Claude Code plugins
96 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 96 plugins that match these filters.
96 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 96 plugins that match these filters.
Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceCloud architecture design for AWS/Azure/GCP/OCI, Kubernetes cluster configuration, Terraform infrastructure-as-code, hybrid cloud networking, and multi-cloud cost optimization
Per-deal-and-packaging Commercial domain. v2.8.0 ships 8 skills: orchestrator + pricing-strategist (model picker + Van Westendorp WTP + packaging) + deal-desk (deal scorer + discount approval routing + redline) + partnerships-architect (5-tier classifier + joint GTM + revshare modeler) + channel-economics (cost-to-serve + ROI + mix optimizer) + commercial-policy (data-backed discount matrix + exception flow + linter) + rfp-responder (Shipley structured RFP/RFI/RFQ + win-theme + winrate predictor, context: fork) + commercial-forecaster (4Q-weighted bookings + cohort NRR/GRR + funnel-confidence with mandatory assumption disclosure). Hard rules: pricing outputs model+range (never a single number), deal outputs route to named human approver (never auto-approve), forecast outputs surface conversion assumption, RFP never invents claims for GAP requirements. 21 stdlib Python tools, 28+ reference docs. Distinct from business-growth (sales execution), c-level-advisor/cro-advisor (strategic), fi
Governed Exa operator pack with 30 workflows for Search, Contents, Agent, Monitors, Websets, access, reliability, cost, and security.
Complete OpenRouter integration skill pack with 30 skills covering LLM routing, model selection, cost optimization, and multi-provider orchestration. Flagship+ tier vendor pack.
Verified PostHog operator pack with 24 workflows for regional ingestion, identity, flags, privacy, costs, incidents, migrations, and safe releases.
Evidence-gated Vast.ai operator pack for GPU rentals, Serverless, governance, recovery, and cost control (24 skills)
AI/ML engineering skills for shipping AI features responsibly: Model Card, AI Eval Plan, RAG Design Doc, Agent Spec, Dataset Datasheet, AI Feature PRD, LLM Cost & Latency Budget, Prompt Optimizer, Eval Rubric Designer, RAG Architecture Review, LLM Guardrails Spec, Agent Design Review, and Model Selection Advisor.
Database query optimization, cloud cost optimization, and scalability improvements
The top video families in depth: conditioning modes, native audio, editing and extension, and per-member caps and costs.
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.
Meeting discipline. Cost-gates every meeting before it exists (MEET / ASYNC / NOT-READY verdicts with real dollar cost incl. optional refocus overhead), builds timeboxed agendas that refuse topics without a desired outcome (decision topics first), and extracts owned action items from raw notes with ORPHAN / NO-DUE flags. Fills the meeting-hygiene gap from audit/productivity-2026-07.
Operate AI agents and LLM features in production: prompt regression suites, model migration plans, context-engineering reviews, agent incident postmortems, and observability specs — plus the pm-ai evaluation and cost skills they build on.
Twelve-skill bundle covering the Journal of Policy Analysis and Management (JPAM) manuscript lifecycle — credible policy evaluation, cost-benefit and distributional analysis, and clear policy implications across the policy fields.
academicCross-journal author workflow toolkit (11 skills): pick the venue (journal-match runs tools/match_venues.py over a generated index of 743 venues with a resubmission-ladder graph, and is measured against a held-out gold set), cost the submission ladder in months and probability of placing, verify an unfamiliar venue against primary sources before submitting, reframe a manuscript from one venue to another, run the analysis through the StatsPAI / Stata MCP execution bridge, self-check submission readiness, score desk-reject risk against the target's own documented triggers, rehearse a calibrated referee panel, draft the point-by-point response, and assemble a Data-Editor replication package. Venue-neutral; reads each target pack's skills + official-source-map for the venue bar and live facts.
academicUltra-compressed communication mode. Cuts token usage 20-50% (75% upper bound) by dropping filler, articles, pleasantries, and hedging while keeping full technical accuracy. Derived from Matt Pocock's MIT-licensed caveman with: (1) 3 stdlib Python tools (deterministic compressor, token-savings estimator with $/Mtok cost extrapolation, lint that detects banned vocab with code-block + exception-zone whitelisting), (2) 3 references citing 7-8 sources (compression principles, when caveman backfires, companion tooling), (3) cs-caveman-mode persona agent + /cs-caveman slash command. Matt's persistence rules + auto-clarity exception preserved verbatim per MIT.
Hardware & physical-product skills: hardware PRDs with cost stacks, BOM cost review, EVT/DVT/PVT gate reviews, factory acceptance tests (AQL), tooling risk assessment, and RMA failure analysis (8D) — NPI judgment for HW PMs and ops leads.
36 skills for deciding if AI fits, testing it before launch and keeping it honest after: deciding if AI fits, speccing it as tests, briefing the model and the agent, building the evals from real outputs, shipping it safely, running and improving it, and its cost, price and explanation to leaders. Each skill is one named method or one artifact, with the inputs it needs, where it breaks and a fixed output template.
productivity29 Claude Skills for pricing your expertise when your clients have AI too, from your own numbers to the price increase letter: the floor rate, the client profitability map, the AI activity map and cost ledger, the savings decision, the full-price work list and AI positioning, productised offers and their unit economics, value and outcome-based pricing, the three-option proposal, the price conversation, the AI discount request and the “AI could do this in 10 seconds” objection, the AI disclosure policy and statement of work, retainer redesign, scope changes, and the price increase plan, letter and quarterly review.
productivityAKS operational skills for troubleshooting, advanced cost-efficiency analysis, known issues, GPU inference, and packet capture.
Chief AI Officer advisory for startups: model build-vs-buy calculator (API vs fine-tune vs build with 3-year TCO across 6 paths + breakeven that balances economics with practical feasibility), AI risk classifier (EU AI Act tier with 7 Article citations + US state patchwork: NYC LL 144, CO AI Act, IL HB 53, CA SB 1001, IL BIPA + industry overlays for FDA AI/ML, CFPB Circular 2023-03, NYDFS Reg 23, NAIC, ECOA, Fed SR 11-7), AI cost economics (API vs self-hosted breakeven with 2026 pricing across A100/H100, utilization reality, hidden costs). 4 in-depth references each citing 5+ authoritative sources. Standalone-installable; also bundled in c-level-skills. Strategic only — does not duplicate engineering AI/ML skills.