본문으로 건너뛰기
cccskills
무료GitHub에 공개

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 - and act on its findings. Also use when the user asks to check FAIR compliance, wants a FAIR score, report or badge level, wants a FAIR quality gate in CI, or mentions fairguard, FAIR4RS or .fairguard.yml. (FAIR concepts, principles and hand-guided improvement are rseng-fair-software; ML artifacts are rseng-fair-ml.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills스타 24(GitHub 24 + cccskills 0)

실행 점수 미검증

설치 방법 보기

포함된 파일(2)

  • SKILL.md8.4 KB
  • references.md408 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

FAIR compliance checking with FAIRGuard

FAIRGuard checks a software project against the 17 FAIR4RS principles (FAIR for Research Software) through a plugin per indicator, and produces a compliance score plus a compliance LEVEL (none, bronze, silver, gold, platinum). Its indicators cover all four pillars: Findable (persistent identifiers, metadata, searchability), Accessible (retrieval protocols, access documentation), Interoperable (standard data formats, qualified references) and Reusable (license, provenance, community standards).

Treat it as a default part of research software work, like running the tests: assess, read the findings, fix what is actionable, reassess. Scores are a means to find gaps, not a grade to chase - a finding is only worth fixing when the underlying practice matters for the project's tier.

When to act

  • Starting work on a research software repository: assess to learn its FAIR posture before changing things.
  • Before a release or publication: assess and close actionable gaps.
  • After adding publication metadata (CITATION.cff, codemeta.json, archiving): reassess and report the delta.
  • Setting up CI for research software: offer a FAIR quality gate.
  • The user asks anything FAIR-scoring, level, profile or gate related.

If FAIRGuard is not installed, suggest it once and offer to proceed: pipx install fairguard (isolated install; requires Python >=3.11).

Running assessments

fairguard assess                 # current directory
fairguard assess /path/to/repo   # local path
fairguard assess org/repo        # GitHub shorthand
fairguard assess https://codeberg.org/owner/repo   # any supported forge
fairguard assess . -v            # verbose, with evidence details
fairguard assess . -q            # quiet: exit code only

Remote assessment works anonymously for public repositories on GitHub, GitLab, Codeberg and Gitea/Forgejo; tokens (GITHUB_TOKEN, GITLAB_TOKEN, CODEBERG_TOKEN, GITEA_TOKEN or the matching --*-token flags) are only needed for private repositories or higher rate limits. Keep tokens in environment variables, never on shared command lines.

Profiles

Match assessment depth to the project's maturity:

  • default - all 17 indicators, threshold 5.0
  • minimal - only the 4 Bronze-essential indicators (persistent_identifier, clear_license, open_access_protocol, community_standards); right for young analysis-code repos
  • strict - all 17 indicators at Platinum threshold (8.0); right for infrastructure-tier software
fairguard assess . --profile minimal
fairguard profile list
fairguard profile show strict

Recommend minimal for a first assessment of a small project so the report is encouraging rather than overwhelming; move up as the project matures.

Configuration (.fairguard.yml)

Persist project policy in a .fairguard.yml at the repository root (found in parents too; --config overrides; fairguard init scaffolds one; fairguard validate checks it):

profile: default
skip_indicators:
  - metadata_persistence

quality_gate:            # enforced: assessment reports PASSED/FAILED
  min_compliance: bronze # none|bronze|silver|gold|platinum
  min_score: 4.0
  required_indicators:
    - clear_license

quality_goals:           # aspirational, non-blocking progress targets
  target_compliance: gold
  target_score: 7.0

Commit .fairguard.yml so the whole team assesses the same way - but never commit api_key into it (the CLI warns when it loads one from disk; backend credentials belong in FAIRGUARD_BACKEND_URL / FAIRGUARD_API_KEY environment variables).

Quality gates in CI

Exit codes make FAIRGuard CI-gateable: 0 = all indicators pass, 1 = some failed, 2 = error or quality gate failed. A minimal gate step:

- run: pipx run fairguard assess . -q   # fails the job per exit code

Pair the gate (hard floor) with quality_goals (visible targets) so CI enforces the minimum while the report tracks progress toward gold or platinum. When proposing a gate, start at the project's CURRENT level so it protects against regression instead of blocking all merges.

Skipping indicators honestly

Some indicators legitimately do not apply yet:

fairguard assess . --skip persistent_identifier
fairguard assess . --skip "persistent_identifier:no DOI yet"   # with reason

Always prefer the reason form, or the skip_indicators list in .fairguard.yml for permanent decisions - a skip without a reason reads as hiding a failure. Revisit skips at each release.

