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Agent Skills 排行榜 · 关键词 + 语义搜索
| # | Skill | 仓库 | 描述 | 安装量 |
|---|---|---|---|---|
| 6501 | ui-skills-root | ibelick/ui-skills |
UI Skills Root You are the routing layer for UI Skills. This skill is shown by npx ui-skills start and is also available in the registry. Use it when an agent in Codex, Cursor, or Claude Code has a clear UI goal. If the goal is unclear, ask one short question. If the goal is clear, choose the right category, load the smallest useful skill context, then implement. Protocol Show more Installs 562 Repository ibelick/ui-skills GitHub Stars 3.4K First Seen 12 days ago Security Audits Gen Agent Trust ...
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| 6502 | tao-list-capabilities | nvidia/skills |
TAO Skill Bank Capabilities Use this skill when the user asks what tao-skill-bank can do, asks for plugin capabilities, asks which application or data workflows are available, asks which models are supported, or asks what models are capable with AutoML. Quick Start Run scripts/list_tao_capabilities.py for general capability questions, or scripts/list_tao_models.py for model/action and AutoML support questions. Capability Answers For a general capabilities answer, run the packaged helper: ${TAO_S...
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| 6503 | tao-run-automl | nvidia/skills |
TAO AutoML Run automated hyperparameter optimization for a TAO model by combining: The selected model skill under skills/models/<network>/ . The selected platform skill under skills/platform/<platform>/ . AutoMLRunner , which generates recommendations, launches train jobs, extracts metrics, and feeds results back to the optimizer. Do not launch until model metadata, platform preflight, data visibility, credentials, image choice, and compute shape are all proven. Reference Map Show more Installs ...
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| 6504 | tao-convert-dataset-format | nvidia/skills |
Convert a TAO DAFT Dataset Quick start tao-daft convert < source-format > < target-format > --path < input > --output < output > Source and target are positional subcommands; --path and --output are flags. Discover the supported formats and per-pair flags from the leaf --help (see "CLI conventions" below). Show more Installs 579 Repository nvidia/skills GitHub Stars 1.9K First Seen Jun 8, 2026 Security Audits Gen Agent Trust Hub Pass Socket Pass Snyk Pass
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| 6505 | tao-finetune-clip | nvidia/skills |
CLIP Contrastive Language-Image Pre-training model for zero-shot and fine-tuned image classification, image-text retrieval, and embedding extraction. Fine-tuning adapts CLIP's shared image-text embedding space to domain-specific image-caption data. No default NGC pretrained checkpoint is required for spec construction, but unset checkpoint behavior is action-specific. In the validation-fixes PyTorch image, export.checkpoint: null exports the selected CLIP architecture and may initialize weights ...
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| 6506 | tao-finetune-cosmos-reason | nvidia/skills |
Cosmos-RL Supervised fine-tuning (SFT) of Cosmos Reason video QA models. The packaged default base model is hf_model://nvidia/Cosmos3-Nano . Pretrained weights are sourced from HuggingFace, not NGC. Gated HuggingFace models require HF_TOKEN . Some Cosmos-RL images cannot load the native Cosmos3 Omni checkpoint format directly; for those images, convert Cosmos3-Nano to a Qwen3-VL HF safetensors directory before train/evaluate and use that converted directory as the PTM path. Uses FSDP-based paral...
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| 6507 | tao-run-inference-service | nvidia/skills |
TAO Inference Microservice Instructions To start an inference service: Collect required inputs (Section 1) and resolve the container image (Section 2). Build the job payload and inner command (Sections 3–4.1); use references/code-templates.yaml → job_payload_builder . Read skills/platform/<platform>/SKILL.md and start the container (Section 4.2). Write the service registry and poll readiness (Section 4.3); use references/code-templates.yaml → registry_write.<platform> and readiness_check . To se...
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| 6508 | tao-run-on-kubernetes | nvidia/skills |
Kubernetes Submits TAO container jobs as Kubernetes Jobs. Works on any cluster reachable via kubeconfig (EKS / GKE / AKS / on-prem) or in-cluster service account (when the SDK runs inside a pod). Single-pod by default; opt into multi-node distributed training via num_nodes > 1 (uses Indexed Job + headless Service, see Multi-node training below). Preflight Four checks: GPU host runtime ready, SDK installed, cluster reachable, GPU Operator/device plugin present. 0. GPU node host runtime. Run thi...
