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Found 5,074 Skills
Datumbox integration. Manage Organizations, Users, Goals, Filters. Use when the user wants to interact with Datumbox data.
Use when facing complex decisions, architectural trade-offs, philosophical questions, or any problem requiring deep analysis before action. Use when the user asks to "think deeply", "question assumptions", "analyze from first principles", "challenge this decision", debates between two approaches (e.g. monolith vs microservices, build vs buy, SSR vs CSR), or invokes /socrates. Also triggered when other skills need a thinking engine for rigorous pre-analysis. Even if the problem seems simple, if there are hidden assumptions worth examining, this skill applies.
Set up and connect a Shopify store from your AI assistant. Use when the user wants to: set up my Shopify store, connect my store, install Shopify plugin, get started with Shopify, manage my store, add products to my store, merchant onboarding, start selling online, Shopify setup help, create my first store, how do I set up an online store, shopify.com/SKILL.md, import products, migrate from Square, migrate from WooCommerce, migrate from Etsy, migrate from Amazon, migrate from eBay, migrate from Wix, import from Google Merchant Center, migrate from Clover, migrate from Lightspeed, move products to Shopify, import catalog, replatform to Shopify. This is for store owners — not developers.
Searches for and retrieves existing visual media (images, logos, icons, photos, graphics, banners, thumbnails, hero images, backgrounds) from sources such as Salesforce CMS, Data 360 or any other source. Use this skill ANY TIME a user request involves finding, searching, getting, fetching, retrieving, grab, looking up, locating media. NEVER call search_media_cms_channels, search_electronic_media tools directly — always go through this skill first. This skill must be activated before any tool is used for media search or retrieval, without exception. Takes PRIORITY and activates FIRST when ANY media search/retrieval is mentioned, regardless of what else happens with the media afterward. Triggers for requests like "search for logo", "find hero image", "get company logo", "locate icons", "fetch background image", "retrieve product photos". Handles the search and source selection workflow. Does not apply when the request is about brand search, to generate NEW images with AI, or edit existing images.
Helps engineering managers assess and improve team health across morale, cohesion, delivery culture, and engagement — produces Google's 5 Factors (Project Aristotle), a 4-state team health diagnosis (Falling Behind / Treading Water / Repaying Debt / Innovating), a 5-zone intensity model, the Engagement Stack, the Trust Battery, Teamicide patterns (Peopleware), a blameless postmortem format, and a library of team activities organized by driver. Use when the user says "team morale," "team is struggling," "burnout," "engagement," "attrition risk," "psychological safety," "team dynamics," "something feels off," "team culture," "team is unhappy," "retros aren't working," "team isn't working hard enough," "ideas for team activities," or "how do I run a team offsite." Do NOT use for individual performance concerns (use `managing-high-performers`), team staffing or hiring (use `team-composition`), or individual motivation interventions (use `engineer-motivation`).
This skill should be used when a developer wants to autonomously execute all tasks under a fully-specified Epic or Feature — for example "go", "start building", "implement everything", "run the loop", "execute the feature", "build it all", "kick it off". Requires that the Epic/Feature/Task tree is fully written before starting. Chains implement → verify → PR for every task in dependency order, with targeted human-in-the-loop gates for contradictions and ambiguities.
Use when generating a Dockerfile for deploying a project to Zeabur. Use when the user needs help writing a Dockerfile for Node.js, Python, Go, Rust, PHP, Ruby, Java, .NET, or Elixir projects. Use when troubleshooting Dockerfile build failures on Zeabur.
Migrate Lightning Web Components from SLDS 1 to SLDS 2 by running the SLDS linter and fixing violations. Use this skill whenever users mention SLDS 2, SLDS uplift, linter violations, LWC token migration, class overrides, hardcoded CSS values that need SLDS hook replacement, or styling hook selection. Covers all styling hook categories — color, spacing, sizing, typography, borders, radius, and shadows. Also use when users mention no-hardcoded-values, no-slds-class-overrides, lwc-to-slds-hooks, no-deprecated-tokens-slds1, or ask about SLDS component migration — even if they don't explicitly say "uplift" or "migration".
Ultra-lightweight channel for feature workflows: No need to write design docs, checklists, or conduct phased reviews. Let AI write code directly as it normally would, but before it starts, tell it where the CodeStable knowledge base in the project is and how to search it. This way, the code it writes will have fewer pitfalls and be more consistent with project conventions. Trigger scenarios: Users say "fast mode", "fastforward", "skip all those steps", "just start coding", "help me make xxx" and the requirement is too small to go through the design process.
Discussion entry when ideas are still vague — first conduct triage through 1-2 rounds of dialogue to determine which downstream process this discussion should eventually go to: if the idea is clear enough, proceed directly to feature-design; if the direction of a small requirement is set, continue the discussion within the feature and document it in `{slug}-brainstorm.md`; if a large requirement cannot fit into a single feature, hand it over to roadmap for decomposition. The role of AI is a thinking partner, not a recorder — dig out the real problem the user wants to solve, proactively evaluate when the user brings a solution, and propose alternative directions when necessary. Trigger scenarios: when the user says "I have an idea that's not clear yet", "Let's brainstorm first", "I want to do something but it's still vague", "Let's talk about this area", "The function direction is still undecided", or when the user comes with a specific solution but wants to hear other ideas first. Bugs (go to issue) and refactoring (go to refactor) are not handled here.
Ultra-lightweight channel for refactor processes - used when changes are clearly too small to go through the full scan → design → apply three-stage workflow. AI directly identifies 1-3 low-risk optimization points, confirms with the user once, modifies in-place using classic methods, and validates itself by running tests. No scan checklist, no design documentation, no multi-step human verification required. Trigger scenarios: User says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip the extra steps", and the scope of changes is clearly localized to a single function / single component with test coverage for self-validation.
Document the pitfalls encountered or good practices discovered during this work into searchable learning documents, which can be accessed by both AI and humans when similar tasks arise in the future. Two tracks: The pitfall track records experiences where "things should have worked but didn't" — including bugs, configuration traps, environment issues, and integration failures; The knowledge track records findings that "should be the default approach going forward" — including best practices, workflow improvements, and reusable patterns. Trigger scenarios: Proactively prompt at the end of feature-acceptance or issue-fix workflows, or when the user mentions phrases like "document knowledge", "learning", "document learnings", or "record this experience". Spec documents record what was done, while learning documents record what pitfalls were encountered / what was learned — they complement each other and are not interchangeable.