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.
redisredis/agent-skills1
cortex-pluginscdeust/Cortex1
swift-ios-skillsdpearson2699/swift-ios-skills1
xcode-skillsartemnovichkov/xcode-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceTwo-loop skill pipeline for Claude Code. Inner loop (plan-interview, intent-framed-agent, session-context, context-surfing, verify-gate, self-healing, simplify-and-harden) catches failures in-session — including active runtime recovery via self-healing when a command, test, build, helper, env, or external service fails mid-task. Outer loop (learning-aggregator, self-improvement, eval-creator, pre-flight-check) captures recurring patterns, promotes them to CLAUDE.md as permanent rules with human approval, and turns failures into regression tests. The harness gets stronger with every cycle.
Redis development best practices — data structures, Redis Search (lexical + vector + hybrid retrieval), caching, and performance optimization, plus Redis Iris (Agent Memory, Agent Playbooks, Context Gateway, Intent Router, Lens), and live lookup of the official redis.io documentation.
Cortex — persistent memory and cognitive profiling for Claude Code (renamed from the 'cortex' plugin in v4.15.0 to match the PyPI/MCP-registry identity hypermnesia-mcp) — thermodynamic memory with heat/decay, intent-aware retrieval, biological plasticity, codebase intelligence, and cognitive profiling. These mechanisms are implemented and inspectable; committed ablations do not establish a retrieval gain from all 36 mechanisms. 57 MCP tools (60 with the optional ai-architect-mcp-codebase + ai-architect-mcp-spec integrations) with enriched schemas (visualization extracted to the standalone Hypermnesia MCP Viz server). PostgreSQL + pgvector in CLI mode; automatic SQLite fallback in Cowork/sandboxed mode. v3.17.0 — autonomous per-project wiki: SessionStart auto-spawns a 6-hour consolidate cycle; a headless `claude -p` worker drains the curation-gap queue, calls codebase-intelligence MCP tools to ground each section in the real call graph, and authors missing anchor pages (architecture / s
App experience frameworks: widgets, Live Activities, App Intents, StoreKit, push notifications, maps, media, video, PDF, CarPlay, ads
developmentApple's Xcode 27 bundled agent skills: SwiftUI, App Intents, Siri integration and evaluations, app resizability, macOS touch gestures, Swift Testing, C bounds safety, security build settings, accessibility audits, String Catalog translation, device interaction, xctrace profiling.
Ten battle-tested SDLC skills: sdlc-intent (PRD digest + seven-layer audit + A/B/C risk tiering), sdlc-design (technical design: alternatives, tiered interface contracts, shared assets, C/H decisions, slicing constraints), sdlc-test (cases/dev/static/exec/spec/report with human checkpoints), sdlc-gate (risk-tiered adversarial review gate: 4/2 fresh-context subagents or self-check, plus guard_dev), sdlc-doubt (adversarial re-check of non-trivial decisions), sdlc-ui-visual (design-to-code visual restoration via screenshots: DOM geometry + pixel heatmap + tiered fix rounds), sdlc-release-check (pre-release audit: config keys, job/MQ registrations, DDL/DML grading, runbook), sdlc-guardrails (write-time red-line engine: PostToolUse hook + pre-commit + baseline scan), sdlc-sync (artifact sync bridge between local markdown and the team-facing platform), sdlc-handoff (cross-agent session handoff: self-contained card, drift check, direct takeover).
development
Consistency audit across docs, code, tests and CI with per-fact authority and advisory-only LLM findings. Audit probes are instructed to leave project inputs unchanged; each local run attempts disclosure-approved report and index writes in a separate Intent OS checkout, reporting held filing explicitly. Invoked by /release Phase 1.6.
Capacitor feature skills for deep links, keyboard handling, offline-first data, push notifications, splash screens, and App Intents (Siri, Shortcuts, Spotlight).