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| 6509 | tao-generate-video-reasoning-annotations | nvidia/skills |
Video Reasoning Annotation Pipeline Generate Chain-of-Thought training datasets from videos by producing multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with step-by-step reasoning traces. Domain-agnostic by default — customize prompts for any video domain. Purpose Transform raw videos into CoT Q&A training data for video understanding models. VLMs (e.g., Gemini, Qwen) act as "teacher" annotators: Steps 0–1 require the model to see the video (VLM calls); Ste...
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| 6510 | retail-etl-medallion-pipeline | aradotso/data-skills |
Retail ETL Medallion Pipeline Skill Skill by ara.so — Data Skills collection. Overview This project implements a production-grade Medallion Architecture ETL pipeline for retail/hypermarket data, handling complex business logic like inventory shrinkage, meat/poultry recipe conversions, supplier rebate tiers, and multi-branch sales consolidation. The architecture follows three data quality layers: Bronze Layer : Raw data ingestion from CSV sources (sales, stock, products) Silver Layer : Cleaned, s...
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| 6511 | retail-etl-pipeline-medallion | aradotso/data-skills |
Retail ETL Pipeline - Medallion Architecture Skill Skill by ara.so — Data Skills collection Overview The Retail ETL Pipeline project implements a complete data engineering solution for retail operations using the Medallion Architecture pattern (Bronze → Silver → Gold layers). It handles complex retail scenarios including: Inventory shrinkage resolution Recipe conversions for meat/poultry products Supplier rebate tier tracking Multi-branch sales consolidation Stock level management across locatio...
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| 6512 | dx-code-analyzer-run | forcedotcom/sf-skills |
Running Code Analyzer Skill CRITICAL: Mandatory Script Usage Every interaction with Code Analyzer results MUST go through the bundled scripts in <skill_dir>/scripts/ . No exceptions. WRONG — never do this: WRONG: inline Python to parse results python3 -c "import json; data = json.load(open('results.json'))..." WRONG: inline Node.js to parse results node -e "const data = require('./results.json')..." WRONG: jq to filter results cat results.json | jq '.violations[] | select(.engine=="pmd")' Sho...
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| 6513 | design-systems-slds-validate | forcedotcom/sf-skills |
SLDS Quality Audit Audit Lightning Web Components for SLDS compliance and produce an automated scorecard plus a required manual review gate. Combines SLDS linter output with supplementary static analysis to catch what the linter misses. Scope Also valid for: auditing SLDS compliance across a project or component set, and before/after quality comparison after making changes. Not for: Fixing linter violations — use design-systems-slds2-migrate instead Building new components — use design-systems-s...
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| 6514 | design-systems-slds-apply | forcedotcom/sf-skills |
Applying SLDS The Salesforce Lightning Design System (SLDS) is a CSS framework with thousands of artifacts. This skill teaches agents how to find and correctly use them. Version: This skill targets SLDS v2 . Legacy --lwc-* tokens and slds-*--modifier syntax are deprecated. Audit scope: The companion design-systems-slds-validate skill analyzer only scans .css , .html , and .js files. Use it directly for LWC and similar HTML/CSS/JS components; treat it as a partial signal for JSX/TSX or other fram...
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| 6515 | prisma-compute | prisma/skills |
Prisma Compute Guide agents through Prisma Compute app creation, deployment, operations, and framework-specific deploy readiness. Critical: Verify the Current Surface Prisma Compute is actively moving into the Prisma Platform CLI. Before giving commands or editing a project, verify the local/current command surface: bunx @prisma/cli@latest app deploy --help bunx @prisma/cli@latest app --help bunx create-prisma@latest --help Use @prisma/cli@latest for Compute app deployment unless current help ou...
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| 6516 | scientific-slides | davila7/claude-code-templates |
Scientific Slides Overview Scientific presentations are a critical medium for communicating research, sharing findings, and engaging with academic and professional audiences. This skill provides comprehensive guidance for creating effective scientific presentations, from structure and content development to visual design and delivery preparation. Key Focus: Oral presentations for conferences, seminars, defenses, and professional talks. CRITICAL DESIGN PHILOSOPHY: Scientific presentations shou...
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| 6517 | status | boshu2/agentops |
Check Research Status Run ID: $ARGUMENTS parallel-cli research status " $ARGUMENTS " --json If CLI not found, tell user to run /parallel:setup .