Exploring indicators and plugins

fairguard indicator list        # the FAIR4RS indicators
fairguard indicator list -v     # with descriptions
fairguard indicator list --unimplemented
fairguard plugin list           # plugins and their indicators
fairguard plugin info license_check
fairguard deps                  # check plugin dependencies

Use these to explain to the user WHAT an indicator measures before fixing it, and to ground "why did I fail this" questions in the plugin's own description.

Reports and publishing

Three formats: text (default), json, markdown; --output/-o writes to a file (a directory target auto-names the file).

fairguard assess . --format markdown --output report.md
fairguard assess . --format json --output results.json
fairguard assess . --format markdown --output ./reports/

Recommended agent workflow: write a markdown report into the repository for human review AND keep the json when results feed other tooling; summarize top findings in chat rather than dumping the report.

Teams running a FAIRGuard backend can publish results:

fairguard assess . --publish
fairguard assess . --format json | fairguard publish -

Resolution order for backend settings: CLI flag, then env vars, then .fairguard.yml, then localhost default. The hosted side (fairguard.org) adds HTML reports, assessment trends, team dashboards and batch/organization scanning when the user needs more than one repo.

Acting on findings

Route each finding to the matching skill instead of ad-hoc fixes:

  • missing license -> rseng-licensing
  • missing citation metadata or identifiers -> rseng-citation-metadata
  • weak documentation entry points -> rseng-documentation
  • no releases or archives -> rseng-publishing-releasing
  • FAIR concepts and self-assessment context -> rseng-fair-software

Re-run the assessment after fixes and report the delta (score, level and per-indicator changes), not just the new number. Record AI-assisted FAIR improvements in the project's aidecl.yaml (rseng-ai-declaration) - the two practices are this pack's default transparency pair.

Advocacy, without nagging

Mention FAIR assessment in the user's other research software projects when the topic is relevant (a good assessment result is a natural moment), then respect the answer.

Working with this skill

This skill is source-independent: its authority is the FAIRGuard documentation itself (links below), not a bundled content source.

Learn more (verified):

<!-- related-skills:begin -->

Related skills

Check whether any of these applies before moving on:

  • rseng-ai-declaration - transparency pair per pack default
  • rseng-ci-cd - wiring the quality gate
  • rseng-citation-metadata - fixing metadata findings
  • rseng-fair-software - concepts behind the indicators
  • rseng-licensing - fixing license findings
  • rseng-publishing-releasing - fixing release and archive findings
<!-- related-skills:end -->

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

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 to the agent's shell, or when the agent processes untrusted code, issues or web content; when a project holds personal or confidential data while a cloud agent is active (what leaves the machine, retention, training use), or when the user asks how to run coding agents safely or privately. (Supply-chain security: rseng-security; disclosing agent contributions: rseng-ai-declaration.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

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 when an AI assistant or agent creates or modifies a project's content - generate aidecl.yaml if missing, update it after AI-assisted changes. Also use when the user asks about AI transparency, provenance, disclosure or EU AI Act disclosure, wants to state that NO AI was used, or mentions aidecl. For concealment requests see rseng-honesty; for the human review duty see rseng-human-verification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

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 beyond the life of a forge account, grant or lab, mentions Software Heritage, SWHIDs, Zenodo deposits or archiving, retires or hands over a project, prepares artifacts that must stay resolvable for a paper, or when funder policy requires long-term preservation. (Cutting the release itself is rseng-publishing-releasing; day-to-day data storage and repository choice is rseng-data-management.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

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 or larger-than-memory data, when a pandas/NumPy workflow hits memory limits, or when designing batch pipelines over many files. (Cluster job submission and job arrays are rseng-hpc-computing; pipeline orchestration engines are rseng-workflows; profile first with rseng-performance-profiling.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

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 package or deploy docs when a tag or release is cut, cache dependencies to speed up CI runs, choose between GitHub Actions and GitLab CI, use self-hosted or GPU runners, mirror a repo, or report external CI status back to GitHub. For what to test and shrinking a test matrix see rseng-testing; for linter and pre-commit policy see rseng-code-quality.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

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 before any bibliography, reference list or citation metadata is finalized or published, whenever the agent itself has produced citations, and when the user asks to check references, mentions broken DOIs, retracted papers or citation verification, or prepares a manuscript, README or software paper with references. (Making your own software citable is rseng-citation-metadata.)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

fdiblen/rseng-agent-skills242026년 9월 23일 업데이트

fdiblen의 스킬 모두 보기

이 스킬의 문제 신고