rseng-agent-skills

In the author's own words, from each skill's SKILL.md — showing the first 30 of 67 files.

• rseng-agent-security

  Covers operating AI coding agents securely: auditing whether the agent runs sandboxed, permission and dangerous-command configuration, containerized environments, secrets kept out of context, network egress and token privileges, prompt-injection risk, and the privacy of agent use itself. Use PROACTIVELY when permission gating is broadly disabled (skip-permissions modes), when secrets are visible…

  skills/rseng-agent-security/SKILL.md on GitHub (opens in a new tab)

• rseng-ai-declaration

  Covers declaring AI involvement with the AI Declaration Format (https://ai-declaration.org): creating and maintaining an aidecl.yaml that records which AI tools and agents were used, what each contributed, when and how much, plus the README disclosure footnote, JSON/JSON-LD export and CI validation. Machine-readable disclosure is the practice; this format is one implementation. Use PROACTIVELY wh…

  skills/rseng-ai-declaration/SKILL.md on GitHub (opens in a new tab)

• rseng-archiving

  Covers long-term archiving of research software and data: Software Heritage save requests and SWHID persistent identifiers, Zenodo deposits with versioned DOIs and forge integration, choosing domain and institutional repositories, deciding what to archive (code, data, environments, documentation) and when, and archiving at project retirement. Use when the user wants software or data preserved bey…

  skills/rseng-archiving/SKILL.md on GitHub (opens in a new tab)

• rseng-big-data-processing

  Covers processing research data that outgrows one machine's memory: out-of-core and chunked computation, Dask for scaling the scientific Python stack, Spark for distributed tabular pipelines, lazy evaluation, partitioning strategies, idempotent and restartable batch jobs, and knowing when NOT to distribute. Use when datasets no longer fit in memory, when the user mentions Dask, Spark, out-of-core…

  skills/rseng-big-data-processing/SKILL.md on GitHub (opens in a new tab)

• rseng-ci-cd

  Covers continuous integration and delivery for research software: CI/CD concepts, automating builds and tests with GitHub Actions and GitLab CI/CD, and wiring an organization's GitLab CI infrastructure to a GitHub-hosted project. Use when the user asks to set up CI, write a pipeline or workflow file (.github/workflows, .gitlab-ci.yml), add automated builds/tests on push or pull request, publish a…

  skills/rseng-ci-cd/SKILL.md on GitHub (opens in a new tab)

• rseng-citation-hygiene

  Covers verifying that every citation is real, correct and current: checking references in manuscripts, READMEs, references files and code metadata against Crossref and OpenAlex, screening cited DOIs against the Retraction Watch database, catching fabricated or mis-attributed citations (a documented AI failure mode), and keeping bibliographies and CITATION.cff files resolvable. Use PROACTIVELY bef…

  skills/rseng-citation-hygiene/SKILL.md on GitHub (opens in a new tab)

• rseng-citation-metadata

  Covers making research software citable and contributors credited: writing CITATION.cff, describing software with CodeMeta (codemeta.json), minting DOIs and ORCIDs, and tracking contributors of every kind. Use when the user asks how to make software citable, add CITATION.cff or codemeta.json, obtain a DOI, ensure contributors get credit, or mentions CFF, CodeMeta, ORCID, CRediT or persistent iden…

  skills/rseng-citation-metadata/SKILL.md on GitHub (opens in a new tab)

• rseng-code-quality

  Covers writing readable research code and structuring software projects: naming, formatting, style guides, linters and formatters, pre-commit hooks, modular design, and a conventional directory layout with top-level metadata files. Use when the user asks how to make code readable or clean, pick or enforce a style guide, set up linting/formatting or pre-commit, name variables and functions, organi…

  skills/rseng-code-quality/SKILL.md on GitHub (opens in a new tab)

• rseng-code-review

  Covers reviewing existing code and whole projects, not just new diffs: structured codebase audits that produce ranked findings and then implement the agreed improvements, recurring project reviews after major tasks and milestones, review scoping by risk and tier, and turning review findings into tracked work and lessons. Use PROACTIVELY after major tasks and milestones, and when the user asks for…

  skills/rseng-code-review/SKILL.md on GitHub (opens in a new tab)

• rseng-community-governance

  Covers building and governing a community around research software: CONTRIBUTING guides, codes of conduct, governance models and decision-making, contributor recognition policy, issue and discussion hygiene, and handing over or sharing maintainership. Use when a project wants external contributors, when the user asks for a CONTRIBUTING.md, code of conduct or governance document, when maintainer b…

  skills/rseng-community-governance/SKILL.md on GitHub (opens in a new tab)

• rseng-community-metrics

  Covers measuring community health with CHAOSS-style metrics: contributor growth and retention, bus factor, first-response and review latency, the casual-to-regular contributor conversion funnel, and organizational diversity - computed from forge data, interpreted against project stage, and turned into community actions rather than vanity dashboards. Use when the user asks how healthy their commun…

  skills/rseng-community-metrics/SKILL.md on GitHub (opens in a new tab)

• rseng-contributor-onboarding

  Covers turning users into contributors and contributors into regulars: curating genuinely good first issues, onboarding paths and checklists generated from the repository, first-PR shepherding, mentorship and buddy structures, and measuring where the contribution funnel leaks. Use when a project wants contributors but gets none, when first-time contributors do not return, when the user asks for g…

  skills/rseng-contributor-onboarding/SKILL.md on GitHub (opens in a new tab)

