By drannarosen
Domain-agnostic research-coding workflow discipline for computational science (JAX/Python research family). 70 skills across ideate / literature / scope / build-correctly / equation-critical sources / verify / inference-rigor / review / performance-and-scale / record / communicate / reproduce-and-release, plus an equation-verifier agent and six slash commands. v1.4.x–1.5.0 adds a true front-of-funnel (research ideation + brainstorming + prior-art + literature discipline), Bayesian/statistical inference rigor (MCMC convergence, predictive checks, model selection), HPC performance & scale (profiling, scaling, JAX performance, cluster-run contract), software/data release & citation (CITATION.cff/DOI, release checklist, data management plans), and a figure craft & interpretation layer (expert astrophysics plotting in a house style, plot-craft review, and figure-interpretation guarding). The suite covers evidence-first execution, numerical validation, reproducibility, PDF equation extraction, equation-to-code traceability, reference-license firewalls, computational-physics code review, figure faithfulness, MyST documentation authoring, eight self-limiting enforcement hooks, and per-domain lenses for reference-parity work.
Go/no-go checkpoint before an expensive or irreversible run (high-impact-checkpoint).
Build or review an implementation-ready equation digest from source PDFs, with rendered-PDF verification states, traceability, license/firewall notes, and errata/conflict handling.
Inspect or enable the research-workflow hook decision log (RWF_HOOK_DEBUG).
Reference-parity audit against an external reference, loading the matching domain lens.
Capture a reproducibility contract for the current work — env lock, seeds, precision, inputs, commit.
Use when you have a result you are about to trust — to red-team it against confirmation bias and the stable-but-wrong failure mode (numerical artifact, latent bug, boundary effect, or a mundane alternative explanation that fits the same data). Produces the strongest attacks plus the cheapest discriminating test for each. Don't use for reviewing CODE (→ scientific-code-reviewer and the Review cluster), the neutral close-out format (→ verification-gate), reporting the result's uncertainty budget (→ uncertainty-reporting-gate), or explaining a convergence/refinement-floor behavior (→ numerical-method-validation).
Use when relying on code, math, or facts the AI assistant itself produced — apply EXTRA scrutiny precisely because the model's signature failure is confident fabrication: hallucinated library APIs, plausible-but-wrong algebra, invented constants/citations, and tests written to pass rather than to catch. The stance that points generic skepticism at the assistant's own output. Don't use as the concrete check itself — route to the specific gate: API existence (→ verify against docs), formulas (→ derivation-before-implementation), constants/citations (→ provenance-of-constants), result size (→ plausibility-envelope), test/claim integrity (→ evidence-first-execution).
Use when a research session produces meaningful results and you need durable manifests, payloads, plot scripts, and a completion note so later sessions can reason from artifacts instead of memory. Don't use for the in-the-moment command discipline (→ evidence-first-execution), the go/no-go close-out (→ verification-gate), or pinning the runtime environment itself (→ reproducible-environment-contract).
Use when a result or model rests on simplifying assumptions, approximations, fixed parameters, or regime-of-validity choices — keep an explicit running ledger of what each result depends on, so when an assumption later breaks you know exactly which conclusions die with it. Don't use for citing the source of a value (→ provenance-of-constants), recording a decision and its rationale (→ decision-log-and-commits), quantifying the numeric error a kept assumption induces (→ uncertainty-reporting-gate), or noting a regime/caveat a *paper* established as you read it (→ reading-notes-discipline) — this ledger is for your own project's assumptions.
Use when writing plotting code for astrophysics figures — author publication-grade plots in the house style: the jaxstroviz theme/helpers as source of truth, seaborn perceptually-uniform colormaps (mako/vlag), CVD-safe categorical palettes with color×marker redundancy, log/linear axis choice, LaTeX (not unicode) labels with CGS/solar units, uncertainty/overlays. Don't use to audit an existing figure's craft (→ plot-craft-reviewer), what a figure lets you conclude (→ figure-interpretation-guard), whether it honestly shows the data (→ plot-faithfulness-inspector), chart-type/design ideation (→ plot-design-inspector), or journal submission specs (→ publication-figure-validator).
