32 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 32 plugins that match these filters.
aiwgjmagly/aiwg1
arize-skillsArize-ai/arize-skills1
open-skillsallemaar/open-skills1
unboundcomputeUnboundCompute/security-agent-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceTake a side project from works-on-my-machine to a repo strangers install, trust and contribute to: a benefit-led README, a no-signup demo, an importer from competitors, good first issues, a licence audit and NOTICE file, fortnightly releases, one-command self-hosting, a read-only MCP server and the 20-second demo clip
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.
Applied security framework for cryptographic primitive selection, chain-of-trust design, authentication factor analysis, supply-chain trust, and physical-threat modeling. Complements OWASP-style application audits.
Compliance & trust skills for regulated and enterprise teams: SOC 2 Readiness, GDPR Compliance, HIPAA Safeguards, ISO 27001 ISMS, Vendor Security Review, and Data Retention Policy. Each ships a stdlib scoring/validation script.
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.
Reusable skills for AI coding agents. Every skill has a readable SKILL.md; 54 of 92 also carry a declarative protocol.yon with named steps, rules, and gates validated structurally by @younndai/yon-parser. Validation does not enforce runtime obedience. Read before you trust.
Post-hoc verification and trust audit of AI-generated factual and interpretive claims. Classifies claims by type and salience, routes them to domain-appropriate sources, scores trustworthiness on a tiered scale with an Interpolated verdict for plausible-but-unsupported detail, and assesses rhetorical fairness on interpretive claims. Designed with a focus on healthcare law in England and Wales, but with general applicability beyond.
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
Audit an AI agent skill before installing it. Use proactively whenever the user is about to add, install, enable, or evaluate an unfamiliar skill — including phrases like "audit this skill", "is this skill safe", "scan skill before install", "check skill for malicious code", "review this plugin", or any pre-install gate on an untrusted, third-party, or community-distributed skill bundle. Runs ten categories of static checks (code execution, network exfiltration, credential harvesting, persistence, prompt injection, supply-chain hooks, obfuscation, destructive filesystem ops, secrets, Trojan Source / homoglyphs) and emits a PASS / WARN / FAIL verdict.
White-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
securityFinds vetted agent skills for your task, mid-flow, from a federated catalog — with license/provenance trust metadata. Privacy-preserving: the search backend stays remote and only an abstract wish-list crosses the boundary.
Error discovery for AI products: read real traces, name failure modes and success modes by stage, and turn the ones that matter into code checks and AI judges you can trust. Includes Eval Studio and a zero-dependency command-line tool.
Expert NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF.
EU NIS2 Directive (Directive (EU) 2022/2555) compliance advisor for essential and important entities: entity classification, Art. 21 risk management measures, Art. 23 incident reporting timelines (24h/72h/1 month), Art. 20 governance obligations, supply chain security (Art. 21(2)(d); coordinated risk assessments Art. 22), gap assessments, policy drafting, ISO 27001 alignment, and penalty exposure analysis. Also covers Commission Implementing Regulation (EU) 2024/2690, the technical/methodological sub-requirements for Art. 21(2) and the significant-incident thresholds binding on DNS/cloud/data-centre/MSP/MSSP/trust-service and other digital entities. Use for NIS2 readiness, transposition questions, ENISA technical implementation guidance, significant-incident thresholds, supervisory differences between essential and important entities, and cross-border coordination.
Expert SOC 2 compliance assistant covering all five Trust Services Criteria (Security/CC, Availability/A, Confidentiality/C, Processing Integrity/PI, Privacy/P). Use this skill whenever a user mentions SOC 2, Trust Services Criteria, SOC 2 Type 1 or Type 2, audit readiness, compliance gaps, control documentation, evidence collection, vendor risk questionnaires, or anything related to AICPA service organization controls. Covers gap analysis, policy writing, control documentation, audit evidence preparation, and vendor risk reviews for organizations at any maturity level — from first-time startups to seasoned compliance teams.
Audits legal AI skills, prompts, workflows, MCP/tool instructions, and agent packages before they are trusted or installed. It detects prompt injection, hidden or hostile instructions, unsafe scripts, suspicious frontmatter, credential exposure, exfiltration paths, persistence mechanisms, cron/launchd hooks, unauthorized network calls, and supply-chain risk. Use it before adopting third-party or generated skills; before publishing a skill to a legal AI marketplace; when reviewing SKILL.md files, skill folders, scripts, references, MCP manifests, or automation instructions; or when a user asks whether an AI workflow is safe. Paste or upload the skill, prompt, MCP/tool instruction, etc you want to review. The auditor treats the material as untrusted data: it does not execute scripts, install packages, or obey commands embedded inside the reviewed content. It returns a verdict — approve, approve with constraints, rewrite, quarantine, or reject — plus the key risks and recommended remediat
Prove a result is trustworthy: stability, convergence, manufactured solutions, and pre/post-flight validation.
Authoritative reference for Pi (@earendil-works/pi-coding-agent): install, configure, run, and extend. Use when the user asks about Pi CLI/flags/commands, providers/models/auth, settings/compaction/sessions, security/trust, extensions, skills, prompt templates, themes, packages, MCP, codemode, custom providers, TUI components, the SDK, RPC mode, or JSON streaming. Also use for Pi's design philosophy and opinionated choices (why minimal, 4 tools, YOLO, no MCP/plan mode/to-dos/sub-agents), Pi's internal monorepo architecture (the Chord typed host runtime (@earendil-works/chord), the durable agent harness (@earendil-works/pi-durable) and its Pico5 specification, telemetry, delta tracking), and the repository's build, development, and contribution rules. Also use for Pi Durable itself: what the experimental harness is, its versions, the runnable examples, and which blog demos 1.2.0 removed.
Discipline mode (staged planning, receipts, verify-against-source, adversarial review) + plan/kickoff commands + a plan-reviewer agent + an always-on SessionStart discipline injector + a Stop-hook autonomous loop (Layer 1, a deterministic gate) that can arm itself from a committed, content-hash-trusted .claude/tale-mode.json during a kickoff phase + a fail-closed plan-mode auto-approve for provably read-only shell (no more permission-dialog walls during investigation).
productivityTask-Track & Handoff for AI agents on long, multi-session work. Within a session it pins one ⭐️ MAIN goal (frozen title, explicit verdict at close) in an emoji-tagged task list so the agent doesn't drift; across sessions or agents it writes a plain-markdown handoff the next agent re-derives before trusting. Not for short, one-pass tasks.
