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Found 4,746 Skills
GitLab search operations via API. ALWAYS use this skill when user wants to: (1) search across GitLab globally, (2) find issues/MRs/code/commits, (3) search within a group or project, (4) find users or projects by keyword.
Guides VM lifecycle operations with kcli. Use when creating, managing, or troubleshooting virtual machines across providers.
Generate realistic KPI benchmarks for an influencer campaign before launch based on industry, platform, creator tier, and budget. This skill should be used when setting performance expectations for a creator campaign, estimating reach engagement and conversion benchmarks before launch, building KPI targets for an influencer program, forecasting campaign performance by creator tier and platform, setting EMV and ROAS targets for a campaign brief, defining what good looks like for an upcoming creator activation, calibrating expectations for a gifting or paid campaign across Instagram TikTok or YouTube, or creating a benchmark framework to measure campaign success against. For calculating ROI after a campaign ends, see campaign-roi-calculator. For calculating engagement rates from actual post data, see engagement-rate-calculator-benchmarker. For building a full KPI framework tied to business objectives, see campaign-goal-to-kpi-framework-builder.
Score each creator on a completed campaign across consistency, content quality, engagement rate, and brand alignment, then produce a ranked retention list for future campaigns. This skill should be used when grading creators after a campaign ends, evaluating influencer performance post-campaign, ranking creators by campaign performance, building a retention list of top creators, deciding which creators to rebook for the next campaign, scoring influencer deliverables after a launch, comparing creator performance across a campaign roster, auditing which creators delivered the most value, or tiering creators into re-engage versus one-and-done lists. For calculating engagement rates and benchmarking them by tier, see engagement-rate-calculator-benchmarker. For scoring niche fit before a campaign, see niche-fit-scorer. For building the full campaign report with ROI narrative, see campaign-roi-calculator-narrative-builder.
Node.js backend patterns: framework selection, layered architecture, TypeScript, validation, error handling, security, production deployment. Use when building REST APIs, Express/Fastify servers, microservices, or server-side TypeScript.
Chat with web AI agents (ChatGPT, Gemini, Claude, Grok, NotebookLM) via browser automation. Use when stuck, need cross-validation, or want a second-model review.
Detect and analyze trending market themes across sectors. Use when user asks about current market themes, trending sectors, sector rotation, thematic investing, what themes are hot or cold, or wants to identify bullish and bearish market narratives with lifecycle analysis.
Conduct deep research on any topic through structured investigation design. Use when the user needs comprehensive, multi-source analysis -- not a quick lookup. Triggers: deep research, comprehensive analysis, research report, compare X vs Y, analyze trends, investigate, or any request requiring synthesis across multiple perspectives. Do NOT use for simple questions answerable with 1-2 searches or for debugging.
Discover existing shadcn components from registries before building custom. Use PROACTIVELY when about to build any UI component, page section, or layout. Use when user explicitly asks to find/search components. Searches 1,500+ components across official and community registries including @shadcn, @blocks, @reui, @animate-ui, @diceui, Magic UI, and 30+ specialty registries. Provides install commands and code examples. Works best with shadcn MCP configured, but provides manual guidance without it.
Analyze codebase structure, dependencies, changes, and cross-agent handoffs. Use when user asks about project structure, where code is located, how files connect, what changed, how to resume work, or before starting any coding task.
Capture and persist lessons learned from a session to compound knowledge over time. Triggers on "/lessons-learned", "what did we learn", "save lessons", "update skills with what we learned", or at the end of a complex multi-session task. PROACTIVE USE: This skill should also be suggested or invoked (1) when resuming from context compaction (the previous context likely contained unrecorded lessons), (2) after resolving a non-trivial bug or debugging session, (3) after significant friction or failed approaches that yielded insight, (4) after a council-of-bots review that surfaced fixes. Identifies reusable patterns, bug fixes, workflow insights, and tool quirks, then persists them to the right places: auto-memory (project-specific), skill files (reusable across projects), or both.
Apply the "How I Made Your Machine" coding style guide to implementation, refactoring, and code review tasks across TypeScript, Rust, and Python. Use when a request asks for this style guide, when improving maintainability and type safety, when modeling domain concepts with explicit variants/types, or when enforcing behavior-first testing.