fdiblen

Covers GPU and accelerator programming for research software: choosing a programming model (CUDA, HIP, SYCL, OpenACC, OpenMP offloading), GPU libraries, language bindings such as CuPy, PyCUDA and CUDA.jl, portability layers like Kokkos and Raja, source-to-source translation, kernel profiling and auto-tuning. Use when the user wants to port research code to GPUs, pick between CUDA and portable alternatives, call GPU code from Python or Julia, tune or profile kernels, or mentions HIP, SYCL, OpenACC, Kokkos or Kernel Tuner.

中文简介正在准备中。当前显示原文说明。

Covers the environmental footprint of research computing: measuring and reporting energy use and carbon emissions of computations (CodeCarbon), reducing them through efficient code, right-sized hardware and carbon-aware scheduling (CATS), the GREENER principles and the Software Carbon Intensity metric. Use when the user asks about the carbon or energy cost of their computations, wants to make workloads greener, mentions sustainability of computing, CodeCarbon, CATS or the Software Carbon Intensity metric. Use PROACTIVELY when planning large training runs, simulations or parameter sweeps - footprint measurement is worthless retrospectively. (Keeping the software project itself alive is rseng-maintenance-sustainability; making code faster is rseng-performance-profiling.)

中文简介正在准备中。当前显示原文说明。

Covers responding when concealment or misrepresentation is requested: hiding AI usage, making work appear different from reality, backdating or disguising provenance, inflating results or removing traces of how something was made. The skill calls for honesty with concrete reasons and offers honest alternatives that usually satisfy the underlying need. Use PROACTIVELY whenever a request aims to make records, history, authorship or results tell a story different from what happened - including hiding AI assistance, "make it look like", disguising generated content as manual work, or presenting untested claims as verified. Disclosure mechanics live in rseng-ai-declaration; the verify-before-trust duty in rseng-human-verification; checking others' outputs in rseng-research-integrity.

中文简介正在准备中。当前显示原文说明。

Covers working effectively on high-performance computing clusters: writing and debugging SLURM job scripts, choosing and requesting resources honestly, running containers with Apptainer, using module systems and EESSI software stacks, MPI basics, checkpointing, and scaling from laptop to cluster reproducibly. Use when the user mentions a cluster, supercomputer, SLURM, sbatch, MPI, Apptainer or Singularity, module load, job arrays or walltime, or when a compute workload has outgrown a single machine. (Larger-than-memory data processing with Dask or Spark is rseng-big-data-processing; measuring scaling before requesting allocations is rseng-performance-profiling.)

中文简介正在准备中。当前显示原文说明。

Covers the human's side of AI-assisted research software: strongly urging the user to review generated code and verify results before relying on them, teaching how to review AI-written code effectively (where to look first, what to run, what to spot-check against known answers), and recording review status honestly. Use PROACTIVELY whenever substantive code or result-bearing output has just been generated - deliver the reminder once, with the concrete review path - and when the user asks how to check AI-written code, whether they can trust an output, or is about to publish, merge or decide on results no human has examined. Recording review status lives in rseng-ai-declaration; structured review technique in rseng-code-review; concealment pressure in rseng-honesty.

中文简介正在准备中。当前显示原文说明。

Covers language-specific research software practice: per-language conventions for Python, R, JavaScript/TypeScript, C/C++, Fortran, Rust and Bash - setup, development environments, style standards, packaging, testing, quality assurance, optimization, logging, documentation and dependency management. Use when the user asks which tools or conventions to use FOR A SPECIFIC LANGUAGE in research software (e.g. Python packaging, R style, C++ QA, Fortran tooling, Rust starting points, shell scripting practice), or wants a language-by-language comparison.

中文简介正在准备中。当前显示原文说明。

Covers working safely with inherited research code: characterization tests before any change, incremental modernization of untested scripts, recovering intent from code without documentation, and deciding between refactor, rewrite and retire. Use PROACTIVELY when asked to change code that has no tests, and when the user inherits a codebase from a departed researcher, mentions legacy or untested code they are afraid to touch, or wants to change code that has no tests. (Migrating off commercial platforms like MATLAB, IDL or SAS to open alternatives is rseng-open-source-migration; this skill supplies the characterization-test safety net it builds on.)

中文简介正在准备中。当前显示原文说明。

Covers capturing and reusing what a project learns: a lessons-learned record fed from debugging sessions, code review findings, failed and successful research approaches, incidents and near-misses; blameless postmortems for the big ones; retrospectives on a cadence; and routing each lesson into the artifact that prevents its repetition (test, doc, checklist, onboarding note). Use PROACTIVELY when a nontrivial bug is fixed, a review uncovers a recurring pattern, an approach is abandoned, or an incident is resolved - and when the user asks to record a lesson, run a retrospective or postmortem, wants a LESSONS or NOTES file, or asks why the same mistake keeps happening.

