11 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 11 plugins that match these filters.
china-financial-servicescyijun/china-financial-services1
testland-qatestland/qa1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplace32 engineering skills: architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, Playwright (9 sub-skills), self-improving agent, Stripe integration, TDD guide, tech stack evaluator, Google Workspace CLI, a11y audit (WCAG 2.2), Azure cloud architect, GCP cloud architect, security pen testing, Snowflake development, adversarial-reviewer, ai-security, cloud-security, incident-response, red-team, threat-detection. v2.8.1 audits senior-fullstack / senior-frontend / senior-backend against karpathy-coder + Matt Pocock — each ships a 7-question forcing-question library, 4 customization profiles (JSON), deterministic decision engine, composition map into POWERFUL specialists, plus cs-fullstack-engineer / cs-frontend-engineer / cs-backend-engineer orchestrator agents (context: fork) + /cs-fullstack-review, /cs-frontend-review, /cs-backend-review, /cs-engineer-grill slash commands.
Computational-science methodology for Claude Code: research framing, pre-registration, reproducible analysis, anomaly investigation, and red-team review
Evidence-first A-share research methods, point-in-time controls, industry-ETF penetration, valuation, factor validation, and red-team review.
financeAdversarial verification for AI-generated legal content with systematic fact-checking, source validation, and quality control. Use when verifying legal documents, fact-checking regulatory content, performing red team review, or quality assurance before distribution to clients or stakeholders.
Adversarially stress-tests a legal text — a contract, statute, regulation, or judicial opinion — for interpretive ambiguity: it finds the seams where the people governed by it will later disagree about what it means and turns each into a concrete dispute scenario with both sides' arguments, the likely outcome, and a fix. Use it whenever someone wants to pressure-test, red-team, audit, or find weak spots, loopholes, gaps, ambiguities, or drafting problems in a legal document; whenever a drafter wants to tighten a contract, statute, regulation, or opinion before it issues; whenever a litigator wants to mine an opinion or contract for arguments; or whenever someone hands over a legal text and asks where it will be fought over or for issue-spotting. Trigger it for contract review, statutory-ambiguity analysis, judicial-opinion scope analysis, and drafting QA — even if the user never says "stress-test" or "ambiguity."
Produces an adversarial attack on a legal argument that survives reply. Runs the opposing-counsel discipline twice: an unrestrained first pass, then the same instrument turned on that pass to cut every point that collapses under challenge - attacks on facts your own side has conceded, objections belonging to the wrong forum, speculation about documents and motives, gotchas with innocent explanations, overclaims, self-refuting assertions, and padding. The deliverable is one standalone attack containing only what can be defended, capped at five heads, not a discussion of the discarded draft. Use to attack, stress-test, red-team or rebut a submission, brief, motion, witness statement, letter or piece of structured legal reasoning when the output must be something you could stand up and say. Also use when an earlier adversarial review came back overlong, scattershot or padded with points you do not trust. Derived from Opposing Counsel Review by Larissa Meredith-Flister (Apache-2.0). Formal
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-mlWhite-box bug hunting, AI-agent and LLM red-teaming, cloud identity and CI/CD trust, client-app trust surfaces across browser and editor extensions and Electron, wire-protocol and token trust across gRPC, WebSocket, and JWT, enterprise identity lifecycle across SCIM and JIT provisioning, directory sync, MFA and key lifecycle, and Kerberos delegation, infrastructure-as-code and container hardening, mobile app security, smart-contract and DeFi review across EVM and Move, skill and MCP supply-chain vetting, API and object-authorization depth, host privilege escalation, network-service and transport trust, firmware and embedded-software trust, web trust boundaries, authentication and session depth, rate-limiting and multi-tenant isolation, injection and resource-exhaustion depth, file-upload and content handling, defensive detection and logging, supply-chain and dependency risk, business-logic and access-control depth, the classic web-attack classes across XSS, template and command injecti
securitySecurity review guidance for agent configs, MCP and filesystem exposure, red-team scenarios, and audit prompts.
developer toolsPrompt, skill, red-team, and agent behavior evaluation workflows.
developer toolsDrop-in agentic loop skills (autoresearch, literature & writing, data, optimization, red-team, power analysis). Skills install namespaced as agent-loops:<name>.