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| 6518 | prompt-images | replicate/skills |
Prompting image models on Replicate Distilled from Replicate's blog posts on prompting image models (2024-2026). Techniques are model-agnostic and focus on transferable principles. Choose a model with the API, not from memory This skill describes general prompting techniques. To choose a model, use the find-models skill and query the Replicate API. The image model landscape changes weekly. Don't assume specific models exist or are still state-of-the-art based on names you've seen before. Always ...
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| 6519 | korean-cinema-search | nomadamas/k-skill |
Korean Cinema Search What this skill does upstream 원본 hmmhmmhm/daiso-mcp 와 npm package daiso 를 사용해 CGV, 메가박스, 롯데시네마 영화관 검색, 상영작, 시간표, 잔여석 조회 를 안내한다. 이 저장소는 upstream 코드를 vendoring 하지 않는다. 기본 경로는 MCP 서버를 직접 설치하지 않고 CLI로 먼저 확인하는 방식 이다. 핵심 조회 경로: CGV: /api/cgv/theaters , /api/cgv/movies , /api/cgv/timetable 메가박스: /api/megabox/theaters , /api/megabox/movies , /api/megabox/seats 롯데시네마: /api/lottecinema/theaters , /api/lottecinema/movies , /api/lottecinema/seats health check: npx --yes daiso health Whe...
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| 6520 | emergency-room-beds | nomadamas/k-skill |
Emergency Room Beds What this skill does 사용자가 알려준 현재 위치를 기준으로 근처 응급실 과 공개 E-Gen 응급실 상태 플래그를 찾는다. 위치는 자동 추정하지 않는다. 위치가 없으면 먼저 현재 위치를 묻는다. 위치 문자열은 Kakao Map anchor 검색으로 좌표를 잡는다. 응급실 목록은 E-Gen 공개 응급실 찾기 표면을 사용한다. 응급실 운영 여부, 입원실/병상 운영 플래그, 권역외상센터/소아전문 여부, 데이터 갱신시각을 보여준다. 정확한 실시간 잔여 병상 수나 병상 가동률을 확정해서 말하지 않는다. 공개 E-Gen nearby 목록은 병상 수치가 아니라 운영 플래그를 제공한다. When to use "근처 응급실 찾아줘" "응급실 병상 상태 확인해줘" "광화문 주변 응급실 어디가 가까워?" "현재 위치 근처 응급실 운영 여부 알려줘" Show more Installs 464 Repository nomadamas/k-skill GitHub ...
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| 6521 | ohou-today-deal | nomadamas/k-skill |
오늘의집 오늘의딜 조회 What this skill does 오늘의집 공개 오늘의딜 페이지( https://ohou.se/commerces/today_deals )의 서버 렌더링 초기 데이터( __NEXT_DATA__ )를 읽어 특가 상품을 조회한다. 오늘의딜/스페셜딜 상품 목록 조회 할인율, 원가, 판매가, 쿠폰/결제혜택 반영 최저가 정리 브랜드, 리뷰 수, 평점, 무료배송 여부, 상품 링크 확인 키워드, 최소 할인율, 무료배송 필터 When to use "오늘의집 오늘의딜 뭐 있어?" "오늘의집에서 할인율 높은 특가 상품 3개 보여줘" "오늘의집 무료배송 특가만 골라줘" "오늘의집에서 러그 특가 찾아줘" When not to use Show more Installs 464 Repository nomadamas/k-skill GitHub Stars 5.4K First Seen May 19, 2026
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| 6522 | tao-analyze-changenet-rca | nvidia/skills |
TAO ChangeNet Classification RCA Skill You are an expert investigator for NVIDIA TAO Visual ChangeNet classification experiments. Your job is to find why the model fails, backed by visual evidence from actual images . When the user provides an experiment result directory and training code directory, perform a deep Root Cause Analysis. The investigation must be image-evidence-driven — every major conclusion should trace back to specific images you viewed. Inputs Experiment result directory — cont...
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| 6523 | tao-analyze-gaps-visual-changenet | nvidia/skills |
TAO VCN Classify Gap Analysis Skill You are an analyst for NVIDIA TAO VCN Classify (Visual Component Net) inference results. Your job is to identify the weakest samples per ground-truth label by measuring signed distance from the decision threshold in the wrong direction , then surface them for downstream augmentation or relabeling. This skill is intentionally lightweight. VCN's classify head is a single-score binary boundary (PASS vs NO_PASS by siamese_score ), so the analysis is computational,...
