just happens to use numpy is not a data-science project — the skill is for projects where model/method/feature selection is part of the loop.

Interview style

Hard on metrics. A metric the planning phase can't compute has no ability to drive decisions. If the user says "better accuracy", push for "AUC on holdout set ≥ 0.85".
Hard on staged criteria. "Enough exploration" is meaningless if it's not tied to a number of experiments, a time budget, or a plateau criterion.
Soft on stage boundaries being fuzzy. It's legitimate to say "the planning phase decides when to move from exploration to selection" — the loop can make that call each replan.

Read the full file on GitHub · 103 lines

Changes

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.

2mo ago First seen · 103 lines · 76 tokens per session scan A 0bb10e7680f1

Subscribe to this mod's changes

grill-me-data-science is a skill published in the GitHub repository haakonbull/autosprint (5 stars, last pushed 4d ago), licensed MIT. It adds 76 tokens to every session and 1,720 once invoked, about $0.0003 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 2026-08-31.

Planning Scientific

grill-me-data-science next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
grill-me-data-sciencehaakonbull/autosprint	Skill	5	4d ago	MIT	A	76	Claude Code
instrument-data-to-allotropeanthropics/knowledge-work-plugins	Skill	35k	2d ago	Apache-2.0	A	123	Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	83	Claude Code
matlabK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	44	Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	71	Claude Code, Codex
research-engineerdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	43	Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmed	Skill	5.8k	yesterday	Apache-2.0	A	205	Claude Code

autosprint: Skill for Claude Code