Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
45,100 skills from 2,196 repositories · updated 12 hours ago
44,137 skills
- provider-configurationhashicorp/agent-skillsImplement Terraform provider configuration and authentication with the Plugin Framework: provider schema for credentials (Optional + Sensitive attributes), environment variable fallbacks, credential provider chains (static config, then environment variables, shared credentials file, and platform identity), unknown-value guards in Configure(), secret redaction, configure-time credential validation, and diagnostics that name every source tried. Use when implementing or reviewing a provider's Configure method or provider schema, adding authentication options (API keys, tokens, profiles, credentials files, assume-role), deciding how a provider should resolve credentials, debugging "no valid credential sources" or missing-credentials errors, or unit testing credential resolution.OfficialTestingNo warnings★ 909↓ —
- run-acceptance-testshashicorp/agent-skillsGuide for running acceptance tests for a Terraform provider. Use this when asked to run an acceptance test or to run a test with the prefix `TestAcc`, when a test fails with missing environment variables, or when diagnosing a failing or suspiciously passing acceptance test.OfficialTestingNo warnings★ 909↓ —
- gsd:statsdavepoon/buildwithclaudeDisplay project statistics — phases, plans, requirements, git metrics, and timelinePlanningNo warnings★ 3.7K↓ —
- nemotron-customizeNVIDIA/skillsPlan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize, evaluate, or compose these into a pipeline. Do NOT use for frontend/dashboard/visualization work, generic ML advice, billing/access, or non-Nemotron coding tasks.DesignNo warnings★ 3.7K↓ —
- tao-train-segformerNVIDIA/skillsSegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction, efficient for real-time segmentation tasks. Use when training, evaluating, exporting, quantizing, or running inference for a TAO SegFormer model. Trigger phrases include "train SegFormer", "semantic segmentation", "lightweight transformer segmenter", "real-time semantic segmentation".PlanningNo warnings★ 3.7K↓ —
- mcore-create-issueNVIDIA/skillsInvestigate a failing GitHub Actions run or job and create a GitHub issue for the failure.DevOpsNo warnings★ 3.7K↓ —
- jetson-quick-startNVIDIA/skillsEntry skill for Jetson / IGX BSP customization. Asks one core click-to-select setup questionnaire and passes prefilled answers to downstream setup skills.OtherNo warnings★ 3.7K↓ —
- reasoningjeremylongshore/tons-of-skills-marketplaceUse BEFORE answering analytical, diagnostic, planning, or multi-step reasoning questions. Trigger phrases include "should I X or Y", "why is X happening", "what's the best approach", "what are the tradeoffs", "help me think through", "diagnose", "root cause", "plan/design X", "what are the implications of", "compare these approaches". Also fires on cross-domain analysis, strategy questions, architecture decisions, or anything requiring multiple factors to be weighed before responding. The skill calls the reasoning MCP tool to retrieve a cognitive scaffold (named failure pattern, executable procedure, suppression vectors, falsification test) the model absorbs internally before generating its response. Catches causal shortcuts, premature conclusions, generic templates, and surface pattern matching that produce confidently-wrong answers. Do NOT trigger for simple factual lookups, syntax questions, file reads, code execution, or restating the user's input.TestingNo warnings★ 2.9K↓ —
- jetson-generate-kbNVIDIA/skillsBuild a per-target knowledge-base markdown next to the active profile by walking the BSP root and source tree. Use after init-image / init-source; not for editing profile fields.MediaNo warnings★ 3.7K↓ —
- nemo-retrieverNVIDIA/skillsUse when searching, extracting, ingesting, or querying a document collection with the NeMo Retriever 26.8.1 CLI, including local LanceDB indexes and deployed Retriever services. Use for PDFs, images, Office files, HTML, text, audio, and video; not for editing documents or web search.DevOpsNo warnings★ 3.7K↓ —
- mcore-split-prNVIDIA/skillsSplit a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.OtherNo warnings★ 3.7K↓ —
- doca-flow-tuneNVIDIA/skillsUse this skill when the user is tuning a live or captured `doca-flow` pipeline with `doca_flow_tune` — snapshotting pipe / counter / KPI state, picking a tuning axis (rule placement, resource hints / table sizing, HW-offload mode) and a matching measurement (rule-install rate, lookup latency, hardware-counter delta), running offline or online (read-only or state-changing) modes, reading the dumper CSV / analyze JSON / visualize mermaid, or applying a recommendation back into the Flow program. Trigger even when the user does not explicitly mention "doca_flow_tune" — typical implicit phrasings include "Flow rule-install rate is low on BlueField", "table sizing looks wrong for this pipe", "tune visualize step is empty", "before/after counters don't move", or "which doca-flow knob does this recommendation hit". Refuse and route elsewhere for measuring baseline numbers (doca-flow-perf), writing the doca-flow application, DOCA install, or streaming Flow telemetry — those belong to other skills.DataNo warnings★ 3.7K↓ —
- brandkitplugin87/ux-ui-agent-skillsGenerate a complete, accessible brand design system from a brief, as primitive → semantic → component DTCG tokens (color, type, spacing, radius, shadow, motion), light + dark, plus a single theme.css, verified for WCAG. Use when the user wants a from-scratch brand/design foundation, a new palette + type system, or a themeable token kit for a product.DesignNo warnings★ 3K↓ —