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| 6524 | tao-train-image-classification | nvidia/skills |
Classification PyT PyTorch image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.) with distillation and quantization for deployment. Set model.backbone.pretrained_backbone_path for backbone weights or train.pretrained_model_path for full model. For TAO Deploy TensorRT actions ( gen_trt_engine , TensorRT evaluate , and TensorRT inference ), read references/tao-deploy-image-classification.md first. Deploy spec templates live in this skill's references/ folder wi...
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| 6525 | tao-run-automl-deft-pipeline | nvidia/skills |
AutoML + DEFT Pipeline A workflow-bridge skill that runs three phases in sequence by delegating to two existing skills — tao-run-automl for HPO and a DEFT application skill (default tao-run-deft-aoi for AOI; other skills/applications/deft-* skills for non-AOI cases) for the iterative data-improvement loop. This skill does not re-implement AutoML or DEFT. It owns only the connective tissue: HPO spec inputs, the spec-handoff between AutoML and DEFT, and the post-DEFT AutoML re-run on the augmented...
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| 6526 | tao-run-deft-aoi | nvidia/skills |
Skill: tao-run-deft-aoi When to Use This Skill Use this skill when the user wants an agent to run the full DEFT AOI improvement loop for an NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection model: baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until a KPI target is met. "Run the DEFT loop" "Fine-tune until FAR below 0.1% at recall=100%" "Improve my AOI ChangeNet model using RCA and synthetic defects" "Iterate training until false accept ...
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| 6527 | tao-generate-image-grounding | nvidia/skills |
Image Grounding Pipeline Turn (image, caption) pairs into per-image grounded annotations: cleaned captions, referring expressions with character spans, and pixel-space bounding boxes for each expression. A single VLM (Gemini or any OpenAI-compatible endpoint) handles both steps. Purpose Generate phrase-grounded training data for referring-expression and grounding models. The VLM acts as a "teacher" annotator: Step 0 extracts referring expressions from the caption while looking at the image; Step...
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| 6528 | harvard-art-museums-etl-pipeline | aradotso/data-skills |
Harvard Art Museums ETL Pipeline Skill by ara.so — Data Skills collection. This skill enables AI agents to help developers build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates real-world ETL pipelines, SQL database design, analytical queries, and interactive Streamlit dashboards for artifact collection data. What This Project Does The Harvard Artifacts Collection Data Engineering & Analytics App provides: API Integration : Fetch...
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| 6529 | readout | warpdotdev/common-skills |
Readout A readout turns an investigation into a durable HTML document someone can read weeks later without any of the original context. It starts one of two ways: Snapshot mode — invoked mid-conversation ("write this up"): the conversation's accumulated findings are the source material. Research mode — invoked fresh ("/readout on how github webhook events are processed in the server"): there is no conversation to mine, so the investigation itself is part of the job. Either way, invoking this ski...
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| 6530 | expo-router | expo/skills |
Expo Router Navigation Navigation and routing for Expo Router apps. For screen styling, colors, controls, animations, media, and visual effects, use the expo-native-ui skill. References Consult these resources as needed: references/ route-structure.md Route conventions, dynamic routes, groups, folder organization tabs.md NativeTabs, migration from JS tabs, iOS 26 features toolbar-and-headers.md Stack headers and toolbar buttons, menus, search (iOS only) form-sheet.md ...
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| 6531 | parallel-task-spark | am-will/codex-skills |
Parallel Task Executor (Sparky) You are an Orchestrator for subagents. Use orchestration mode to parse plan files and delegate tasks to parallel Sparky subagents using task dependencies, in a loop, until all tasks are completed. Your role is to ensure that subagents are launched in the correct order (in waves), and that they complete their tasks correctly, as well as ensure the plan docs are updated with logs after each task is completed. Process Step 1: Parse Request Extract from user request: ...
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| 6532 | super-swarm-spark | am-will/codex-skills |
Parallel Task Executor (Sparky Rolling 12-Agent Pool) You are an Orchestrator for subagents. Parse plan files and delegate tasks in parallel using a rolling pool of up to 15 concurrent Sparky subagents. Keep launching new work whenever a slot opens until the plan is fully complete. Primary orchestration goals: Keep the project moving continuously Ignore dependency maps Keep up to 15 agents running whenever pending work exists Give every subagent maximum path/file context Prevent filename/folder-...