• rseng-data-management-plans

  Covers data management plans (DMPs) for research projects: what funders require, drafting a DMP from the project's actual data reality (types, volumes, storage, sharing, preservation, responsibilities, costs), machine-actionable DMPs (RDA common standard, Data Stewardship Wizard, DMPonline funder templates), and keeping the plan synchronized with practice. Use when a proposal or project needs a D…

  skills/rseng-data-management-plans/SKILL.md on GitHub (opens in a new tab)

• rseng-data-management

  Covers research data management around software: organizing and documenting datasets (layout, data dictionaries), keeping data out of git while versioning it properly (DVC, git-annex, DataLad), FAIR data and metadata standards, depositing data with DOIs in repositories such as Zenodo, licensing data, and handling sensitive or personal data. Use PROACTIVELY when a project reads or produces dataset…

  skills/rseng-data-management/SKILL.md on GitHub (opens in a new tab)

• rseng-debugging

  Covers systematic debugging of research software: hypothesis-driven diagnosis instead of guess-and-change, building minimal reproducers, bisecting across commits, data and parameters, debugging scientific failure modes (wrong numbers rather than crashes, nondeterminism, scale-dependent bugs), debugger and print-discipline mechanics, and turning every fix into a regression test. Use when the user…

  skills/rseng-debugging/SKILL.md on GitHub (opens in a new tab)

• rseng-defensive-coding

  Covers defenses against silently wrong research results: validating data at boundaries (schemas, assertions, sanity checks), explicit physical units and quantities in code (pint/astropy-style), disciplined randomness (explicit seeded generators, parallel streams), and fail-loud handling of NaN and missing data. Use PROACTIVELY when code ingests external or instrument data, when values carry physi…

  skills/rseng-defensive-coding/SKILL.md on GitHub (opens in a new tab)

• rseng-dependency-management

  Covers the full lifecycle of third-party dependencies: vetting a library before adoption on every axis that matters - suitability, license, trust and vulnerabilities including the transitive tree, documentation, maintenance and version currency - then keeping dependencies current with lockfiles and automated updates, and replacing them when they rot. Use PROACTIVELY whenever a new dependency is a…

  skills/rseng-dependency-management/SKILL.md on GitHub (opens in a new tab)

• rseng-discovery

  Covers discovering the research landscape around a topic or project: finding relevant publications (OpenAlex, arXiv, Zenodo, JOSS, Semantic Scholar, Google Scholar) and finding related software - libraries, packages, tools, platforms and competitor or alternative projects - across software registries, archives, package indexes, public forges and curated awesome lists. Use when the user asks what…

  skills/rseng-discovery/SKILL.md on GitHub (opens in a new tab)

• rseng-documentation

  Covers how to document research software at every level: writing a README, code-level docs (comments, docstrings, API and CLI help), project docs (INSTALL, CONTRIBUTING, LICENSE, CITATION, changelog), publishing hosted documentation with Read the Docs, and capturing a Research Software Story. Use when the user asks how to write or improve a README, decide what documentation their project needs, a…

  skills/rseng-documentation/SKILL.md on GitHub (opens in a new tab)

• rseng-fact-checking

  Covers verifying facts and sources at the content level: checking that a cited source actually contains and supports the claim it is cited for (claim-source alignment), assessing source trustworthiness (peer-review status, venue reputation, predatory-publishing signals, primary vs secondary), and flagging fabricated or misattributed support. Use PROACTIVELY whenever the agent itself asserts check…

  skills/rseng-fact-checking/SKILL.md on GitHub (opens in a new tab)

• rseng-fair-ml

  Covers applying FAIR principles to machine learning artifacts: making models findable and reusable with model cards and rich repository metadata, documenting datasets with Croissant and datasheet-style records, licensing models and weights, linking the model-data-code-paper cluster with persistent identifiers, and the RDA FAIR4ML metadata direction. Use when a project trains, fine-tunes, publishe…

  skills/rseng-fair-ml/SKILL.md on GitHub (opens in a new tab)

• rseng-fair-software

  Covers how to apply the FAIR principles - findable, accessible, interoperable, reusable - to research software, and how to assess a project's FAIRness. Use when the user asks how to make software FAIR, wants help with findability, discoverability, or software reuse, mentions metadata, persistent identifiers, DOIs, registries, or software citation in a FAIR context, or asks to run a FAIR self-asse…

  skills/rseng-fair-software/SKILL.md on GitHub (opens in a new tab)

• rseng-fairguard

  Covers assessing research software against the 17 FAIR4RS principles with FAIRGuard (https://www.fairguard.org): compliance scores and levels (bronze to platinum), assessment profiles, quality gates for CI, .fairguard.yml configuration, per-indicator skips, and acting on findings. Use PROACTIVELY on research software projects - at repo intake, before releases, after adding publication metadata -…

  skills/rseng-fairguard/SKILL.md on GitHub (opens in a new tab)

• rseng-gpu-computing

  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 alt…

  skills/rseng-gpu-computing/SKILL.md on GitHub (opens in a new tab)

• rseng-green-computing

  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 wor…

  skills/rseng-green-computing/SKILL.md on GitHub (opens in a new tab)

• rseng-honesty

  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 ma…

  skills/rseng-honesty/SKILL.md on GitHub (opens in a new tab)

• rseng-hpc-computing

  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 Si…

  skills/rseng-hpc-computing/SKILL.md on GitHub (opens in a new tab)

• rseng-human-verification

  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…

  skills/rseng-human-verification/SKILL.md on GitHub (opens in a new tab)

• rseng-language-guides

  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 softw…

  skills/rseng-language-guides/SKILL.md on GitHub (opens in a new tab)

• rseng-legacy-code

  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 c…

  skills/rseng-legacy-code/SKILL.md on GitHub (opens in a new tab)

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