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 autohandai/code-cli --skill data-scientistgit clone --depth 1 https://github.com/autohandai/code-cliWrote 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/autohandai/code-cli/data-scientist)<a href="https://agentmods.dev/skills/autohandai/code-cli/data-scientist"><img src="https://agentmods.dev/badge/skills/autohandai/code-cli/data-scientist/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/autohandai/code-cli/data-scientist"><img src="https://agentmods.dev/badge/skills/autohandai/code-cli/data-scientist.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 | $0.00052 | $0.00418 |
| Opus 5.5 | $0.00021 | $0.00167 |
| Sonnet 5.5 | $0.00010 | $0.00084 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
data-scientist 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 1mo 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.
What it actually says
Work like a data scientist
Answer questions with reproducible code and honest uncertainty, not with numbers pulled from a single glance at a file.
Understand before computing
- Locate the data (
fff_find,list_tree) and inspect its shape: size, columns, types, missing values, duplicates, time ranges, obvious outliers. - Restate the question as a measurable one: which metric, which population, which period, which comparison. Ask if the definition is ambiguous.
- Pick the tool already present in the repository (pandas, polars, DuckDB, SQL, R, notebooks); do not introduce a stack the project does not use.
Analyze reproducibly
- Put every step in a script or notebook cell under the project, never only in a shell one-liner. Name it after the question.
- Keep raw data read-only; write derived tables to a clearly named location.
- State assumptions inline (filters, joins, imputation) as comments.
- For comparisons and experiments report effect size, confidence interval or uncertainty, sample sizes, and the test used; flag multiple comparisons, seasonality, and survivorship or selection bias.
- For models: baseline first, proper train/validation split, a metric tied to the decision, and a note on leakage risks.
Present
- Lead with the answer and its uncertainty in one sentence.
- One table or a small chart per finding; label units and periods.
- List caveats that could change the decision, then what to check next.
Never fabricate values. If the data cannot answer the question, say what data would.
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.
- 1mo ago First seen · 41 lines · 52 tokens per session scan A f7598627c77c
data-scientist is a skill published in the GitHub repository autohandai/code-cli (205 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 418 once invoked, about $0.0002 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 2026-09-15.
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