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| 6533 | agent-memory | api/git |
No SKILL.md available for this skill. View on GitHub
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| 6534 | flutter-animation | flutter/skills |
Flutter Animations Implementation Goal Implements and manages Flutter animations, selecting the appropriate animation strategy (implicit, explicit, tween, physics, hero, or staggered) based on UI requirements. Assumes a working Flutter environment, stateful/stateless widget competence, and a standard widget tree structure. Instructions 1. Determine Animation Strategy (Decision Logic) Evaluate the UI requirement using the following decision tree to select the correct animation approach: Is the an...
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| 6535 | flutter-routing-and-navigation | flutter/skills |
flutter-navigation-routing Goal Implements robust navigation and routing in Flutter applications. Evaluates application requirements to select the appropriate routing strategy (imperative Navigator , declarative Router , or nested navigation), handles deep linking, and manages data passing between routes while adhering to Flutter best practices. Instructions 1. Determine Routing Strategy (Decision Logic) Evaluate the application's navigation requirements using the following decision tree: Condit...
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| 6536 | flutter-state-management | flutter/skills |
flutter-state-management Goal Implements robust state management and architectural patterns in Flutter applications using Unidirectional Data Flow (UDF) and the Model-View-ViewModel (MVVM) design pattern. Evaluates state complexity to differentiate between ephemeral (local) state and app (shared) state, applying the appropriate mechanisms ( setState , ChangeNotifier , or the provider package). Ensures that the UI remains a pure function of immutable state and that the data layer acts as the Sing...
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| 6537 | flutter-architecture | flutter/skills |
Flutter Architecture Overview Provides architectural guidance and best practices for building scalable Flutter applications using MVVM pattern, layered architecture, and recommended design patterns from the Flutter team. Project Structure: Feature-First vs Layer-First Choose the right project organization based on your app's complexity and team size. Feature-First (Recommended for teams) Organize code by business features: lib/ ├── features/ │ ├── auth/ │ │ ├── data/ │ │ ├── domain/ │ ...
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| 6538 | setup-artist | recoupable/setup-artist |
Setup Artist Scaffold a complete artist workspace so agents can start working immediately. Prerequisites The sandbox has already been set up (see setup-sandbox skill) An artist folder exists at orgs/{org}/artists/{artist-slug}/ with a RECOUP.md marker file The RECOUP.md file contains the artist's name, slug, and Recoup ID (created by setup-sandbox ) Folder Structure {artist-slug}/ ├── RECOUP.md ├── README.md ├── .env.example ├── .env ├── context/ │ ├── artist.md │ ├── audience.md │ ├── era...
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| 6539 | quant-analyst | 404kidwiz/claude-supercode-skills |
Quantitative Analyst Purpose Provides expertise in quantitative finance, algorithmic trading strategies, and financial data analysis. Specializes in statistical modeling, risk analytics, and building data-driven trading systems using Python scientific computing stack. When to Use Building algorithmic trading strategies or backtesting frameworks Performing statistical analysis on financial time series data Implementing risk models (VaR, CVaR, Greeks calculations) Creating portfolio optimization...
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| 6540 | video-understand | heygen-com/skills |
video-understand Understand video content locally using ffmpeg for frame extraction and Whisper for transcription. Fully offline, no API keys required. Prerequisites ffmpeg + ffprobe (required): brew install ffmpeg openai-whisper (optional, for transcription): pip install openai-whisper Commands Scene detection + transcribe (default) python3 skills/video-understand/scripts/understand_video.py video.mp4 Keyframe extraction python3 skills/video-understand/scripts/understand_video.py video.mp4 -m...
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| 6541 | sub-account | binance/binance-skills-hub |
Binance Sub-account Skill Sub-account request on Binance using authenticated API endpoints. Requires API key and secret key for certain endpoints. Return the result in JSON format. Quick Reference Endpoint Description Required Optional Authentication /sapi/v1/sub-account/virtualSubAccount (POST) Create a Virtual Sub-account (For Master Account) (USER_DATA) subAccountString recvWindow Yes /sapi/v1/sub-account/futures/enable (POST) Enable Futures for Sub-account (For Master Account) (USER_DATA) em...
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| 6542 | simple-earn | binance/binance-skills-hub |
Binance Simple-earn Skill Simple-earn request on Binance using authenticated API endpoints. Requires API key and secret key for certain endpoints. Return the result in JSON format. Quick Reference Endpoint Description Required Optional Authentication /sapi/v1/bfusd/account (GET) Get BFUSD Account (USER_DATA) None recvWindow Yes /sapi/v1/bfusd/quota (GET) Get BFUSD Quota Details (USER_DATA) None recvWindow Yes /sapi/v1/bfusd/redeem (POST) Redeem BFUSD(TRADE) amount, type recvWindow Yes /sapi/v1/b...
