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Found 1,680 Skills
Apply when scoping, reviewing, or documenting cross-cutting VTEX commerce architecture across storefront, IO, headless, marketplace, payments, or any other VTEX module. Grounds work in the Well-Architected Commerce framework—Technical Foundation (reliability, trust, integrity; security, infrastructure, compliance), Future-proof (innovation, simplicity, efficiency; scalable and adaptable solutions), and Operational Excellence (accuracy, accountability, data-driven improvement; process and customer experience). Routes implementation detail to product tracks (IO caching and paths, Master Data strategy, marketplace integrations). Use for solution design, architecture reviews, and RFP-level technical structure.
Account-based B2B advertising — display ads, retargeting, cross-channel campaigns targeting specific accounts or segments. Use when running ABM ad campaigns, targeting accounts with display ads, retargeting website visitors, building B2B audiences, measuring ad-to-pipeline attribution, or choosing a B2B advertising platform. Do NOT use for email outbound (use /sales-cadence), general paid media/B2C ads (out of scope), or ZoomInfo-specific config (use /sales-zoominfo). For platform-specific help, use /sales-zoominfo.
When the user wants help with Google Ads attribution models, data-driven attribution, attribution windows, cross-channel attribution, how attribution affects Smart Bidding, multi-touch conversion paths, or understanding which campaigns are actually driving conversions. Triggers on 'attribution', 'attribution model', 'data-driven attribution', 'last click attribution', 'assisted conversions', 'conversion window', 'multi-touch', 'attribution model comparison', 'first click attribution', 'credit allocation', or 'which campaign is driving conversions'. For conversion tracking setup see google-ads-conversion-tracking. For Smart Bidding strategy see google-ads-bidding.
Build a pre-implementation harness for ambiguous or risky coding tasks by grounding the request in the repository, producing a structured impact map, surfacing ambiguities and risks, defining scope boundaries, and creating a validation-ready implementation contract before any code changes are made. Use when a task is broad, underspecified, cross-cutting, or likely to drift without an explicit planning checkpoint.
Search LCSC Electronics for electronic components — find parts by LCSC number (Cxxxxx) or MPN, check stock/pricing, download datasheets, analyze specifications. Sister company to JLCPCB, same parts library. Sync and maintain a local datasheets directory for a KiCad project, or use batch MPN-list seeding (`--mpn-list`) for bulk workflows without a project. No API key needed — uses the free jlcsearch community API. Use this skill when the user mentions LCSC, JLCPCB parts library, JLCPCB assembly parts, production sourcing, Cxxxxx part numbers, needs to find LCSC equivalents for parts, is preparing a BOM for JLCPCB assembly, or wants to download datasheets and LCSC is available. For package cross-reference tables and BOM workflow, see the `bom` skill.
Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says "审查论文数据", "check paper claims", "verify numbers", "论文数字核对", or before submission to ensure paper-to-evidence fidelity.
Follow this sub-process for code optimization — handle tasks where 'behavior remains unchanged but structure changes' (structure / performance / readability). Shift single-module internal optimization from 'AI random refactoring' to 'first scan to generate a checklist, confirm each item with the user, execute step by step according to the method library, and obtain manual approval for each step'. Trigger scenarios: When the user mentions phrases like 'optimize / refactor / rewrite / split / poor performance / too long code' without any accompanying behavior changes. Do not handle new requirements (route to feature), bugs (route to issue), or cross-module architecture restructuring (route to architecture + decisions).
BrandJet AI platform help — multi-channel outreach sequences, unified inbox, brand monitoring, AI visibility tracking, lead discovery, social listening, email warmup, Artemis AI agent, and integrations. Use when outreach sequences aren't getting replies, brand mentions going unnoticed, multi-channel sequences feel disjointed, unified inbox is overwhelming, or AI visibility scores are dropping. Do NOT use for designing cadence strategy (use /sales-cadence), cross-platform deliverability (use /sales-deliverability), social listening strategy (use /sales-social-listening), or enriching contacts (use /sales-enrich).
Orchestrate parallel implementation with coder/overseer pairs. Coders implement decomposed tasks using evanflow-tdd; overseers review each coder's output for bugs, gaps, errors, AND cohesion violations against a shared contract. A final integration overseer checks cross-coder cohesion. Use for plans with 3+ truly independent tasks that share an interface contract.
Resize, crop, or export any image or video into platform-ready social media assets using Adobe Creative Cloud tools. Use this skill when a user wants to prepare a photo, image, or video for one or more social platforms — Instagram, TikTok, LinkedIn, Facebook, YouTube, Snapchat, Pinterest, Threads, or X/Twitter. Triggers on: "prepare my image for Instagram", "resize for TikTok", "get this ready to post", "make versions for all platforms", "social media sizes", "crop for stories", "export for LinkedIn", "resize my video for social", "make social media assets", or any request to adapt a photo or video for specific platforms. Handles subject-aware cropping, AI canvas expansion, test previews before full runs, and same-ratio video resizing.
Issue triage: audit open issues, categorize, detect duplicates, cross-ref PRs, risk assessment, post comments. Args: "all" for deep analysis of all, issue numbers to focus (e.g. "42 57"), "en"/"fr" for language, no arg = audit only in French.
Audit a python-pptx export against its source HTML deck, identify layout/content drift (footer overflow, cropped content, missing italic/em, lost styling, off-rhythm spacing), and re-export with strict footer-rail + cursor-flow layout discipline. Use this skill whenever the user has a .pptx that was generated from an HTML slide deck and asks to compare/audit/verify/fix the export — including phrases like "compare ppt with html", "fidelity audit", "fix the pptx", "ppt is cut off", "footer overlap", "italic missing in pptx", "re-export the deck", "pptx-html-fidelity-audit", or any case where a python-pptx → HTML round-trip needs verification or repair. Also trigger when the user shows you a deck.html and a deck.pptx side by side and is debugging visual differences.