14 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 14 plugins that match these filters.
auto-empirical-research-skillsbrycewang-stanford/Auto-Empirical-Research-Skills1
persona-osSidsaladi9/persona-os1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplace12 Claude Cowork-native skills that act on your real data through connectors and the sandbox — Gmail/Calendar inbox triage, meeting prep and follow-up sweeps; Drive/Docs/Sheets audits, restructures and decks; Notion/Slack decision logs, DB hygiene and async standups; GitHub/Linear issue triage, PR descriptions and changelogs. Each drives real tools and returns an artifact — not a framework you run by hand.
Twelve-skill bundle covering the Language (Linguistic Society of America) manuscript lifecycle — Cambridge University Press ScholarOne submission, double-anonymous review, the 18,000-word General Research Article cap, the Perspectives target-article format, Leipzig-convention interlinear glossing, and the Language Style Sheet.
academicEnd-to-end feature-flag discipline: classify, ship, ramp, retire. Detects stale flags as debt, generates phased rollout plans (ring/linear/log/cohort), and audits every flag for a documented kill switch. 3 stdlib Python tools, 4 references on flag taxonomy + provider trade-offs (LaunchDarkly/GrowthBook/Statsig/Unleash/Flipt/DIY) + rollout strategies + lifecycle. /flag-cleanup slash command. Cross-tool compatible.
Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml): cleaning -> variable construction -> Table 1 -> diagnostics -> estimation (OLS/IV/DID/RDD/PSM/SCM/DML/Causal Forest) -> robustness -> mechanism/heterogeneity/mediation -> publication-ready tables & figures. Includes epidemiology and ML-causal modes.
The product-manager OS that gets better the more you use it: a CLAUDE.md operating brain, a memory that learns your product and team (3-min /setup), and a self-improving loop that watches the work you repeat and builds you custom skills for it. Plus 53 book-grounded skills across discovery → strategy → execution → launch — every one scored in CI and gated against regression — a workspace your artifacts land in, two isolated workers (critic + researcher), live connectors that write your work back to Linear/Notion/Slack, 5 chained workflows, ready-to-wire automations, and bundled getprompts + getskills libraries.
productivityUse when analyzing, selecting, validating, or communicating models for insurance, actuarial, financial-risk, or other consequential uncertain outcomes. Covers regression, generalized linear models, frequency-severity, panel and longitudinal data, survival, time series, credibility, reserving, tail risk, calibration, and model governance. Do not use for generic software forecasting, ordinary SaaS financial models, or credentialed actuarial, investment, legal, or regulatory advice without the relevant specialist review.
Manage Linear teams, projects, cycles, issues, comments, workflow state, and documents from a terminal through Linear's public GraphQL API. Use when a user asks to list, search, inspect, create, update, move, or comment on Linear work, or to find Linear documents. Do not use to embed a live agent inside Linear or to build an MCP integration.
Use when asked to fix, build, refactor, review, verify, or release software and the work is non-trivial — including delivering a change request through planning, gates, implementation, review, verified PR, and authorized release; continuing work started elsewhere with no packet via evidence-based position assessment; or operating tracked work items in Linear, Jira, or Notion via their tooling skills. neckbeard routes the change through framing, discovery, design, implementation, review, verification, and learning — choosing the smallest *safe* intervention, proving it at the real delivery boundary, and leaving an inspectable evidence ledger. For issue-to-PR work, conditionally loads a 9-phase journey with gates and lifecycle integration. Composes specialist catalog skills rather than replacing them. Not a persona or LOC-minimizer. Do not use this skill for unrelated requests; route to the nearest named specialist.
Use GitHub or Linear issues as cross-session resolution memory. Create/update/resume self-contained, cold-readable issues, duplicates checked first.
memoryDefect management: severity vs priority classification, bug tracker workflows on Jira, Linear, GitHub Issues and Azure DevOps, and fix confirmation. Agents (ci-defect-filer, defect-fix-verifier) turn CI failures into deduplicated tracker bugs and re-run a defect's reproduction once a fix is claimed. Distinct from qa-bug-repro, which covers reproduction, report authoring, and the weekly defect review.
operations-resilienceUses repository evidence to decompose requirements into the fewest implementation-ready value units, then registers approved tasks in Linear
External knowledge and workflow integrations — Notion, plus future productivity surfaces (Linear, Slack, calendars)
Manage Linear issues, projects, and workflows via CLI. This skill should be used when the user wants to create, list, up
Read Linear work into the shared content-based repository intake route, with a separate approved delta sync for existing briefs.
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