27 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 27 plugins that match these filters.
lawve-ailawve-ai/awesome-legal-skills2
testland-qatestland/qa2
awesome-journal-skillsbrycewang-stanford/Awesome-Journal-Skills1
claude-code-skillsalirezarezvani/claude-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
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Twelve-skill bundle covering the 《经济学(季刊)》 manuscript lifecycle: workflow router, fit positioning, internationally-legible topic selection, field-benchmarked literature review, structural / quasi-experimental identification, modern staggered-DID compliance, inference rigor, mechanism evidence, figures-over-tables, English abstract, submission preflight, and rebuttal.
academicKnowledge 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.
The post-draft lifecycle nobody else covers: CFP requirement parsing, venue/track selection and tailoring, desk-reject preflight, anonymization, review triage, benchmark scorecards, rebuttals in all three real formats, and camera-ready rails for ACM and IEEE.
Six Xcode build optimization skills in one plugin: benchmark, compilation analysis, project analysis, SPM build analysis, a recommend-first orchestrator, and a fixer for applying approved changes.
developmentLoad testing and performance benchmarking with metrics analysis and bottleneck identification
vLLM operator reference suite — deployment, configuration, quantization, caching, KV, tool parsers, reasoning parsers, chat templates, benchmarking, performance tuning, observability, omni, input modalities, speculative decoding, NVIDIA hardware, and a Gemma 4 31B operating-point serve recipe.
inferenceRoutes any legal task to the right LLM, like OpenRouter but for legal work and grounded in benchmarks instead of brand loyalty. Built from mid-2026 legal evals (legalbenchmarks.ai, Vals AI × Stanford LegalBench across 124 models, Harvey's Legal Agent Benchmark, the Atticus Project's CUAD/MAUD/ACORD) plus translation evidence (WMT25, SwiLTra-Bench, ArabLegalEval). Covers five verticals: contract drafting, info extraction, legal research, contract review, and legal translation (including Arabic/MENA). Each asks up to four questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then returns a primary model, a fallback, what to avoid, and what a human must verify. Core principle: capability is not controllability, so every route ends with a verification step. Not legal advice; a lawyer owns the output.
Test whether your Claude Code skills actually trigger, and benchmark any coding agent — author, run, and analyze the eval suite that proves it, locally or as a CI gate.
testing4 industry-analysis skills: peer benchmarking, sector overview, competitive positioning, and supply-chain mapping.
Email marketing operating manual for AI agents, with hard send-safety gates: campaigns, newsletters and flows, deliverability and DNS, switching ESPs, compliance, anti-slop copy, email design and benchmarks, with any ESP through MCP or an API.
Expert CIS Controls v8 (CIS Top 18) advisor — implementation group scoping (IG1/IG2/IG3), control gap assessments, safeguard-level guidance, asset inventory, software inventory, data protection, secure configuration, account management, access control, continuous vulnerability management, audit log management, email and web browser protections, malware defenses, network infrastructure management, network monitoring and defense, application software security, incident response, penetration testing, and CIS Controls mapping to NIST CSF, ISO 27001, SOC 2, and CMMC. Use for any question about CIS Controls, CIS Benchmarks, Implementation Groups, or prioritized cyber hygiene for any organization size.
Search relevance testing for Elasticsearch, OpenSearch, Solr, vector and hybrid search: judgment lists and NDCG, MRR and recall@k regression checks. Builds judgment lists with rater guidelines, benchmarks vector recall against latency, and measures fusion lift in hybrid pipelines. A review agent (relevance-regression-reviewer) compares before and after metrics per query.
ai-mlOperate, configure, benchmark, and troubleshoot llama.cpp across CPU, Metal, CUDA, HIP/ROCm, Vulkan, SYCL, and hybrid or multi-GPU systems. Use when installing or building llama.cpp, selecting or inspecting GGUF models, running llama-cli or bounded llama-completion, serving an OpenAI-compatible API with llama-server, tuning memory and performance, or diagnosing backend, context, template, and API failures. Do not use for model training or fine-tuning, general inference-framework selection, llama-cpp-python or other bindings, LlamaIndex, Ollama, or LM Studio operation.
Design organizational topology, talent, rewards, culture, and health decisions with explicit decision rights and validation. Do not use for legal advice, project delivery planning, financial modeling, or technology implementation. reporting structures), talent strategy (make-vs-buy, skill taxonomies, succession planning), compensation frameworks (market benchmarking, equity design, leveling), culture architecture (values codification, rituals, psychological safety), organizational health metrics (eNPS, retention risk, engagement surveys), DEI strategy (inclusive design, equitable systems, belonging).
Optimize software performance through user-journey baselines, profiling, representative benchmarks, controlled experiments, and field verification. Use when asked to make an application, service, workflow, or hot path faster or more efficient, or to build the measurement loop first. Do not use for a reliability incident, capacity forecast, generic product A/B test, or operating a named telemetry or load-testing tool without an optimization objective.
Operate, configure, benchmark, and troubleshoot vLLM inference servers: Docker and Kubernetes deployment, quantization-aware model configuration (tensor parallelism, KV cache), OpenAI-compatible API serving, throughput and latency benchmarking, continuous batching tuning, GPU operation, and upgrade/rollback. Use when deploying or running a vLLM server (vllm serve, vllm/vllm-openai), sizing a model and its KV cache for GPUs, selecting quantization and parallelism, serving via /v1 endpoints, measuring serving throughput or latency, tuning batching, or diagnosing GPU, OOM, or startup failures in a vLLM deployment. Do not use for model training, fine-tuning, evaluation-set design, or engine-selection methodology (that is ml-engineering), or for operating the llama.cpp stack with GGUF models (that is llama-cpp); other inference engines (TGI, Ollama, Triton) are out of scope.
Factory for building production-grade agent skills: interviews you for purpose and gold standards, researches the domain, drafts the skill, autonomously improves it against an LLM-as-judge (or real-world) metric, and verifies it with an independent multi-agent panel — delegating to tribunal when installed. Ships a same-model-uplift benchmark standard (bare vs skill vs skill+panel, scored by execution, honest negatives required) and a publish checklist, and stops the improvement loop on a plateau inside judge variance instead of iterating toward an unreachable target. Extends the official single-pass skill creators. Orchestrates companion skills (autoresearch, premortem, handoff) — install those alongside it; the full batteries-included environment is the agent-skills-harness.
NVIDIA AIPerf — vendor-neutral generative-AI inference benchmarking (the genai-perf successor): `aiperf profile` with concurrency / request-rate / fixed-schedule trace-replay / user-centric / multi-run-confidence load, 15 endpoint types and 6 custom dataset formats, goodput SLOs, GPU + Prometheus telemetry, and the reasoning-token TTFT/TTFO split — benchmarks vLLM / SGLang / TensorRT-LLM / Dynamo / NIM / Triton / Ollama.
inferenceGo and Rust unit testing: table-driven Go tests with t.Run subtests, benchmarks, native fuzzing and the race detector, plus cargo test with Criterion. Each language skill covers framework choice and test conventions, with Ginkgo and Go mocking, rstest and mockall as references.
functional-testingSix skills for landing pages: webanatomy-setup, find-examples, audit-page, research-best-practices, write-page, build-page. Read a page as a structure, ground every move in a scored benchmark library, and assemble a shareable wireframe.
