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.
49,105 skills from 2,358 repositories · updated 2 minutes ago
49,103 skills
- jetson-init-sourceNVIDIA/skillsSet up the BSP source workspace: Linux_for_Tegra overlay tracker, bsp_sources, Crosstool-NG toolchain. Use after jetson-init-image; not for fetching inputs.MediaNo warnings★ 3.8K↓ —
- performanceplugin87/ux-ui-agent-skillsOptimize UI performance against Core Web Vitals (LCP, INP, CLS) with loading/code-split strategy, layout-shift prevention, and animation performance rules. Use when the user wants to improve speed, fix jank or layout shift, hit Web Vitals budgets, or make a UI feel fast on low-end devices.DesignNo warnings★ 3.1K↓ —
- clade-rate-limitsjeremylongshore/tons-of-skills-marketplaceHandle Anthropic rate limits — understand tiers, implement backoff, Use when working with rate-limits patterns. optimize throughput, and monitor usage. Trigger with "anthropic rate limit", "claude 429", "anthropic throttling", "anthropic usage limits", "claude tokens per minute".OtherNo warnings★ 2.9K↓ —
- inbox-placement-monitoraaron-he-zhu/aaron-marketing-skillsUse when the user asks to "track where my emails are actually landing after I send", "read my seed-list inbox vs spam vs promotions results", "trend my Gmail Postmaster / Microsoft SNDS reputation", or "did placement drop after my last send"; produces a per-provider inbox/spam/promotions placement read, a domain/IP reputation trend from Postmaster + SNDS, a send-over-send delta with named regressions, and a reusable SEND-S placement snapshot on your own exported telemetry. Not for the pre-send SPF/DKIM/DMARC auth pre-flight — use deliverability-qa; not for computing the EQS or running the vetoes — use email-quality-auditor. 邮件收件箱落点监测/收件箱vs垃圾邮件/Postmaster声誉趋势TestingNo warnings★ 3.1K↓ —
- tao-train-mask-auto-labelNVIDIA/skillsMAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations (point or box annotations) using a ViT-MAE backbone. Use when training, evaluating, or running inference for a TAO MAL model. Trigger phrases include "train MAL", "Mask Auto-Label", "weakly-supervised segmentation", "box-prompted segmentation", "minimal-annotation mask prediction".OtherNo warnings★ 3.8K↓ —
- nemotron-retrieval-recipesNVIDIA/skillsUse when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.DevOpsNo warnings★ 3.8K↓ —
- golang-securitysamber/cc-skills-golangSecurity best practices and vulnerability prevention for Golang — injection (SQL, command, XSS), cryptography, path traversal, SSRF and HTTP security headers, cookies, secrets management, memory safety, PII in logs, STRIDE/DREAD threat modeling, plus `gosec` SAST, race detection, and fuzz testing. Apply when writing, reviewing, or auditing Go code for security, or when touching crypto, file or network I/O, secrets, user input, or authentication. Not for non-exploitable defensive bugs such as nil panics or slice aliasing (→ See `samber/cc-skills-golang@golang-safety` skill), dependency vulnerability scanning with govulncheck (→ See `samber/cc-skills-golang@golang-dependency-management` skill), or wiring security scanners into CI pipelines (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).SecurityNo warnings★ 3.8K↓ —
- tao-finetune-cosmos-embedNVIDIA/skillsCosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "search videos with text".MediaNo warnings★ 3.8K↓ —
- holoscan-module-lifecycleNVIDIA/skillsDevelop external Holoscan Modules with the Holoscan CLI and use them in Holoscan applications: scaffold, test, Python bindings, DEB/WHEEL packaging, and clean-consumer proof.TestingNo warnings★ 3.8K↓ —
- nemo-fabric-build-adapterNVIDIA/skillsBuild, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract. Use when creating adapter or target descriptors, mapping AgentConfig into an agent harness or custom-agent runtime, implementing start/invoke/stop, declaring schemas and capabilities, packaging discovery metadata, or assessing adapter conformance. Do not use for consumer applications that only call the NVIDIA NeMo Fabric SDK.LegalNo warnings★ 3.8K↓ —
