37 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 37 plugins that match these filters.
air-gapped-marketplaceair-gapped/skills3
coalmineTheColliery/CoalMine1
alexgreensh-repo-forensicsalexgreensh/repo-forensics1
365-skillsAgents365-ai/365-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplace9 quality-canary skills: rot-canary (code-health), gold-standard (world-class completeness), source-grounding (anti-hallucination), supply-chain-audit (deps/build/artifact), resilience-audit (failure-mode FMEA), telemetry-canary (observability), testability-canary (decoupling/DI), scale-canary (perf scaling), drift-canary (contract/schema drift). rot-canary adds cross-platform auto-cadence hooks. Served conformed from the committed plugin/ dist (built by scripts/build-plugin.mjs).
Offline security scanner for AI-agent repos, skills, plugins, and MCP servers. 29 scanners detect prompt injection, MCP tool poisoning, credential exposure, malicious lifecycle hooks, package IOCs, CVEs, CISA KEV exposure, and post-incident traces. Zero dependencies. Zero telemetry.
Authoritative reference for Pi (@earendil-works/pi-coding-agent): install, configure, run, and extend. Use when the user asks about Pi CLI/flags/commands, providers/models/auth, settings/compaction/sessions, security/trust, extensions, skills, prompt templates, themes, packages, MCP, codemode, custom providers, TUI components, the SDK, RPC mode, or JSON streaming. Also use for Pi's design philosophy and opinionated choices (why minimal, 4 tools, YOLO, no MCP/plan mode/to-dos/sub-agents), Pi's internal monorepo architecture (the Chord typed host runtime (@earendil-works/chord), the durable agent harness (@earendil-works/pi-durable) and its Pico5 specification, telemetry, delta tracking), and the repository's build, development, and contribution rules. Also use for Pi Durable itself: what the experimental harness is, its versions, the runnable examples, and which blog demos 1.2.0 removed.
Distributed tracing tests with OpenTelemetry: assert span names, attributes, parent links and status, and query Jaeger, Zipkin or Tempo in CI. Validates OpenTelemetry Collector pipelines and writes per-feature trace specs. A review agent (trace-coverage-reviewer) finds untraced critical paths and spans missing semantic-convention attributes.
backend-distributedLLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices
Design, run, review, or release framework- and vendor-neutral evaluations and observability for AI agents. Use when defining agent evals, datasets, graders, trajectory review, monitor independence, oversight quality, regression analysis, release gates, production traces, or privacy-aware telemetry. Covers task and trajectory contracts, statistical comparisons, and incident-to-case learning; route framework implementation to pydanticai or langgraph when needed. Do not use this skill for unrelated requests; route to the nearest named specialist.
Use when deciding whether an AI-enabled workflow should be adopted, scaled, constrained, redesigned, or retired, and the decision must connect business outcomes, worker or user effects, quality guardrails, full operating cost, telemetry, uncertainty, and accountable governance. Do not use for a standalone financial model, infrastructure cost calculation, agent evaluation design, runtime operations, or general AI governance; route those details to the neighboring specialist skills.
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 the observability stack that deploys as one unit: Prometheus scrape configuration, recording and alerting rules, relabeling, retention, and high availability; OpenTelemetry Collector pipelines (receivers, processors, exporters, sampling, trace/span correlation); and Loki ingest, LogQL, retention, and label design — with a bundled read-only telemetry-check script for Prometheus rule sanity and scrape-target reachability. Use when running, tuning, or troubleshooting a Prometheus, OpenTelemetry Collector, or Loki deployment, or reviewing the collection/ingest/retention layer, including bounded PromQL and LogQL query construction, semantic review, and no-data diagnosis. Do not use for observability strategy, SLI/SLO design, or paging policy (that is platform-engineering), Grafana dashboards, panels, and Grafana-side alerting (that is grafana), or Tempo/tracing operations, which remain deferred to a future named-tool skill.
Inference KV-cache and transport suite — LMCache multiprocess (MP) standalone-server mode (DaemonSet + Deployment K8s pattern, ZMQ connector, L1 + L2 NIXL/POSIX/GDS/HF3FS/fs/s3/mooncake adapters) and NVIDIA NIXL transfer library (UCX/GDS/Mooncake/libfabric/HF3FS/S3 plugins, agent API, telemetry) used by Dynamo/vLLM/SGLang. Pairs with the vllm-caching skill in the `vllm` suite and sglang-hicache in the `sglang` suite.
inferenceCognitive memory for AI agents — semantic recall, a knowledge graph, contradiction detection and procedural learning. Local-first: one SQLite file, no account, no telemetry.
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.
inferenceDell hardware suite — Ansible automation against Dell PowerEdge iDRAC 9 (14G–16G) and iDRAC 10 (17G) BMCs via the `dellemc.openmanage` collection. Covers the iDRAC 10 / iDRAC 9 ≥ 7.30.10.50 `BasicAuthState: Unadvertised` default that silently 401s `ansible.builtin.uri`, the canonical `idrac_session` + `x_auth_token` lifecycle, iDRAC 10 attribute registry deltas (renamed groups, deprecated `vFlashSD*`/`Telemetry*`/`GroupManager`, the BIOS→`System.ServerPwr.*` move), iDRAC 9-only modules to avoid on 17G, WS-MAN removal on 17G, and the upstream bug catalog. Room for sibling skills covering RACADM, OpenManage Enterprise, DUP catalogs, and PowerEdge BIOS tuning.
infrastructureTwo slash-command skills: janitor-device diagnoses and cleans up slow macOS, Windows or Linux machines, and janitor-claude audits and tidies the current Claude Code working directory. Read-only by default, every mutation requires explicit confirmation, no silent sudo, no git mutations, no telemetry.
Live status line and agent telemetry — the active /command or skill run with a timer, per-subagent context bar, model, estimated cost, tokens, duration and tool count, plus a per-run report. Run /statusline-kit:setup once to switch the status line on.
Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors.
OpenTelemetry Collector component configuration: config keys, defaults, validation rules, signal support, stability, and gotchas. Progressive disclosure via components/<name>/README.md plus on-demand detail files.
Building custom OpenTelemetry Collector distributions with OCB: builder manifest authoring, core/contrib/provider version alignment, local component development, CI/Docker/multi-arch builds, and troubleshooting.
OpenTelemetry declarative YAML configuration for SDK setup (otelconf, OTEL_CONFIG_FILE, file_format). Points at upstream schema, env-var substitution, and configuration precedence.
OpenTelemetry Transformation Language (OTTL) expert for writing and debugging telemetry transformations in the Collector (transform, filter, routing, tail_sampling).
