16 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 16 plugins that match these filters.
claude-code-skillsalirezarezvani/claude-skills2
claude-code-workflowswshobson/agents1
research-agent-skillsKalarisLabs/research-agent-skills1
trailofbitstrailofbits/skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceOrchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
Twelve-skill bundle covering the Communication Research (CR) manuscript lifecycle — hypothesis-testing experiments, surveys, and content analysis with rigorous measurement, mediation/moderation, and APA reporting.
academicTwelve-skill bundle covering the 《南开管理评论》 manuscript lifecycle: workflow router, fit positioning, theory-gap articulation, mechanism-backed hypothesis development, measurement rigor (reliability / validity / common-method-bias), survey-SEM (mediation / moderation / moderated mediation / HLM), experiments, multi-case / grounded theory, China contextualization, discussion that advances a management theory, submission preflight, and rebuttal.
academicTwelve-skill bundle covering the Sociological Theory (ST) manuscript lifecycle. Theoretical-problem framing, tradition positioning, concept and proposition construction, argument validity in place of statistical robustness, boundary conditions, conceptual exhibits (typologies and mechanism diagrams, never data plots), contribution framing, ASA style, and the double-anonymous Manuscript Central review. NO empirical hypothesis-testing — that is ASR / AJS's lane.
academicHypothesis generation, experimental design, statistics planning, validation.
researchHypothesis testing, A/B experiment analysis, sample size calculation, and confidence intervals. 3 stdlib-only Python tools: Z-test/t-test/chi-square with effect sizes, sample size calculator with power tradeoffs, and Wilson score confidence intervals.
End-to-end chaos engineering discipline: design experiments with hypothesis + steady-state metric + blast radius + abort criteria, calculate risk score against error budget, and generate blameless postmortems. 3 stdlib Python tools (experiment_designer, blast_radius_calculator, experiment_postmortem), 4 references on chaos principles + experiment design + 7-attack taxonomy + tooling landscape (Chaos Toolkit/Mesh/Litmus/Gremlin/AWS FIS/DIY), templates, and /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (chaos targets).
Write, review, and triage property-based tests — Hypothesis, fast-check, proptest, and Echidna or Medusa for Solidity invariants
Intrapreneurship & innovation agent: 10 skills, 4 agents. Business model canvas, customer discovery, financial modeling, go-to-market strategy, hypothesis testing, ideation, market analysis, pitch deck, design sprints, and validation.
innovationProperty-based testing with Hypothesis (Python), fast-check (JS/TS), proptest (Rust) and jqwik (JVM): generators, shrinking, and CI integration. Agents (property-based-test-author, vacuous-property-critic) write one property per invariant and flag assume-heavy or always-true properties.
functional-testingPlan static inspection of unknown binary files with a deterministic CLI whose Ghidra operations are currently unimplemented and fail closed. Use when you need to inspect a PE, ELF, or Mach-O file — triage suspicious binaries, map imported APIs, decompile functions, trace call paths, or produce structured evidence reports. Do not use for runtime analysis (debugging, dynamic tracing, sandbox execution), for modifying or patching binaries, or for binaries you already know everything about. The skill owns planning, hypothesis formation, and evidence synthesis; the CLI owns all deterministic operations.
Use for PhD-level expertise in data science, statistics, and machine learning: rigorous statistical analysis, experimental design, causal inference, advanced modeling, research methodology, or data science project leadership. Load when the user asks about statistical methods, experimental design, model selection, A/B testing, hypothesis testing, power analysis, regression, causality, Bayesian analysis, or research methodology. For insurance, actuarial, claims, reserving, solvency, credibility, tail-risk, or financial-risk statistical modeling, use `actuarial-risk-modeling`; for deterministic operating and SaaS financial models, use `financial-modeling`. Do not use this skill for unrelated requests; route to the nearest named specialist.
Guide embedded technical engagements from ambiguous stakeholder need through discovery, framing, hypothesis, build, evaluation, deployment, adoption, measurement, and generalization while preserving evidence, decision rights, and field learning. Use when one accountable technical lead must carry continuity across customer or stakeholder discovery, implementation, production fit, adoption, and measurable outcomes. Do not use for a bounded repository change, product investment governance, ongoing reliability or platform ownership, an isolated specialist task, or advisory work that ends before implementation and adoption.
The FDE + DeepSCR Agent Skill: Stage 0 Reconnaissance -> Scoping -> Prototyping -> Production -> Feedback, 6-Q decomposition, FDE Assurance Score (0-100, DeepSCR: Hypothesis -> Contradiction -> Verification -> Certification), 7 local MCP tools, and deliverable templates renderable to docx/pptx/pdf via Anthropic document skills.
Grow X followers via numbered, A/B-testable growth experiments — each with a hypothesis, target metric, rollback state, and verdict. North-star = new-follower ÷ profile-visit conversion.
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