- tao-run-dinov3-ssl-deftNVIDIA/skillsRun iterative DINOv3 SSL DEFT data selection and retraining from an unlabeled pool, using GRIT or multi-task weakness and fixed-encoder retrieval. Use for active-learning-style DINOv3 mining, not plain pretraining/fine-tuning (tao-train-dinov3) or AOI/PAS DEFT.ProductivityNo warnings★ 3.8K↓ —
- tao-train-grounding-dinoNVIDIA/skillsGrounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for language-guided detection — detects objects described by text prompts without a fixed class vocabulary. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Grounding DINO model. Trigger phrases include "train Grounding DINO", "open-vocabulary detection", "text-prompted detector", "language-guided object detection".OtherNo warnings★ 3.8K↓ —
- remove-ai-slopscode-yeongyu/oh-my-openagentRemoves AI-generated code smells from branch changes or an explicit file list behind regression tests, including junk tests (prose pins, source greps, copied inventories, mock echoes), each proven by its class's rule before a cut. Use when the user asks to clean up, deslop, remove AI-slop patterns, or prune tests from recent changes.TestingNo warnings★ 70.6K↓ —
- jetson-video-capabilityNVIDIA/skillsUse when Jetson codec, profile, chroma, bit-depth, dimension, engine-count, or operational support must be reconciled from live APIs, authenticated NVIDIA samples, and NVIDIA documentation; also applies the content-DRM scope.MediaNo warnings★ 3.8K↓ —
- tao-finetune-kumo-anomalyNVIDIA/skillsKumo-Anomaly — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune Kumo-Anomaly", "run Kumo-Anomaly inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune Kumo-Anomaly", "use perform_anomaly_analysis_with_diffusion", "automl Kumo-Anomaly", "hyperparameter search Kumo-Anomaly", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "kumo-anomaly", "sdk/anomaly_analysis", or "TSDiffuser_Generic".OtherNo warnings★ 3.8K↓ —
- doca-comm-channel-adminNVIDIA/skillsUse this skill to enumerate host↔DPU DOCA comch (formerly Comm Channel) servers and connections via the shipped doca_comm_channel_admin binary — listing comch-capable devices and decoding the per-device server / connection table (server name, PID, in-use / max, PCIe address). The shipped binary is a SINGLE-SHOT SCAN-AND-PRINT tool with no registered arguments — NO list / inspect / drain / restart subcommands; one inventory pass over every comch-capable doca_dev on this side. Channel reset / drain / restart go to doca-comch (program side), doca-setup / doca-hardware-safety (driver reload), or BFB / RShim — NOT to this binary. Trigger on phrasings like "list comch servers", "which channels are active on this BlueField", or "verify admin tool sees same channel as program." Refuse and route elsewhere for the comch programming API, library install, protocol design, channel reset, or general orientation.DesignNo warnings★ 3.8K↓ —
- doca-sha-offload-engineNVIDIA/skillsUse this skill when wiring the DOCA SHA Offload Engine (an OpenSSL ENGINE) into an existing OpenSSL pipeline to offload one-shot SHA-1, SHA-256, or SHA-512 (EVP_Digest) onto DOCA SHA hardware without rewriting against doca-sha. Covers engine load mechanics (`openssl engine dynamic`, `set_pci_addr` ctrl, `-engine_impl`), the SHA-224 negative test that proves offload engaged, the message-size window where offload beats CPU SHA, and engine-vs-library selection. Trigger even when the user does not say "DOCA SHA Offload Engine" or "OpenSSL ENGINE" — typical implicit phrasings: "speed up openssl SHA on BlueField", "offload SHA without code changes", "is openssl using the accelerator or falling back to software", "prove DOCA SHA actually ran", "openssl dgst hashed but I'm not sure it was offloaded". Refuse and route elsewhere for new SHA pipelines (use doca-sha), MD5 / SHA-3 / SHA-224 / HMAC-SHA offload, incremental hashing via chained `EVP_DigestUpdate`, or OpenSSL PROVIDER authoring.TestingNo warnings★ 3.8K↓ —
- designing-experimentsforyourhealth111-pixel/Vibe-SkillsDesign experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.DesignNo warnings★ 3.8K↓ —