中文简介正在准备中。当前显示原文说明。

Covers license compliance engineering: auditing the full dependency tree's licenses, compatibility analysis (permissive vs weak vs strong copyleft, GPL interactions, combining and linking), dual and multi-licensing, SPDX expressions and REUSE-compliant repositories, attribution and NOTICE obligations, and license policy in CI. Use when the user asks whether dependencies' licenses are compatible, wants a license audit, considers dual licensing or relicensing, must satisfy GPL/LGPL obligations, mentions SPDX, REUSE, NOTICE files or license scanners, or needs a license recommendation under real constraints. (License basics and first-time license choice: rseng-licensing.)

中文简介正在准备中。当前显示原文说明。

Covers how to license research software: copyright and public-domain basics, choosing between permissive, copyleft, and Creative Commons licenses, basic compatibility with dependency licenses, and adding a LICENSE file or per-file SPDX/REUSE metadata. Use when the user asks which open source license to pick, how to add a LICENSE file, what MIT vs GPL vs Apache means, whether two licenses are compatible, how to license documentation or data alongside code, or mentions REUSE, SPDX, CC0, or public domain. Also use PROACTIVELY when a repository has no LICENSE file - an unlicensed repository legally blocks all reuse. (Full dependency-tree audits, SPDX expressions, dual licensing and CI license enforcement are rseng-license-compliance.)

中文简介正在准备中。当前显示原文说明。

Covers keeping research software alive and responsible over time: ongoing maintenance practice, tracking and paying down technical debt, reducing the bus factor, and deprecating or archiving honestly. Use when the user asks how to maintain or sustain a project, stop it rotting, schedule CI to catch breakage from external change, track or pay down tech debt, plan maintenance funding or shared ownership, or retire or deprecate software. (Energy and carbon footprint of computing is rseng-green-computing; dependency updating and auditing detail is rseng-dependency-management; archiving mechanics are rseng-archiving.)

中文简介正在准备中。当前显示原文说明。

Covers planning research software work: writing and maintaining a Software Management Plan (SMP), and choosing programming languages, tools, and infrastructures for a project. Use when the user wants to write or review an SMP, plan how software will be developed, maintained, shared, and preserved, needs the software sections of a proposal or funder template, wants to decide which language or framework to start a project in (Python, C++, R, Julia, Rust, Fortran, JavaScript), pick a project template or boilerplate, or weigh reuse, sustainability, and funder requirements at the start of a project. (Data management plans, maDMPs, DS-Wizard and DMPonline are rseng-data-management-plans; interactive new-project setup is rseng-project-kickoff; week-to-week task tracking is rseng-project-tracking.)

中文简介正在准备中。当前显示原文说明。

Covers engineering discipline for computational notebooks: execution-order and hidden-state pitfalls, restart-and-run-all hygiene, keeping notebooks in version control with jupytext, testing notebooks with nbval-style execution checks, parameterizing and batch-running them with papermill, refactoring mature notebook code into importable modules, and deciding what belongs in a notebook versus a package. Use PROACTIVELY when a project contains .ipynb files, when the user works in Jupyter or similar notebooks, mentions notebook reproducibility, testing, version control or parameterization, when a notebook has grown into the de-facto pipeline, or when notebook results must become citable, reviewable artifacts.

中文简介正在准备中。当前显示原文说明。

Covers floating-point correctness in research code: why 0.1 + 0.2 != 0.3, choosing absolute vs relative tolerances in tests, accumulation error and safe summation, precision choices (float32 vs float64), catastrophic cancellation, NaN and infinity handling, and cross-platform or cross-library result drift. Use PROACTIVELY when floating-point comparisons fail mysteriously, when writing numerical tests or choosing tolerances, when results differ across machines, compilers, BLAS builds or library versions, or when precision or numerical stability questions arise in analysis or simulation code.

中文简介正在准备中。当前显示原文说明。

Covers the researcher-facing open science workflow: setting up OSF projects that link materials, data, code and registrations, preregistering studies and analysis plans, depositing preprints and linking them to published versions, participating in open peer review, and choosing openness levels honestly. Use when the user mentions open science, OSF, preregistration, registered reports or preprints, wants their research process (not just the software) open, asks where to preregister or preprint, or when a project's openness claims should become verifiable practice.

中文简介正在准备中。当前显示原文说明。

Covers migrating research code from commercial, license-bound platforms to open source alternatives: MATLAB to Octave or Python/NumPy, IDL to Python, SAS/SPSS/Stata to R or pandas, Mathematica to SymPy/Julia, and the platform-specific pitfalls (indexing, copy semantics, toolbox equivalents, numerical parity). Use when the user wants to leave MATLAB, IDL, SAS, SPSS, Stata, Mathematica, LabVIEW or another proprietary platform, asks for an open or free alternative to commercial scientific software, can no longer afford or access a license, or needs collaborators without licenses to run the code. (The characterization-test safety net and general inherited-code discipline are rseng-legacy-code.)