- cudaq-importingNVIDIA/skillsUse when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.OtherNo warnings★ 3.7K↓ —
- gateplugin87/ux-ui-agent-skillsRun the one-command quality gate and report the real N/N result. Use before claiming any build/review is done.EngineeringNo warnings★ 3K↓ —
- provider-framework-migrationhashicorp/agent-skillsMigrate Terraform provider resources and data sources from Plugin SDKv2 to the Plugin Framework: muxing both plugins in one provider (terraform-plugin-mux, tf5to6server), per-resource migration workflow, SDKv2-to-Framework schema mapping (ForceNew, ValidateFunc, DiffSuppressFunc, Default, Timeouts, blocks), null-vs-zero-value behavioral traps, and state-compatibility verification. Use when converting or translating SDKv2 resources to the Framework, setting up a muxed provider server, deciding whether a resource should be migrated at all, or debugging plan diffs and state errors that appeared after a migration.OfficialDevOpsNo warnings★ 909↓ —
- critiqueplugin87/ux-ui-agent-skillsAdversarial design critique of the current work. Render it, look at it, and argue for rejection. Run after the gates are green, never instead of them.DesignNo warnings★ 3K↓ —
- terraform-stackshashicorp/agent-skillsComprehensive guide for working with HashiCorp Terraform Stacks. Use when creating, modifying, or validating Terraform Stack configurations (.tfcomponent.hcl, .tfdeploy.hcl files), working with stack components and deployments from local modules, public registry, or private registry sources, managing multi-region or multi-environment infrastructure, or troubleshooting Terraform Stacks syntax and structure.OfficialDevOpsNo warnings★ 909↓ —
- doca-benchNVIDIA/skillsRun `doca_bench` (DOCA 2.7.0 or newer) to measure throughput, bulk latency, precision latency, or maximum bandwidth for RDMA, Compress, AES-GCM, SHA, DMA, EC, Ethernet, Comch, or GPUNetIO on a host or BlueField Arm. Use it to discover enabled benchmark libraries, capture a reproducible command/version/device/environment baseline, compare stable runs against a declared tolerance, or diagnose configuration, device-binding, workload-precondition, and measurement failures. Trigger for requests such as measuring BlueField compression speed, NIC RDMA throughput, crypto latency, or a pre-upgrade baseline. Do not use for application end-to-end timing, custom benchmark code, DOCA installation, or binary patches.FinanceNo warnings★ 3.7K↓ —
- speckit-checklistforyourhealth111-pixel/Vibe-SkillsGenerate custom quality checklists for validating requirements completeness and clarity. Use to create unit tests for English that ensure spec quality before implementation.PlanningNo warnings★ 3.8K↓ —
- governanceplugin87/ux-ui-agent-skillsGovern how the design system evolves (SemVer for tokens/components, the contribution workflow, deprecation policy, and change communication). Use when the user wants to add/promote/deprecate a component or token, decide a version bump, set up a contribution process, or keep the system from fragmenting.DesignNo warnings★ 3K↓ —
- doca-flowNVIDIA/skillsDOCA Flow packet steering on NVIDIA NICs/DPUs, linked into a C application: match/action pipes, ports, representors, forwarding, hairpin, counters. Trigger on packet steering, classifier, rule-matching, representor, hairpin or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Also covers pipe validation before hardware programming, Flow/DOCA version match, and Flow API errors. First app: follow the local bundle (doca-setup, doca-programming-guide, doca-flow, doca-public-knowledge-map). Stage 1 pull nvcr.io/nvidia/doca/doca (NGC catalog tag, never invent); Stage 2 needs a real NIC. Then ls samples inside the container; never emit main.c, a Dockerfile, or hand-typed -l flags. Route software-only host pipelines, plain DPDK `rte_flow`, kernel TC, OVS, BFB bring-up, and DPU OS installation elsewhere. DPU OS installation is destructive and always requires explicit confirmation.EngineeringNo warnings★ 3.7K↓ —
- gsd:inboxdavepoon/buildwithclaudeTriage and review all open GitHub issues and PRs against project templates and contribution guidelinesEngineeringNo warnings★ 3.7K↓ —
- doca-argpNVIDIA/skillsUse this skill for hands-on DOCA Arg Parser CLI work on a shipped sample or new DOCA-using app — adding / removing / renaming flags; wiring `doca_argp_init` → register params → `doca_argp_start` → `doca_argp_destroy` in order; picking a parameter type from the full public enum (`DOCA_ARGP_TYPE_STRING`, `_INT`, `_BOOLEAN`, `_DEVICE`, `_DEVICE_REP`, `_DOUBLE` — six values, not three); preserving the built-in DOCA flags (`-h` `-v` `-l` `--sdk-log-level` `--log-filter` `-j`/`--json`, NOT `--json-config`); `--device` is per-program; or debugging `DOCA_ERROR_BAD_STATE` / `INVALID_VALUE` / `NOT_SUPPORTED` / `IO_FAILED` from `doca_argp_*`. Trigger on implicit phrasings: "add a custom flag to a DOCA sample", "should I use getopt here", "BAD_STATE registering a new param", "my JSON config key is rejected", or "my sample's --json is ignored". Refuse and route elsewhere for variadic-flag / subcommand / shell-completion features, DOCA Core context, or DOCA Log internals.EngineeringNo warnings★ 3.7K↓ —
gsd:help
davepoon/buildwithclaude
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npx skills add davepoon/buildwithclaude --skill gsd:help -a claude-code
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