claude

data-science-research

Exploratory data science & reproducible research team — agents (exploratory-data-scientist, feature-and-modeling-engineer, research-reproducibility-engineer) for the analysis layer between raw data and a defensible result: data profiling/cleaning, EDA, hypothesis generation, communicating findings with uncertainty; feature engineering, classical modeling (regression, trees, boosting), model selection, evaluation (cross-validation, metrics) and leakage avoidance; and the reproducibility spine — notebook hygiene, pinned environments, experiment tracking, data/version control, seeds, and reproducible pipelines. best-practices, decision-tree knowledge bank, skills, commands, templates, a hook, scenarios bank, and stdlib-only ds_calc.py (classification/regression metrics, split-check). Seams: is-the-result-statistically-real -> applied-statistics; productionize/serve/monitor -> ml-engineering; data pipelines/warehouse -> data-platform. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.3.1MITupdated today
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Plugin data-science-research from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.

What it installs

Skills (4)

  • eda-workflow

    Run a disciplined exploratory pass before anyone models: profile the data (shape, types, missingness, cardinality, distributions), make and document cleaning decisions, visualize distributions and relationships, spot leakage candidates and target-definition problems, generate hypotheses, and communi

  • feature-engineering-and-modeling

    Engineer features and fit, select, and honestly evaluate classical models on tabular data: build leakage-aware features, set a baseline, choose between regression / trees / gradient boosting, build a leakage-safe cross-validation harness with every transform fit inside the fold, and pick the metric

  • find-plot-recipe

    Look up a library-agnostic plot recipe and runnable implementations across 15 charting libraries (matplotlib, seaborn, plotly, bokeh, altair, ggplot2, Makie, D3, ECharts, ...) from anyplot.ai's public read-only API during the EDA visualization step. Opt-in third-party reference — public HTTP API by

  • reproducible-research

    Make an analysis re-runnable by anyone: enforce notebook hygiene (top-to-bottom-clean, no out-of-order state), pin environments and dependencies in a lockfile, set and thread random seeds, version data and artifacts, track experiments (params + metrics + code/data version per run), and convert a run

Slash commands (3)

  • /data-science-research:build-baseline-model

    Engineer features and fit a leakage-safe classical baseline: a dumb baseline to clear, regression / trees / gradient boosting, a cross-validation harness with transforms inside the fold, and the metric chosen from the decision.

  • /data-science-research:make-reproducible

    Make an analysis re-runnable by anyone: pin the environment, set and thread seeds, version the data, track the experiments, clean the notebook, and script a deterministic pipeline.

  • /data-science-research:profile-and-explore

    Profile and explore a dataset before anyone models it: shape/types/missingness/cardinality/distributions, documented cleaning, leakage candidates, and hypotheses with uncertainty.

Subagents (3)

  • exploratory-data-scientist

    Use for the FIRST look at a dataset — profiling, cleaning, and exploring before anyone models. Profiles shape, types, missingness, cardinality, distributions; documents cleaning decisions; visualizes relationships; spots leakage candidates and target-definition problems; generates hypotheses.

  • feature-and-modeling-engineer

    Use this agent to engineer features and fit, select, and HONESTLY evaluate classical models on tabular data. It builds features (encodings, interactions, transforms), reaches for a baseline first (mean / majority / linear), then regression / trees / gradient boosting.

  • research-reproducibility-engineer

    Use this agent to make an analysis REPRODUCIBLE — so someone else (or future-you) can re-run the numbers and get the same answer.

Hooks (1)

  • PreToolUse

    /usr/bin/env -u BASH_ENV -u ENV -u SHELLOPTS -u BASHOPTS -u LD_PRELOAD -u LD_LIBRARY_PATH -u LD_AUDIT -u DYLD_INSERT_LIBRARIES -u DYLD_LIBRARY_PATH -u PYTHONPATH -u PYTHONSTARTUP PATH=/usr/local/bin:/

View source on GitHub mcorbett51090.github.io

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finance

claude

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23 skills · 7 commands · 7 agents · hooks

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Web design & build specialist team — web architect (IA, stack, hosting), UX designer (wireframes, flows, conversion), visual designer (brand, typography, tokens), frontend implementer (HTML/CSS/JS, React, Astro, Next), content strategist (copy, microcopy, SEO content), accessibility auditor (WCAG 2.2 AA/AAA), and performance engineer (Core Web Vitals, image/font optimization). skills (the gold-standard-website-pipeline gated build pipeline, static-site build, design-system audit, card/tile UI, accessibility review, Core Web Vitals tuning, technical SEO audit, information architecture, conversion design, content audit, design-tokens scaffolding, third-party hygiene, Fluent UI v9 + React implementation), templates, an advisory hook, and a knowledge bank seeded with a curated 2026 reference set of "cutting edge yet simple" marketing-site donors — the design agents carry compact inline priors plus a pointer to the full brief. Inherits ravenclaude-core protocols.

19 skills · 7 commands · 7 agents · hooks · LSP

9

EdTech-flavored Partner Success Manager team — specialist agents anchored on the PSM lane, with skills, templates, an advisory hook, a knowledge bank with Mermaid decision trees, a engagement bank, and a stdlib psm_calc.py calculator (seat-utilization / renewal-NRR-forecast / time-to-value). Covers QBRs, success plans, health scores, a self-contained Power-BI-style partner-health report, onboarding, touchpoint logs, adoption sequencing, 90-day implementation arcs, train-the-trainer curricula, advocacy/reference pipelines, renewal decision memos, and the integration contract for a centralized CS dashboard (Planhat + Salesforce + Snowflake + Zapier + Granola). Team growth ships as skills + knowledge + templates, not parallel agents. Primary-source citations with retrieval dates. Vertical-explicit but segment-agnostic — fits K-12, higher-ed, corporate L&D, or mixed EdTech books. Inherits ravenclaude-core protocols including the alternate-methods Capability Grounding Protocol.

17 skills · 5 commands · 6 agents · hooks

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Financial-regulatory & compliance specialist team — function agents (AML/KYC, regulatory-reporting, risk-and-controls, policy & procedure, examination-prep, Bermuda-insurance) PLUS jurisdiction/regulator specialists: BMA financial-institutions (banking, trust, corporate-services, fund-admin, investment-business, money-service, digital-assets — the primary build-out, with deep AML/ATF + supervision/fees + licensing-classification trees), CIMA (Cayman), Bahamas, Channel Islands (Jersey JFSC + Guernsey GFSC), UK PRA, and US federal+state. primary-source-cited regulator knowledge files, best-practice rules, skills, templates, a defensive PreToolUse hook (PII scrub — SSN/IBAN/credit-card/Bermuda TIN/wire instructions). Positioning reflects field experience inside a Tier-1 financial regulator (BMA). Inherits ravenclaude-core protocols.

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