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).
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Sign in to claimExecutes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
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The course author's AI toolkit for Sophie. Brainstorm, design, author, and review teaching content — with an AI collaborator you supervise. Includes a patient Socratic design partner, Mode-A course design (backward design), evidence-based pedagogy techniques (retrieval, spacing, interleaving, elaboration, concrete examples, dual coding, scaffolding), figure/equation/interactive-figure authoring, and an independent quality reviewer. For instructors building interactive STEM textbooks and course sites.
npx claudepluginhub drannarosen/research-workflow --plugin research-workflowComprehensive 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.
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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.
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