35 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 35 plugins that match these filters.
aiwgjmagly/aiwg1
godmodeNoobyGains/godmode1
team-avatartmcfarlane/team-avatar1
ui-crafteduclopez/ui-craft1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceFounder-mode executive team plugin: 13 cs-* C-suite agents (CFO, CMO, CRO, CPO, COO, CHRO, CISO, Chief of Staff, General Counsel, Chief Data Officer, Chief AI Officer, Chief Customer Officer, VP of Engineering) with distinct cognitive voices, plus 21 /c-level-agents:* slash commands — forcing-question office hours (CFO/CMO/CPO/CRO/CTO/CISO/GC/CDO/CAIO/CCO/VPE reviews), strategic sprint pipeline (brief → boardroom → decide → execute → post-mortem), and meta routing (/c-level-agents:founder-mode auto-router, /cs-onboard, /c-level-agents:cross-eval multi-model consensus, /c-level-agents:freeze cooldown lock). Wraps the 33 c-level skills with cognitive gearing, persona voice, and artifact-driven handoffs. The business-domain answer to YC Garry Tan's gstack.
Internal BizOps domain. v2.8.0 ships 7 skills: orchestrator + process-mapper (BPMN/bottleneck/cycle-time, Lean+TOC) + vendor-management (scorecard+SLA+3rd-party risk, NIST SP 800-161/ISO 27036) + capacity-planner (Erlang-C queueing math for ops teams, NOT engineering) + internal-comms (ADKAR+Kotter 8-step, NOT marketing) + knowledge-ops (SOP+runbook+KB hygiene with 5W2H, context: fork) + procurement-optimizer (UNSPSC spend categorization + supplier consolidation). Orchestrator uses context: fork to route inquiries via Matt Pocock grill discipline (one question per turn, recommended answer, canon-cited challenge). Every SKILL.md ships a Forcing-question library section. 18 stdlib Python tools, 24+ reference docs. Distinct from business-growth (external sales) and c-level-advisor (strategic).
Build, launch, grade, and schedule Claude Managed Agents (CMA) in your own Anthropic account — a plugin re-implementation of Anthropic's launch-your-agent reference skill (Apache-2.0; independent, not a fork). Every session starts with a goal (./my-agent/goal.json, opt-in SessionStart hook, driven by /cs-goal); loop_compiler.py compiles it into a bounded grade->iterate loop (max_iterations 1..20), a recurring cron scheduled-deployment loop, or a single-pass interview->stage->launch workflow. 6 skills: orchestrator (context: fork goal router) + interview + stage-launch + grade-iterate + run-without-you + wrap-up. 18 stdlib-only deterministic scaffolders (no network/API calls; live launches emitted as BYOK curl that never prints the key), 4 agents, 8 commands, hooks, 5 references, 4 assets. Validators enforce CMA limits. Distinct from engineering/agent-harness (generic domain loop) and write-a-skill (authors Claude Code skills, not CMAs).
ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
Binary reverse engineering, malware analysis, firmware security, and software protection research for authorized security research, CTF competitions, and defensive security
AI image generation with creative workflow orchestration, prompt engineering, and curated inspiration library via MCP server
Engineer an agent's forgetting, not just its remembering. Four deterministic stdlib scripts implement the four lenses of agent memory: a cost profiler that splits construction from query spend and reports cost per correct answer (construction energy exceeds total query energy across 300 queries in the Stanford characterization); an architecture picker that scores the four paradigm families — long-context, flat RAG, structure-augmented RAG, agentic — disqualifies on hard constraints, names the cost the winning choice makes you pay, and refuses to pick when the top two tie; a density auditor that classifies every record in a memory directory or JSONL export as FACT / SKILL / LOG / PROSE, finds near-duplicates, and flags stale wording; and a forgetting-policy linter that fails any design with no forgetting rule. Ships cs-memory-engineer agent, /cs-memory-engineering and /cs-forgetting-audit commands, 4 references, and a seven-question forcing worksheet.
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.
Conversation-handoff document generator. Compacts the current session into a markdown handoff for a fresh agent — references existing artifacts (PRDs, plans, ADRs, issues, commits) by path/URL instead of duplicating them. Derived from Matt Pocock's MIT-licensed handoff with: (1) 3 stdlib Python tools (template generator tailored to 5 next-session emphases, artifact deduplicator across 5 categories of duplication, skill recommender matching content to 14 skills in this repo), (2) 4 references citing 7-8 sources (handoff structure, deduplication discipline, next-session skill matching, companion tooling), (3) cs-handoff-author persona agent + /cs-handoff slash command. Matt's no-duplication discipline + mktemp convention preserved verbatim per MIT.
Knowledge base compiled from OpenAI's Spinning Up in Deep RL (MIT, Joshua Achiam) by engineering/book-to-skill. A resident core carries the RL optimization problem, the model-free taxonomy, the policy-gradient template with its five valid weights, the safe-step family (VPG to TRPO to PPO) and the overestimation family (DDPG to TD3 and SAC); 20 on-demand chapters cover key concepts and MDPs, the algorithm taxonomy and model bias, the policy gradient derivation with the log-derivative trick and EGLP lemma, Achiam's researcher essay, the key-papers topic map, the exercises including the silent DDPG broadcasting bug, the benchmark parity disclosure, one chapter per algorithm, and the logger/MPI/ExperimentGrid utilities. Ships a glossary, a patterns file with 16 techniques, a decision cheatsheet with thresholds, a cs-spinning-up-deep-rl agent and /cs-spinning-up-deep-rl. Structured study notes, not a reproduction of the source.
Engineering discipline for AI coding agents: question the request, verify against current docs instead of memory, plan, build test-first on the cheapest capable model, review with a second model family, prove it before saying done
Principal-level engineering skills for Claude Code and Codex. Aang routes the work, and each character owns one discipline.
Design engineering skill + system for AI coding agents: anti-slop UI rules, outcome recipes, spec-driven design (/sddesign), design-review + a11y agents, and MCP quality gates.
AI-powered development tools that get smarter with every use. Make each unit of engineering work easier than the last. Includes 18 specialized agents, 19 commands, 29 skills, and 4 hooks, 1 MCP server.
Spec-driven development and context engineering for AI coding agents, backed by project context in Git.
Chaos testing for resilience with failure injection, latency simulation, and system resilience validation
22 engineering documentation skills covering the complete software lifecycle.
documentationTiered AI code-review: triage -> relevant specialist reviewers -> synthesis. Built to catch what AI-written code actually ships with: code that ignores what the repository already knows, needless dependencies, over-engineering, hallucinated APIs.
PRD-CE methodology engine: scaffold (PRD/SoT/EPICs) + lifecycle skills + governance hooks + readiness scoring.
Risk-graded context-engineering skills, command prompts, and change-record templates. No hooks are configured, so nothing runs automatically; the bundled ng CLI is included for repo-side use (run from a checkout) and runs nothing on its own.
