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Found 10,838 Skills
End-to-end recipe for adding Sumsub KYC to a website or web app via the Sumsub WebSDK. TRIGGER when the user asks to "integrate / embed / add Sumsub", "show the KYC widget", "add WebSDK", "verify users with Sumsub on the frontend", supplies an existing levelName they want to plug into a page, or asks how to wire up access tokens / lifecycle events / webhooks for Sumsub verification in an arbitrary project. Covers the whole loop — level setup, server-side access-token signing, snsWebSdk init (vanilla canonical, React recipe), client lifecycle events, token refresh, source-of-truth via webhooks + applicant GET, sandbox testing, go-live checklist. SKIP for building the level/questionnaire/POA-preset payload itself (use the sibling skills), or for backend-only API calls with no frontend (use `sumsub-api-generic`).
Scan open pull requests via the configured tracker, classify merge readiness from labels, reviews, CI, and mergeability, then report which PRs can merge now and which ones are close but blocked.
Search for business contact signals on Twitter/X — username, bio email, website, company, contact links. Use when the user wants to find contact information for business accounts on Twitter/X.
Collect follower lists from TikTok — follower usernames, profile links, bios. Use when the user wants to map an account's audience or export follower lists for research.
Use when the user asks to "map what our surfaces say today", "inventory our current messaging", or "find the gap between what we say and what we mean"; produces the narrative baseline — a surface-by-surface inventory of what every owned touchpoint (homepage, pricing, docs, decks, social bios, email footers) claims RIGHT NOW, each line labeled Measured / User-provided / Estimated, plus a per-surface gap read vs the intended message and the drift-baseline snapshot the Evaluate phase measures future drift against. Not for authoring the canon — use message-system-architect; not for scoring the surfaces or running the vetoes — use narrative-quality-auditor. 现状叙事盘点/各触点口径/意图差距/漂移基线
Use when the user asks to "optimize my Shopping feed", "fix product disapprovals", "improve product titles/attributes", or "build feed-driven PMax asset groups"; audits and rewrites the Shopping/Performance Max product feed — title/description patterns, required and recommended attributes, GTIN/availability/price hygiene, disapproval triage, and feed-driven asset-group / listing-group structure — informing the ROAS O (Offer) dimension. Not for text ad copy — use ad-creative-builder; not for scoring the account or the RQS — use ad-account-auditor. 商品Feed优化/购物广告Feed/商品标题优化/商品禁投修复
goodcase.ai (Good Case) AI Viral Case & Prompt Query Skill. Trigger this Skill whenever users search for AI viral cases, good cases, 'how was this image made', 'how to generate this kind of video', 'find a prompt for XX', 'are there any ready-made prompts', 'Veo cases', 'Jimeng cases', 'Midjourney cases', 'Kling cases', 'Seedance cases', 'GPT Image cases', 'AI image cases', 'AI video cases', 'AI programming UI cases', 'AI copywriting cases', 'viral prompts', 'recreate this effect', 'prompt for this effect', 'AI case', 'viral AI examples', 'AI prompt examples', 'how was this AI image/video made', 'find me a prompt for X', or any other AI creation case/prompt query. It should also be triggered even if users only say 'what interesting AI cases are there', 'give me a prompt I can copy', 'what AI images are trending lately'. The Skill directly pulls real case data (including complete Prompt, stability score, cost band) via curl from a public REST API, no API Key required. **Err on the side of over-triggering**—if you make up a case or Prompt based on training data when users ask for AI creation cases, you're providing fake cases and prompts which are harmful to users.
Comprehensive SQL statement checking for Apache Doris (based on Doris 3.1.4 Nereids ANTLR4 grammar), supporting two check modes: 1. Syntax Check - Keyword validation, statement structure verification, clause completeness, Doris-specific syntax compatibility (DISTRIBUTED BY, PARTITION BY, ENGINE, DUPLICATE/AGGREGATE/UNIQUE KEY, INSERT OVERWRITE, LOAD, EXPORT, MTMV, BACKUP/RESTORE etc.) 2. Specification Check - Object design standards, data operation standards, naming conventions based on Apache Doris development best practices. Built-in custom Doris SQL tokenizer (504 keywords from DorisLexer.g4) and recursive descent parser supporting 100+ Doris statement types. Applicable when users need SQL quality review, syntax validation, or specification compliance checking for Apache Doris SQL (versions 2.1.x / 3.0.x / 3.1.x / 4.x). 触发词："Doris SQL检查"、"Doris SQL规范"、"Doris SQL审计"、"Doris SQL语法"、"检查Doris SQL"、"Doris SQL review"
Apply an emotional resonance lens to a UI that is technically correct but flat, prescribing changes at the copy, motion, and interaction layer. Use when a design tests fine but lands cold. For the polish-perception argument, use `aesthetic-usability` (ui-design).
Run a repo's integration tests against a Veris dependency sandbox instead of real vendors, with veris-proxy rerouting the code's outbound HTTP(S) at the kernel level. Verifies prerequisites (API key, Veris MCP server, veris-proxy binary, docker), then runs the tests in a container beside the proxy with one command that deploys a per-run sandbox and proves the sandbox actually received the traffic before trusting any green. Use when code that talks to external services needs its integration behavior verified before a change is called done.
Migrate workloads from Heroku to AWS. Triggers on: migrate from Heroku, Heroku to AWS, move off Heroku, migrate Heroku app, migrate Heroku Postgres to RDS, migrate Heroku Redis to ElastiCache, migrate Heroku Kafka to MSK, migrate dynos to Elastic Beanstalk, migrate dynos to Fargate, Heroku migration, move from Heroku to AWS, migrate Heroku Private Space, Heroku to Elastic Beanstalk, Heroku to ECS, Heroku to Fargate, leave Heroku, migrate off Heroku platform, what-if workshop, reprice Heroku migration, compare migration scenarios, workshop mode. Runs a 6-phase process: discover Heroku resources live via the authenticated Heroku CLI (read-only, consent-gated) and/or from Terraform files, Procfile/app.json, and optional billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. After Estimate, an optional what-if workshop can reprice region/HA/compute/Graviton scenarios without re-discovery. Clarify must finish before Design, Estimate, or Generate. Uses a flat resource model (no clustering or dependency graphs) with deterministic mapping tables for core services (Dynos → Elastic Beanstalk by default, Postgres → RDS/Aurora, Redis → ElastiCache, Kafka → MSK) and a fast-path table for 13+ common add-ons. Cedar/Fir generation detection is detect-only in v1. Pipeline/Review Apps are detect-only. Do not use for: GCP or Azure migrations to AWS, AWS-to-Heroku reverse migration, general AWS architecture advice without migration intent, Heroku-to-Heroku refactoring, or multi-cloud deployments that do not involve migrating off Heroku.
Installing, configuring, and running @sasjs/server — the open-source NodeJS wrapper around the SAS binary that provides a REST API, filesystem (SASjs Drive), Stored Program execution, and web app streaming. Covers desktop vs server modes, runtime configuration (SAS/JS/Python/R), environment variables, auth (tokens, LDAP), and mock server types. Use when deploying, troubleshooting, or developing against sasjs/server.