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 | $0.00000 | $0.04061 |
| Opus 5 | $0.00000 | $0.02031 |
| Sonnet 5 | $0.00000 | $0.00812 |
| Haiku 4.5 | $0.00000 | $0.00406 |
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 11d 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 — 312 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, ~200 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.
- 11d 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 (11 stars, last pushed 7d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,061 tokens. 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.
Other skills, from other repositories
jupyter-notebook
Iterative Python via live Jupyter kernel (hamelnb).
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.
bioservices
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use…
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…