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 daeryundf2-prog/LAZYANTIGRAVITY --skill data-scientistgit clone --depth 1 https://github.com/daeryundf2-prog/LAZYANTIGRAVITYWrote 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/daeryundf2-prog/lazyantigravity/data-scientist)<a href="https://agentmods.dev/skills/daeryundf2-prog/lazyantigravity/data-scientist"><img src="https://agentmods.dev/badge/skills/daeryundf2-prog/lazyantigravity/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/daeryundf2-prog/lazyantigravity/data-scientist"><img src="https://agentmods.dev/badge/skills/daeryundf2-prog/lazyantigravity/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 | $0.00054 | $0.01339 |
| Opus 5.5 | $0.00022 | $0.00536 |
| Sonnet 5.5 | $0.00011 | $0.00268 |
| Haiku 4.5 | $0.00005 | $0.00134 |
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 19d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Scientist: Hybrid-Engine Data Processing
Answer data questions through the cheapest engine and surface that can prove the answer, and decide where the computation should live before touching the data.
Execution surfaces: resident kernel first
A persistent REPL/eval kernel (many harnesses expose one for JavaScript and Python) is the default surface. Reason: each one-shot process pays roughly a second of spawn-plus-import overhead and re-scans the input file, while a resident connection amortizes both — after a one-time load, repeat queries return in milliseconds. Exploration is repeat queries, so this difference dominates the session.
- JavaScript kernel (Bun): run
scripts/ensure-js-deps.shonce; it prints the absolute import path for@duckdb/node-api. Dynamic-import it, connect once, query across cells. - Python kernel: the default surface for Python work. duckdb/numpy/matplotlib are
typically resident; Polars and pyarrow come from
scripts/ensure-py-deps.sh, which installs them once into a user cache keyed to the kernel's interpreter —sys.path.insertthe printed directory and import. The interpreter itself is never mutated. - uv lane (
uv run --with ...): isolation for a heavy or crash-prone one-shot that should not take the kernel down. - No kernel (plain-shell harness): the same engines as one-shots —
bun -efor DuckDB-js,uv run python -cfor the Python stack — batching several questions per process.
Per-surface patterns and pitfalls: read references/execution-surfaces.md before first use.
Engine selection
- DuckDB for SQL-shaped work: direct file queries, joins, aggregation, subqueries, window functions. It queries CSV/Parquet/JSON in place without loading, spills to disk past its memory limit, and reads remote files with the same syntax.
- Polars when the pipeline is DataFrame-shaped: expression-chain transforms, reshapes,
streaming datasets past RAM — resident in the Python kernel via
ensure-py-deps.sh. Readreferences/polars-lane.md— the current 1.x API differs from widely-memorized older spellings. - numpy when numeric work goes beyond SQL/DataFrame aggregation: statistical tests, linear algebra, FFT, random sampling.
- matplotlib for every chart — read
references/visualization.mdfirst; it carries the quality bar and a mandatory visual check.
What ships with it
10 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.
- references/execution-surfaces.md 4.0 KB
- references/placement.md 3.3 KB
- references/polars-lane.md 3.5 KB
- references/uv-setup.md 2.7 KB
- references/visualization.md 2.9 KB
- scripts/ensure-js-deps.sh 983 B runs code
- scripts/ensure-py-deps.sh 1.3 KB runs code
- scripts/quick-query.py 3.3 KB runs code
- scripts/setup-uv.ps1 2.3 KB runs code
- scripts/setup-uv.sh 1.8 KB runs code
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.
- 19d ago First seen · 104 lines · 54 tokens per session scan A bf455e10ba7c
data-scientist is a skill published in the GitHub repository daeryundf2-prog/LAZYANTIGRAVITY (14 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 1,339 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 2026-09-27.
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