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 jaechang-hits/SciAgent-Skills --skill anndata-data-structuregit clone --depth 1 https://github.com/jaechang-hits/SciAgent-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/jaechang-hits/sciagent-skills/anndata-data-structure)<a href="https://agentmods.dev/skills/jaechang-hits/sciagent-skills/anndata-data-structure"><img src="https://agentmods.dev/badge/skills/jaechang-hits/sciagent-skills/anndata-data-structure/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/jaechang-hits/sciagent-skills/anndata-data-structure"><img src="https://agentmods.dev/badge/skills/jaechang-hits/sciagent-skills/anndata-data-structure.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 | $0.00076 | $0.06483 |
| Opus 5 | $0.00038 | $0.03241 |
| Sonnet 5 | $0.00015 | $0.01297 |
| Haiku 4.5 | $0.00008 | $0.00648 |
Grade A, and why
anndata-data-structure 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 6d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
2 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.
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.
- 6d ago First seen · 549 lines · 76 tokens per session scan A 3c3a60572c0d
anndata-data-structure is a skill published in the GitHub repository jaechang-hits/SciAgent-Skills (362 stars, last pushed 11d ago), with no licence file. It adds 76 tokens to every session and 6,483 once invoked, about $0.0004 per session on Opus 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 2026-09-03.
Other skills, from other repositories
pytdc
Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic…
datamol
Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking…
python-dependency-version-resolution
Use when when setting up a new conda environment for a Python-based bioinformatics pipeline and you need to confirm that all declared dependencies (e.g., pysam >=0.15.4, bx-python >=0.8.8, numpy >=1.18.1, scipy >=1.4.
python-environment-management
Use when you are preparing to run Hi-C data normalization or read alignment filtering steps that depend on Python modules (iced, pysam, numpy, scipy) and you need to ensure consistent module versions across multiple runs or compute nodes.
python-pandas-data-manipulation
Use when you have precomputed expected contact frequency tables (TSV format with columns like distbp, contactfrequency, nvalid) and need to apply log-binning and smoothing to group distance values into log-spaced bins, aggregate statistics within each bin, and export a cleaned, annotated output.
python-package-api-interface-design
Use when you are building or refactoring a scientific Python library and need to decide how to organize and expose utility functions (e.g., adaptive coarse-graining, filtering, analysis routines) so that end users can import and call them reliably.