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 shikaijieskj/science-skills --skill science-data-codegit clone --depth 1 https://github.com/shikaijieskj/science-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/shikaijieskj/science-skills/science-data-code)<a href="https://agentmods.dev/skills/shikaijieskj/science-skills/science-data-code"><img src="https://agentmods.dev/badge/skills/shikaijieskj/science-skills/science-data-code/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/shikaijieskj/science-skills/science-data-code"><img src="https://agentmods.dev/badge/skills/shikaijieskj/science-skills/science-data-code.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 | $0.00093 | $0.00574 |
| Opus 5 | $0.00046 | $0.00287 |
| Sonnet 5 | $0.00019 | $0.00115 |
| Haiku 4.5 | $0.00009 | $0.00057 |
Grade A, and why
science-data-code 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 10d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Science Data and Code
Use this skill to connect the manuscript's claims to inspectable data, code, materials, and metadata. Treat availability as part of the evidence chain.
Default stance
- Do not invent DOIs, accession numbers, repository records, licenses, embargoes, ethics approvals, access committees, or code status.
- Prefer public, stable, discipline-appropriate repositories.
- Separate data, code, materials, protocols, models, and third-party resources.
- Flag
available upon reasonable requestas weak unless there is a clear legal, ethical, commercial, privacy, or third-party restriction. - Verify current target-journal policy before final submission wording.
Open extra files
| File | Open when |
|---|---|
| references/availability-patterns.md | Need ready-to-adapt data/code/materials statement patterns |
| references/repository-checklist.md | Need repository, identifier, license, metadata, or FAIR checks |
| references/source-basis.md | Need source hierarchy or policy-vs-advice boundaries |
Workflow
- Identify target journal and article type.
- Inventory every asset needed to support the main and supplementary claims: raw data, processed data, figure source data, code, scripts, models, protocols, materials, third-party datasets, and restricted data.
- Map each asset to one access route:
public repository,controlled access,within paper/supplement,reused public source,third-party restricted,available on justified request, ornot yet provided. - Choose a repository and identifier strategy before drafting.
- Draft data/code/materials availability text with asset-to-location mapping.
- Add missing-information flags.
- Run the FAIR/reproducibility checklist.
Output format
Data and code availability
[ready-to-paste statement]
Asset map
| Asset | Supports claim/figure | Location | Identifier | Access conditions | Missing info |
Repository actions
- [specific actions]
Risk flags
- [specific flags or "None"]
What ships with it
4 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.
- 10d ago First seen · 62 lines · 93 tokens per session scan A c52ef4e30915
science-data-code is a skill published in the GitHub repository shikaijieskj/science-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 93 tokens to every session and 574 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…