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| 6543 | ios-security | dpearson2699/swift-ios-skills |
iOS Security Guidance for handling sensitive data, authenticating users, encrypting correctly, and following Apple's security best practices on iOS. Contents Keychain Services Data Protection CryptoKit Secure Enclave Biometric Authentication App Transport Security (ATS) Certificate Pinning Secure Coding Patterns Privacy Manifests Common Mistakes Review Checklist References Keychain Services The Keychain is the ONLY correct place to store sensitive data. Never store passwords, tokens, API keys, o...
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| 6544 | develop-web-game | openai/skills |
Develop Web Game Build games in small steps and validate every change. Treat each iteration as: implement → act → pause → observe → adjust. Skill paths (set once) export CODEX_HOME = " ${CODEX_HOME :- $HOME / .codex} " export WEB_GAME_CLIENT = " $CODEX_HOME /skills/develop-web-game/scripts/web_game_playwright_client.js" export WEB_GAME_ACTIONS = " $CODEX_HOME /skills/develop-web-game/references/action_payloads.json" User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills )....
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| 6545 | file-test-bug | microsoft/github-copilot-for-azure |
File Test Bug Creates a GitHub issue in microsoft/github-copilot-for-azure for integration test failures. Input Skill name (required): e.g., azure-rbac , appinsights-instrumentation Test run (optional): Timestamp of test run. Defaults to most recent with logs for the skill. Steps Ask user for skill name if not provided Parse tests/reports/junit.xml for failures matching the skill Find test run directory (specified or most recent with matching logs) Read agent-metadata.md from tests/reports/test-...
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| 6546 | bluebubbles | steipete/clawdis |
BlueBubbles Actions Overview BlueBubbles is OpenClaw’s recommended iMessage integration. Use the message tool with channel: "bluebubbles" to send messages and manage iMessage conversations: send texts and attachments, react (tapbacks), edit/unsend, reply in threads, and manage group participants/names/icons. Inputs to collect target (prefer chat_guid:... ; also +15551234567 in E.164 or user@example.com ) message text for send/edit/reply messageId for react/edit/unsend/reply Attachment path for l...
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| 6547 | biome-js | paulrberg/agent-skills |
BiomeJS Skill Quick guidance for BiomeJS configuration based on Sablier project patterns. Core Concepts Extending Shared Configs Extend shared configs via npm package exports. The consuming project must always provide its own files.includes : { "$schema": "./node_modules/@biomejs/biome/configuration_schema.json", "extends": ["@sablier/devkit/biome"], "files": { "includes": ["/*.{js,json,jsonc,ts}", "!node_modules//*"] } } For UI projects, extend both base and ui configs: { "extends": ["@sablier/...
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| 6548 | railway-docs | railwayapp/railway-skills |
Railway Docs Fetch up-to-date Railway documentation to answer questions accurately. When to Use User asks how something works on Railway (projects, deployments, volumes, etc.) User shares a docs.railway.com URL User needs current info about Railway features or pricing Before answering Railway questions from memory - check the docs first LLM-Optimized Sources Start here for comprehensive, up-to-date info: Source URL Full docs https://docs.railway.com/api/llms-docs.md llms.txt index https://railwa...
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| 6549 | web-search-tavily | jwynia/agent-skills |
Web Search (Tavily API) Search the web using Tavily's AI-optimized search API. Returns high-quality, structured results with relevance scores and optional AI-generated summaries. Note: This skill requires a Tavily API key. For basic web search using the agent's built-in capability, see web-search. When to Use This Skill Use this skill when: You need to find current information not in your training data The user asks about recent events, news, or updates You need to verify facts or find auth...
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| 6550 | authoring-skills | vercel/next.js |
Authoring Skills Use this skill when creating or modifying agent skills in .agents/skills/ . When to Create a Skill Create a skill when content is: Too detailed for AGENTS.md (code templates, multi-step workflows, diagnostic procedures) Only relevant for specific tasks (not needed every session) Self-contained enough to load independently Keep in AGENTS.md instead when: It's a one-liner rule or guardrail every session needs It's a general-purpose gotcha any agent could hit File Structure .agents...
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