42 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 42 plugins that match these filters.
awesome-journal-skillsbrycewang-stanford/Awesome-Journal-Skills1
qaskillsPramodDutta/qaskills1
trailofbitstrailofbits/skills1
pm-claude-skillsmohitagw15856/pm-claude-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceAutonomous experiment loop — optimize any file by a measurable metric. 5 slash commands (/autoresearch-agent:setup, /ar-run, /ar-loop, /ar-status, /ar-resume), 8 built-in evaluators, configurable loop intervals (10min to monthly).
Transform Claude Code into an autonomous AI development powerhouse with rich task context, specialized agents, and intelligent workflow automation
Autonomous multi-agent development framework with spec-driven sprints. Write specs, run /sprint, and let coordinated agents (backend, frontend, QA, UI testing) handle implementation through self-iterating convergent loops.
Twelve AAMAS-specific skills for autonomous-agents and multiagent-systems submission strategy, grounded in the official AAMAS 2026 Call for Papers, submission instructions, IFAAMAS proceedings, and the AAMAS 2027 OpenReview group.
academic10 curated QA skills: Claude Code QA, autonomous QA agent, E2E testing (Playwright, Cypress), API testing (REST + Playwright request), pytest patterns, Jest unit testing, and k6 performance testing.
Convert a rambling description of a desired outcome into one polished, autonomous /goal prompt ready to paste into a fresh session. Extracts deliverable/quantity/stakes/tools/destination, asks at most one question batch, verifies every named resource against the live environment, writes a 150-350 word prose prompt with the seven-part anatomy, and self-checks six binary criteria before delivering. The output is a single copy-paste prompt, never the build itself.
Improves code targets — skills, plugins, or a branch's changes — through an autonomous review-and-fix workflow with a pluggable reviewer (any installed skill or agent), a cross-round findings ledger, oscillation escalation, and a mechanical scope guard.
Agent safety seatbelts: the pre-flight security checklists you run before an agent touches email, the browser, files, or goes autonomous — least-privilege reviews, prompt-injection spotting, and the blast-radius drill
Autonomous research loops, agent harnesses and research artifacts.
researchCortex — 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
Seventeen skills that ship as one pack — a portable discipline for tracked, multi-session, agent-assisted development: the orchestrator seat a non-engineer works from (one session routing, auditing, and merging parallel lanes), a router entry point, a once-per-repo setup interview that records whether you lead from that seat or read code and pick skills, per-repo institutional memory written as side effects of work, relentless grilling, decision maps, throwaway prototypes, autonomous research lanes, ticket filing, human-step wizards, session handoffs, a build discipline of seam-scoped test-first tracer slices, a disciplined bug-diagnosis loop where no fix ships without a named cause, an adversarial review that breaks changes before they ship, a skeptic-verified state review of the codebase itself, an append-only decision trail that lets a human audit an unattended run after stepping away, and a PR-writing standard that leads with the problem, shows before-and-after proof, and says whet
Loops (a file-based knowledge base) + a codebase harness (dev-local, e2e, crabbox cloud boxes, verify-before-ship) so agents can run, test, and ship code autonomously.
engineeringRun Claude autonomously for 6-8 hours overnight using Git hooks that enforce TDD - wake up to fully tested features
Autonomous task automation with file creation policies and session persistence
Orchestrates specialized agents to autonomously implement entire features end-to-end
Complete system: 15 problem-shaped skills backed by 97 genius agents, 86 skills total, 30 commands, 71 tools, 36 hooks, authoritative coding-standards rules, goal-driven and autonomous build loops, and the shared Claude Code and Codex gates
Security find-and-fix loop — adapted (Apache-2.0) from anthropics/defending-code-reference-harness and generalised to all vulnerability classes. Four read/write-only skills (no target-code execution): threat-model (bootstrap from code+CVEs+git-history, or interview an owner via Shostack's four questions, or both -> THREAT_MODEL.md), vuln-scan (static source review, parallel per-focus-area subagents -> VULN-FINDINGS.json), triage (adversarial N-vote verification, 16 false-positive exclusion rules, root-cause dedup, impact-on-asset x exploitability severity with stated deployment conditions, owner routing, checkpoint/resume -> TRIAGE.json), and patch (root-cause candidate diffs + independent reviewer, fix-first priority for deployment-conditional dangerous primitives, never auto-applied -> PATCHES/). The autonomous C/C++ + AddressSanitizer execution pipeline this was extracted from is referenced, not bundled (vuln-scan/HARNESS.md).
security10 Fable 5-native Agent Skills, updated for Fable 5.1 — over-planning, gold-plating and test bloat, fabricated progress, unrequested actions, stalled autonomous runs, and a skill-refactorer for migrating pre-Fable-5 skills.
developmentAI-native scientific research pipeline. Takes a research goal — optionally with a research paper — and autonomously conducts rigorous, multi-step academic research.
researchAutonomous Development Orchestrator with 3-Tier agent system (Opus/Sonnet/Haiku). 13 specialized agents and 12 skills for the full SDLC — planning, architecture, implementation, testing, review, and documentation.
