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 a-attia/scicomp-research-skills --skill research-software-engineeringgit clone --depth 1 https://github.com/a-attia/scicomp-research-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/a-attia/scicomp-research-skills/research-software-engineering)<a href="https://agentmods.dev/skills/a-attia/scicomp-research-skills/research-software-engineering"><img src="https://agentmods.dev/badge/skills/a-attia/scicomp-research-skills/research-software-engineering/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/a-attia/scicomp-research-skills/research-software-engineering"><img src="https://agentmods.dev/badge/skills/a-attia/scicomp-research-skills/research-software-engineering.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 | $0.00236 | $0.04945 |
| Opus 5.5 | $0.00094 | $0.01978 |
| Sonnet 5.5 | $0.00047 | $0.00989 |
| Haiku 4.5 | $0.00024 | $0.00494 |
Grade A, and why
research-software-engineering 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 22d 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 — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Software Engineering
How this skill is organised (progressive disclosure)
This skill follows the three-level progressive disclosure pattern
also used by agent-resource-discipline and codified by Anthropic's
skill-creator:
- Level 1 (always in context once loaded): this
SKILL.md, ~340 lines. Contains the universal principles + a workflow table showing which reference to load for which task. - Level 2 (loaded on demand by name): the
references/*.mdfiles -- one per discipline. Loaded only when a session actually exercises that discipline. - Level 3 (planned future work): numerical-correctness
enforcement hooks (golden-output-diff guard, lockfile-drift guard,
experiment-id guard, commit-pin guard) -- specification deferred,
same rationale as
agent-resource-discipline's planned hooks.
Always load this SKILL.md when the trigger fires. Load specific references only when the current sub-task requires them.
When to load this skill
Load this skill at the start of any session that will involve any of:
- writing or extending a scientific-computing library or research code (numerical methods, PDE solvers, inverse problems, OED, UQ, scientific ML);
- adding tests to numerical code;
- packaging / releasing research code (pyproject.toml, GitHub Actions, Zenodo);
- preparing code for a paper submission (commit-pinning, archiving);
- auditing existing scientific software for correctness, reproducibility, or maintainability concerns;
- API design decisions for a Pythonic / JAX / dolfinx / petsc4py scientific library;
- performance tuning, GPU offload, MPI parallelisation;
- extracting a reusable library out of experiment scripts.
If the session is a paper-writing session that does NOT touch code,
load research-paper-writing instead. If the session is mixed (paper +
code), load both.
Core principle: numerical correctness is not optional
A scientific-computing program that returns a plausible-looking wrong number is much worse than one that crashes. The agent MUST treat numerical correctness as the highest-priority concern, ahead of performance, ahead of API ergonomics, ahead of style. Specifically:
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.
- 22d ago Changed · +70 lines · +236 tokens per session 9799db12b1c0
- 2mo ago First seen · 312 lines · 0 tokens per session scan A c42b7ebad417
research-software-engineering is a skill published in the GitHub repository a-attia/scicomp-research-skills (13 stars, last pushed 23d ago), licensed MIT. It adds 236 tokens to every session and 4,945 once invoked, about $0.0009 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 2026-08-30.
research-software-engineering compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| research-software-engineeringa-attia/scicomp-research-skills | Skill | 13 | 23d ago | MIT | A | 236 | Claude Code, Codex |
| jupyter-notebookNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 18 | Claude Code, Codex |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| pennylaneK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 74 | Claude Code |
| cuequivariance-jaxNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 38 | Claude Code, Codex |
| cuequivariance-torchNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 37 | Claude Code, Codex |
| cuopt-numerical-optimization-apiNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 51 | Claude Code, Codex |