- doca-telemetry-exporterNVIDIA/skillsUse this skill when the user is doing hands-on DOCA Telemetry Exporter programming on a host where DOCA is installed — defining a doca_telemetry_exporter_schema and event types, creating sources, picking a publish surface (typed events / opaque events / the metrics counter-gauge-histogram API / OTLP logs / NetFlow), walking the schema-then-source lifecycle, or debugging DOCA_ERROR_* failures from the exporter API. Trigger even when the user does not explicitly mention "DOCA Telemetry Exporter" or "doca_telemetry_exporter_*" — typical implicit phrasings include "publishing counters from my DOCA app", "BAD_STATE when I report an event", "consumer/DTS sees nothing but my report succeeded", "how do I export NetFlow/IPFIX records", or "should I link the exporter or the telemetry service". Refuse and route elsewhere for the receiving DOCA Telemetry Service (DTS), plain stdout logging via doca_log, or real-time event subscription back into the app via doca-comch — those belong to other skills.EngineeringNo warnings★ 3.8K↓ —
- nemo-fabric-integrateNVIDIA/skillsUse this skill when integrating NVIDIA NeMo Fabric into a consumer application, service, evaluation harness, or platform through the typed Python SDK — translating the consumer's own application, job, or deployment config into an in-memory FabricConfig, choosing the single-invocation convenience API or an explicitly started runtime, validating with plan and doctor, and consuming normalized results, artifacts, and telemetry.DevOpsNo warnings★ 3.8K↓ —
- impeccablepbakaus/impeccableUse when the user wants to design, redesign, shape, critique, audit, polish, simplify, harden, animate, or otherwise improve a frontend interface: websites, landing pages, dashboards, product UI, components, forms, settings, onboarding, empty states, and native iOS or Android screens. Handles UX review, hierarchy, layout, typography, color, motion, UX copy, accessibility, responsive behavior, edge cases, and capturing or documenting design systems (DESIGN.md, tokens). Also for making bland UI bolder, loud UI quieter, or ambitious visual effects. Not for backend-only or non-UI tasks.DesignNo warnings★ 100.1K↓ —
- figma-sdk-patternsjeremylongshore/tons-of-skills-marketplaceProduction-ready patterns for the Figma REST API and Plugin API. Use when building reusable Figma client wrappers, extracting design tokens, traversing node trees, or creating typed API helpers. Trigger with phrases like "figma patterns", "figma best practices", "figma client wrapper", "figma typed API".DesignNo warnings★ 2.9K↓ —
- foundationpose-pipelineNVIDIA/skillsAdapt BOP datasets, run the FoundationPose perception pipeline with TAO depth, and evaluate or re-score pose results. Use for dataset runs and result comparisons; environment installation belongs to foundationpose-setup.OtherNo warnings★ 3.8K↓ —
- tao-run-inference-serviceNVIDIA/skillsStart, query, and stop a network-specific TAO inference microservice ({network_arch}-inference-microservice) by delegating container execution to the appropriate platform skill. Handles container image resolution, job-payload JSON construction, and the service registry. Use when the user wants to run inference on a TAO model checkpoint using a microservice container, deploy a TAO inference endpoint, or stop a running inference container.DevOpsNo warnings★ 3.8K↓ —
kiro-spec-batch
gotalab/cc-sdd
PlanningUpdated Oct 22, 2026
From the author
Create complete specs (requirements, design, tasks) for all features in roadmap.md using parallel subagent dispatch by dependency wave.
Install
npx skills add gotalab/cc-sdd --skill kiro-spec-batch -a claude-code
Installs to .claude/skills/kiro-spec-batch/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Download .zip
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
What it can touch
Instructions onlyNo network use seenLicence: MIT
Safety checks on SKILL.md
No warnings.
Why 90
Security100
Provenance83
Maintenance100
Licence100
Adoption57
Files (1)
- SKILL.md