Social media intelligence powered by Xpoz. Search, analyze, and export data from Twitter/X, Instagram, Reddit, and TikTok. Includes 13 skills: best practices reference for query syntax, pagination, field selection and all platform tools; getting started onboarding and setup; social tracking for continuous keyword/user/hashtag monitoring; sentiment analysis across platforms; Twitter data export to CSV (up to 500K rows); influencer discovery and ranking; Reddit discussion research; competitive intelligence and brand comparison; security OSINT and threat monitoring; AI answer tracing (the searches, retrieved pages, and cited sources behind Claude, ChatGPT, and Gemini answers); GEO visibility checking (where a brand appears in AI answers and who wins instead); GEO Reddit influence mapping (the subreddits and threads AI engines cite); buying-intent lead scanning across the four platforms.
Visual regression testing with Percy, Chromatic, Playwright screenshots and Storybook, plus chart rendering tests for Chart.js, D3 and Vega. Covers responsive breakpoint matrices, baseline conventions, and a binding baseline gate. Agents (visual-diff-classifier, visual-baseline-curator) sort diffs into intentional, incidental or regression and propose an initial baseline set.
quality-engineeringEight lean but firm skills, including spec-sync for durable intent and context-hygiene against stale documentation. They trigger only when their conditions match and stay strict once active. Includes a SessionStart discipline primer, passive local usage counting, and first-entry guide-doc seeding for CLAUDE.md / AGENTS.md.
Intent-driven planning, implementation and independent review with bounded repairs.
Legal Abduction is an AI-assisted workflow for organizing case documents into traceable observations, competing explanations, an evidence matrix, gaps, and tests that could distinguish between hypotheses. Use it when you need a structured, source-linked analysis of a bounded legal or investigative question—not a verdict or a prediction about a person. Install the skill in a compatible AI agent, provide an authorized document set and a clearly scoped question, then follow its guided workflow: the agent extracts and proposes; you review and approve evidence before it informs comparisons and reports. The local tools validate references and preserve an integrity trail. Human judgment remains essential. Legal Abduction does not provide legal advice or determine guilt, credibility, or intent.
Guide for reading, interpreting, and applying statutes, regulations, and rules in legal and compliance contexts. Use when the user asks about (1) how to read and interpret statutes, regulations, or rules, (2) statutory interpretation methods and canons of construction, (3) understanding legislative intent, (4) applying statutes to specific legal situations, (5) extracting requirements from legal text, (6) distinguishing between different types of legal requirements, or (7) cross-jurisdictional compliance analysis.
Ai2 Asta MCP — Semantic Scholar academic graph over MCP (no Python). Intent-to-tool routing, safe fields defaults to avoid context blowups, citation traversal, author lookup, snippet evidence retrieval, and DOI/arXiv/PMID via externalIds.
Intent to implementation: ready, then go.
Turn a video, recording, voice memo, or media URL into an evidence packet — authoritative whisper transcript, timestamped frames, manifest — plus a routing recommendation. Six intents behind a mandatory purpose gate; the pipeline script is resumable and encodes the hard-won gotchas.
Host-gated intent review and mutation testing.
developmentStripe payment processing — Checkout Sessions, Payment Intents, subscriptions, webhooks, Connect, customer management, error handling
Use this skill to run BMad (Breakthrough Method of Agile AI-Driven Development) as a harness-agnostic control-plane protocol that turns human intent into bounded, inspectable, resumable agent work. Compress intent into a five-field contract (Why, Capabilities, Constraints, Non-goals, Success signal); classify work as direct/bounded/initiative and route to the smallest safe path; carry decisions in durable artifacts; review as triage; route failure to the layer where ambiguity entered; and gate autonomy on observable acceptance with machine-readable status (draft/ready-for-dev/in-progress/in-review/done/blocked). Use when a change request, feature, delegated build, or multi-agent epic needs intent capture, bounded implementation, review, and resumability in any agent harness. Do not use for validating whether a problem is real (product-discovery), shaping bets before planning (product-shaping), formal spec/gate pipelines (spec-driven-development), or the issue-to-PR delivery flow (neckb