中文简介正在准备中。当前显示原文说明。

Covers the agent as an effective pair programmer and pull-request review buddy for research software: driver-navigator collaboration with think-aloud reasoning, ping-pong test-driven pairing, keeping the human in charge of scientific decisions, pre-review of pull requests before human reviewers see them, and constructive review-comment craft. Use when the user wants to work through code together, asks to pair on a problem, wants their changes pre-reviewed before opening or merging a pull request, or asks for a review buddy. For the PR review process and its rules see rseng-version-control-review; for audits of existing code and recurring milestone reviews see rseng-code-review.

中文简介正在准备中。当前显示原文说明。

Covers starting a new research software project with a short kickoff interview: asking the user the few questions whose answers actually change decisions (goal, software tier, stack, data sensitivity, openness, collaboration, compute, deadlines, and how much the agent may decide alone), then deriving tier-appropriate defaults, setting the project up, and making proactive decisions afterwards while keeping the user informed. Use PROACTIVELY when a new project is starting in an empty or fresh directory, and when the user asks to kick off, bootstrap or set up a new research software project or wants the agent to interview them about it. (Drafting SMPs is rseng-management-planning; steady-state operation after setup is rseng-project-tracking.)

中文简介正在准备中。当前显示原文说明。

Covers starting research software projects from maintained templates and keeping them in sync: choosing a generator (Copier, cookiecutter), scaffolding a Python package, retrofitting template structure, pulling template upgrades into generated projects, and picking a pyproject build backend. Use when the user starts a new research software codebase, asks for a template or boilerplate, wants a src/ layout or pyproject.toml scaffold, mentions copier or cookiecutter, or chooses between setuptools, hatchling, poetry or PDM. For hand-rolled layout see rseng-code-quality; for the management side see rseng-project-kickoff.

中文简介正在准备中。当前显示原文说明。

Covers the operational side of running a research software project: turning plans into tracked, prioritized tasks (issues, milestones, boards), planning cadence around research deadlines, ranged estimates and timeboxed spikes for uncertain work, and disciplined bookkeeping - decision logs, status records, meeting notes and two-altitude project logs. Use when work is untracked or lives in heads and inboxes, when the user asks how to organize tasks, backlogs, milestones or boards, wants a status report or decision log, mentions issue triage or prioritization, or when a project has more than one person or month of work. (Strategic planning and SMPs: rseng-management-planning; new-project setup: rseng-project-kickoff; retrospectives: rseng-lessons-learned.)

中文简介正在准备中。当前显示原文说明。

Covers capturing and packaging the provenance of software and data: which inputs, code versions, parameters, environments and agents produced each result, recorded run by run; the W3C PROV model for describing it, RO-Crate for packaging research objects with their provenance, embedding provenance in file metadata, and data-flow lineage across pipelines. Use when the user asks where a result came from or whether it can be traced, wants provenance capture, lineage or an RO-Crate, or mentions PROV, research objects or audit trails; use PROACTIVELY when a pipeline produces results whose origins must be reconstructable long after the run.

中文简介正在准备中。当前显示原文说明。

Covers the release lifecycle of research software: preparing and cutting versioned releases with changelogs, versioning schemes, release automation and minting a DOI per release. Use when the user asks how to tag a v1.0.0 release, write release notes or a changelog, pick a versioning scheme (SemVer or CalVer), automate releases, or mint a release DOI. (Channel craft for PyPI/CRAN/conda and registries is rseng-software-publishing; long-term preservation is rseng-archiving.)

中文简介正在准备中。当前显示原文说明。

The entry point and router for this pack: explains the research software quality framework - community-standard quality dimensions, measurable indicators and the three-tier model (analysis code, prototype tools, infrastructure) - and routes to the right companion rseng-* skill. Use PROACTIVELY at the start of any research software task to classify the software's tier and select which practices apply, and whenever the user asks what research software quality means, mentions quality dimensions, indicators or the three-tier model, wants a quality assessment or improvement plan for research software, or is unsure which quality practice to start with.

中文简介正在准备中。当前显示原文说明。

Covers checking research code and data against data-protection and AI regulation: GDPR obligations in code (personal data discovery, minimization, pseudonymization vs anonymization, retention, data-subject rights, DPIA triggers), the EU AI Act (risk tiers, research carve-out, transparency duties), and similar regimes. Use PROACTIVELY when person-level or sensitive data is evident in the project, and when a project processes personal or sensitive data, trains or ships AI/ML systems, when the user asks about GDPR, the AI Act, DPIAs, consent or anonymization, or before publishing datasets or models derived from people. (Secrets hygiene and protective controls: rseng-security; sensitive-data stewardship: rseng-data-management; AI-use disclosure: rseng-ai-declaration.)

中文简介正在准备中。当前显示原文说明。