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
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Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
Domain-agnostic research-coding workflow discipline for computational science (the JAX/Python research family — gravax, stellax, progenax, radax, …), packaged as a Claude Code plugin. The human is the scientist-in-the-loop, PI-level collaborator, and supervisor; the skills enforce evidence-first execution, structural correctness over compatibility, falsifiability, and reproducible artifacts. Domain specifics (e.g. MESA parity) live in thin lenses, so the stances stay sharp while the suite stays general.
| Phase | Skill |
|---|---|
| Collaborate | researcher-in-the-loop · high-impact-checkpoint |
| Ideate | research-ideation · research-brainstorming |
| Literature | prior-art-check · reading-notes-discipline · related-work-map |
| Scope | minimal-falsifiable-slice · discriminating-experiment-design · testing-strategist |
| Build correctly | ownership-and-structure · correct-cutover · numerical-precision · derivation-before-implementation · staleness-sweep · no-silent-except |
| Equation-critical sources | pdf-equation-extraction · equation-to-code-traceability · reference-license-firewall · equation-errata-ledger |
| Verify | evidence-first-execution · verification-gate · numerical-method-validation · gradient-validation · reference-parity-audit · adversarial-result-check · uncertainty-reporting-gate · plausibility-envelope · ai-self-distrust · seed-and-stochasticity · prior-sensitivity · systematic-error-hunting · figure-interpretation-guard · no-stub-when-done |
| Inference rigor | mcmc-convergence-gate · predictive-checks · model-selection-discipline |
| Review (audit written code/figures) | scientific-code-reviewer · numerical-methods-auditor · jax-code-validator · error-handling-reviewer · code-craft-reviewer · benchmark-generator · plot-faithfulness-inspector · plot-craft-reviewer |
| Performance & scale | profiling-discipline · scaling-validation · jax-performance · cluster-run-contract |
| Record | decision-log-and-commits · provenance-of-constants · experiment-tracking · data-provenance · data-io-validator · null-result-integrity · assumption-ledger · no-secrets-in-git |
| Communicate (docs & figures) | myst-expert · docs-writing-voice · myst-ci · interactive-figures · mystmd-plugin-dev · astro-plotting-craft · plot-design-inspector · publication-figure-validator |
| Reproduce & release | artifact-first-reproducibility · reproducible-environment-contract · software-citation · research-release-checklist · data-management-plan |
Each skill's description carries a "Don't use when… (→ sibling)" partition and a ## Related block, so the suite reads as one ordered protocol. reference-parity-audit loads a domain lens when one exists (lenses/mesa.md and lenses/nbody.md ship; lenses/rad-transfer.md is added on first need).
The Ideate and Literature clusters (v1.4.0) complete the front of the funnel the suite previously lacked: research-ideation (divergent — generate and triage directions) → research-brainstorming (convergent — sharpen one into a falsifiable hypothesis + discriminating observable) → prior-art-check (is it novel?) → discriminating-experiment-design → minimal-falsifiable-slice → Build. The Inference rigor cluster gates the inference itself for the NumPyro family — sampler convergence (R-hat/ESS/divergences), prior/posterior predictive fit, and honest out-of-sample model selection — distinct from the forward-numerics Verify cluster. Performance & scale covers measure-first profiling, strong/weak scaling, JAX compile-boundary performance, and the HPC job→artifact contract. Reproduce & release extends reproducibility to the citable public artifact (CITATION.cff/DOI, the figure→release trace, FAIR data management plans).
The Equation-critical sources cluster is for papers whose equations become code, tests, or benchmark fixtures. It keeps rendered-PDF verification, implementation traceability, reference-code licensing boundaries, and errata/conflict decisions separate on purpose. The equation-verifier agent is the adversarial row checker for promoting digest rows to verified.
The Review and Communicate clusters and several MyST references were consolidated in v1.2.0 from the former astro-code-review and myst plugins (now retired) — see the Status section. MyST authoring skills ship co-located references (myst-cheatsheet, math-and-gotchas, myst-projects-and-workflows, voice-fingerprint, page-anatomy) and the shippable mystmd-plugins/interactive.mjs directive bundle.
npx claudepluginhub drannarosen/research-workflow --plugin research-workflowThe course author's AI toolkit for Sophie. Brainstorm, design, author, review, and run governed course revisions with an AI collaborator you supervise. Includes two explicit instructor approval gates, source-grounded evidence, semantic review, and an immutable revision record.
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Harness-native ECC plugin for engineering teams - 67 agents, 279 skills, 94 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses
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Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.