38 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 38 plugins that match these filters.
testland-qatestland/qa2
magnus-agent-skillsmagnus919/agent-skills2
arize-skillsArize-ai/arize-skills1
nimble-plugin-marketplaceNimbleway/agent-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceAI/ML engineering skills for shipping AI features responsibly: Model Card, AI Eval Plan, RAG Design Doc, Agent Spec, Dataset Datasheet, AI Feature PRD, LLM Cost & Latency Budget, Prompt Optimizer, Eval Rubric Designer, RAG Architecture Review, LLM Guardrails Spec, Agent Design Review, and Model Selection Advisor.
Data & analytics skills: Metrics Framework, SQL Query Explainer, Dashboard Brief, Chart Data Extractor, Cohort Analysis, Data Pipeline Spec, A/B Test Readout, Metric Tree Builder, Data Quality Audit. Build metric trees, explain SQL, spec dashboards, run cohort retention with LTV modelling, design ETL/ELT pipelines, read out A/B tests with significance and guardrails, and audit datasets for quality before you trust them.
Add Arize AX observability to LLM applications — auto-instrumentation, trace export, dataset management, experiment workflows, prompt optimization, deep linking, and migrations from Langfuse, LangSmith, and Braintrust via the ax CLI.
Live web search for AI agents. Find current pages, read any URL, and run research agents that work across many sources and return findings with citations. Includes reusable Extraction Templates for consistently shaped records from known sites, and Web Search Agents for research, enrichment, and dataset building.
Audit datasets for completeness, consistency, accuracy, and validity. 3 stdlib-only Python tools: data profiler with DQS scoring, missing value analyzer with MCAR/MAR/MNAR classification, and multi-method outlier detector.
Study design and validity review, literature-grounded variable operationalization, sample-size planning, data cleaning, de-identification, codebook generation, and dataset versioning.
Setup plus 17 CLI-backed Bohrium skills for compute jobs, development nodes, datasets, files, scientific databases, images, projects, knowledge bases, papers and patents, PDFs, scholars, SciencePedia, scientific tools, web search, sandboxes, LKM, and Science Mentor. Requires the bohr CLI (setup skill can install it).
Test data engineering: Faker and factories, WireMock and MSW API mocks, seed datasets, golden files, and boundary, pairwise and negative test inputs. Includes FactoryBot, Mimesis and Bogus, a Mountebank reference, synthetic PII, and a malicious payload bank. Agents (golden-file-manager, test-data-setup-agent) maintain snapshot baselines and set up a feature's full test-data layer.
foundationsSplit datasets for training, validation, and testing
LLM and prompt evaluation with Promptfoo, DeepEval and Ragas, Giskard vulnerability scans, Langfuse tracing, and golden-dataset regression suites. Audits eval suites for methodology errors. Agents (prompt-eval-reviewer, llm-red-team-planner) review an eval suite and plan adversarial probe campaigns across the OWASP LLM Top 10.
ai-mlCore Coupler.io MCP capabilities — create dataflows, build Custom GAQL sources, generate dataset context, refine prompts, generate reports.
Drive the ServiceGraph API — metrics-enriched business datasets for founders (agencies, directories, newsletters, and more). Ships a branded `servicegraph` skill that works against any dataset, plus 14 specific skills for the US professional-services catalog (law, marketing, consulting, accounting, IT services, engineering, HR, PR, design, and more) and 3 product-directory skills for where to launch a software product and earn backlinks (general product/SaaS directories, MCP-server registries, and AI-tool/agent directories).
service-providersArbitrator research tool for investor-State dispute settlement (ISDS), built for arbitration counsel. It produces reliable, structured, and thorough insights into arbitrators' experience, and can identify, evaluate, and rank the suitability of arbitrators for specific disputes. The tool flags arbitrator decisions and publications that bear on the issues in the dispute, checks an arbitrator's caseload, and suggests additional issues for counsel's consideration prior to nomination. Try asking about specific arbitrators, or describe your dispute and ask for a shortlist of suitable arbitrator candidates. Outputs are grounded in the UNCTAD ISDS Navigator dataset, targeted live lookups (ICSID), and additional verification from primary sources. For best functionality, also download the isds-research skill.
Journal impact factor (JCR IF) lookup — find a journal's IF by name, compare IF across journals, and rank publication venue quality, backed by a bundled journals_if.csv dataset and a local Python CLI
Fetch and export public X (Twitter) data with a self-contained Python CLI built on the twikit library — no developer API keys, just the user's own login cookies. Covers user profiles, a user's tweets/replies/likes, single tweets, threads, search, followers/following, home/bookmark timelines, with JSON/JSONL/CSV/SQLite output and resumable pagination. Use whenever the user wants to scrape, download, collect, archive, monitor, or analyze tweets or X/Twitter accounts, export a timeline or follower list to a file or dataset, pull X data for research, or build a tweet corpus — even if they don't name the tool. Requires the user's own X login cookies (auth_token + ct0, or username/password login).
Use whenever the user mentions Zenodo, depositing or publishing research artifacts (datasets, software, papers, posters) to Zenodo, minting a DOI for a dataset/code release, uploading files to a Zenodo record, creating a new version of a Zenodo deposit, or searching Zenodo records. Covers the full Zenodo REST API workflow (create, upload via bucket API, metadata, publish, version, search) for production and sandbox.
Interact with Figshare via the v2 REST API - search public datasets/articles, batch-download files, and create, update, publish, or multi-part-upload to your own articles (trigger on "figshare", figshare DOIs 10.6084/m9.figshare.*, or figshare.com URLs)
LLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices
Design, run, review, or release framework- and vendor-neutral evaluations and observability for AI agents. Use when defining agent evals, datasets, graders, trajectory review, monitor independence, oversight quality, regression analysis, release gates, production traces, or privacy-aware telemetry. Covers task and trajectory contracts, statistical comparisons, and incident-to-case learning; route framework implementation to pydanticai or langgraph when needed. Do not use this skill for unrelated requests; route to the nearest named specialist.
Design and apply QA methodology for software teams: test strategy, regression testing, CI failure triage, test automation, quality gates and metrics, risk-based testing, exploratory testing, test design techniques, AI code quality gates (independent verification, artifact provenance, spec-first oracles, AI-review-comment triage, acceptance-criteria testability for Spec-Driven Development), mutation-guided test hardening and review evidence (surviving mutants, weak assertions, diff-aware mutation testing), agentic eval design (dataset test design, judge-as-system-under-test, flaky-eval discipline), QA career levels (Senior/Staff/Principal), and SDET engineering (test infrastructure, gTAA, CI/CD integration). Do not use for root-cause debugging of production incidents, security implementation or threat modeling, or evaluation framework governance and statistical analysis — route those to systematic-debugging, secure-software-engineering, and agent-evals-and-observability respectively.
