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Found 3,103 Skills
Intelligent loading performance analysis with automated workflows for TTFB investigation (DNS/connection/server breakdown), render-blocking detection, script performance deep dive (first vs third-party attribution), font optimization, and resource hints validation. Includes decision trees that automatically analyze TTFB sub-parts when slow, detect script loading anti-patterns (async/defer/preload conflicts), identify render-blocking resources, and validate resource hints usage. Features workflows for complete loading audit (6 phases), backend performance investigation, and priority optimization. Cross-skill integration with Core Web Vitals (LCP resource loading), Interaction (script execution blocking), and Media (lazy loading strategy). Use when the user asks about TTFB, FCP, render-blocking, slow loading, font performance, script optimization, or resource hints. Compatible with Chrome DevTools MCP.
Expert guide for building APIs with the Elysia framework, including routing, validation, plugins, error handling, and type-safe handlers. Use when building APIs with Elysia framework, defining routes, plugins, or type-safe handlers.
Amend an existing plan file — add tasks, modify pending or delegated tasks, and mark completed tasks that are invalidated by the change as [>] (needs re-run). Walks through analysis, cascading impact, and user confirmation before touching the file. When memory (wiki substrate) exists, it may also preserve durable amendment findings there. Reports goal impact and routes intent, boundaries, or validation changes to loam::setting-goals.
Create, review, and manage first-class goal artifacts that turn a broad ambition into an externally verifiable outcome. Goals are optional, long-lived workflow artifacts stored at goals/<slug>.md. They own intent, a validation contract, lifecycle, concise review evidence, and linked work. Use when the user wants to set a goal, review a goal, pause or reactivate a goal, achieve or abandon a goal, or change what a goal means. Not for specs, plans, memory, or checkpoints.
Use this skill when the user wants to create, read, update, delete, or troubleshoot AI agents (conversational agents with behaviors). Covers 7 MCP tools including pre-flight validation, plus pipe-scoped knowledge bases (list, plain text/document/data lookup CRUD, access probe) attached via dataSourceIds. For traditional automations and AI automations, see skills/automations/.
Applies cross-parameter default rules when creating .NET projects with dotnet new, filling gaps consistently without overriding values the user set explicitly. USE FOR: choosing which target framework to pair with native AOT, deciding whether to keep HTTPS when authentication is enabled, recognizing that controllers and minimal-API flags are mutually exclusive, filling unset related parameters during project creation, explaining why a default was applied and ensuring an explicit user value is never overridden. DO NOT USE FOR: creating the project itself (use template-instantiation), finding or comparing templates (use template-discovery and template-comparison), authoring or validating custom templates (use template-authoring and template-validation).
One-time pipeline configurator. Inspects the repo (default branch, validation scripts, labels), asks a few questions, writes .ai/agentic.config.json — the file every other skill reads — installs the tracker descriptor, and generates missing project docs (SDLC.md, CODE_REVIEW.md, BACKWARD_COMPATIBILITY.md, AGENTS.md starter). Re-run when the toolchain or label taxonomy changes. Verifies cross-skill coverage and prints the install command for missing skills.
Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.
One-shot brand-faithful presales redesign of a website page. The user provides only the URL; everything else — extraction, tension identification, three differentiated variants (one fully cinematic), validation — is derived from the captured brand surface. Use when the user asks to "uplift", "refresh", or "redesign a site for presales" without wanting to coordinate the extract / direct / prototype chain themselves.
Comprehensive guide for implementing Syncfusion React Calendar components including Calendar, DatePicker, DateRangePicker, DateTimePicker, and TimePicker. Covers installation, data binding, date/time selection, range selection, formatting, localization, masking, validation, customization, templates, accessibility, and controlled component patterns in React applications.
Synthesize the three Jason Shapiro contrarian-pipeline verdicts (COT crowding, news-reaction failure, weekly price-action confirmation) into one actionable setup_status via a fail-closed precedence state machine. Pure, offline synthesis -- no network, no API keys, no computation beyond validation and precedence.
Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. Part of the skills-for-java project