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Found 193 Skills
Use when running an annual SaaS audit, doing category-level spend review, or rationalizing the supplier base — when the user needs to do a spend audit, spend categorization (UNSPSC-aligned), purchasing-cycle analysis, or risk-balanced supplier consolidation. Triggers on "spend audit", "SaaS audit", "spend categorization", "supplier rationalization", "supplier consolidation", "purchasing cycle", "procurement review", "category strategy", "duplicate SaaS", "renewal cluster". Ships 3 stdlib-only Python tools (UNSPSC-aligned spend categorizer with Pareto breakdown and industry profiles, purchasing-cycle analyzer that surfaces bottleneck categories per Goldratt's Theory of Constraints, supplier-consolidation planner that refuses single-source recommendations for tier-1 categories without a documented break-glass plan), 3 reference docs each citing 7+ authoritative sources (A.T. Kearney / Hackett / Spend Matters / UNSPSC / Productiv / Vendr / Tropic / IACCM / ISM / BCG), and a 20-minute spend-intake template. Distinct from sibling vendor-management (performance scoring of vendors you keep paying), finance/financial-analysis (close + report, not category strategy), and c-level-advisor/general-counsel-advisor (contract law, not category rationalization).
Use this skill whenever building, reviewing, or refactoring React components that fetch data from APIs — especially at scale (recommender carousels, infinite feeds, pages with many parallel fetches, dashboards). Covers request orchestration (parallelism, batching, deduplication), cache strategy (keys, normalization, staleTime, SWR), backend protection (concurrency caps, debounce/throttle, jittered retries, circuit breakers), prefetching (route loaders, hover/intent, idle, server hydration), failure resilience (AbortController, timeouts, error boundaries, stale fallback, idempotent mutations), and feed/carousel patterns (virtualization, cursor pagination, summary/detail split). Trigger even if the user doesn't explicitly mention "performance" or "scale" — any non-trivial React data-fetching code benefits from these patterns. Includes 5 ready-to-use scaffolding templates (resource query hook, carousel data loader, infinite feed, hover-prefetch link, request collapser).
Author step content for Novu workflows defined in the Dashboard or generated/edited via the Novu MCP. Use when filling in step controls (subject, body, editorType, headers, body, conditions) for email, in-app, sms, push, chat, delay, digest, throttle, or HTTP Request steps.
Run an autonomous Humanize-governed SGLang SOTA performance loop for one LLM model: first perform the fixed fair SGLang/vLLM/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches SGLang code, optionally uses ncu-report-skill for kernel evidence, and revalidates until SGLang matches or beats the best observed framework under the same workload and SLA.
Use when designing futuristic agentic workflows, when wanting AI to proactively act on team communications, or when eliminating the bottleneck of formal specifications
Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.
Fast automation platform error resolver for Power Automate, n8n, Make, Zapier and other platforms. Handles common patterns like 401/403 auth errors, 429 throttling, and data format issues. Provides immediate fixes without deep research for well-known error patterns. Use when error matches common scenarios (status codes 401, 403, 404, 429, timeout, parse JSON failures). For complex or unknown errors, defer to automation-debugger skill. When the user outputs some code/json snippets and ask for a quick fix, this skill will provide immediate solutions.
Implement Mistral AI rate limiting, backoff, and request management. Use when handling rate limit errors, implementing retry logic, or optimizing API request throughput for Mistral AI. Trigger with phrases like "mistral rate limit", "mistral throttling", "mistral 429", "mistral retry", "mistral backoff".
Handle Evernote API rate limits effectively. Use when implementing rate limit handling, optimizing API usage, or troubleshooting rate limit errors. Trigger with phrases like "evernote rate limit", "evernote throttling", "api quota evernote", "rate limit exceeded".
Design and implement end-to-end client onboarding workflows from prospect intake through funded account, covering KYC verification, document collection, e-signature, and custodian submission. Use when the user asks about building a digital onboarding flow, integrating identity verification or CIP checks, reducing NIGO rejection rates, opening complex account types like trusts or entities, connecting to custodian APIs, designing suitability questionnaires, or comparing advisor-assisted vs self-service models. Also trigger when users mention 'new account opening', 'onboarding bottleneck', 'KYC integration', 'beneficial ownership', 'OFAC screening', 'account funding', or 'onboarding automation'.
Use when optimizing application performance, reducing load times, improving database queries, meeting performance budgets, or diagnosing bottlenecks in web applications or APIs. Triggers: slow page loads, poor Web Vitals, database timeouts, large bundle size, user-reported sluggishness, scaling preparation.
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get tuning recommendations, or optimize Spark/Gluten configurations. Also trigger when the user mentions 'diagnose', 'compare runs', 'why is this query slow', 'tune my Spark job', 'benchmark comparison', 'performance regression', or asks about executor skew, shuffle overhead, AQE effectiveness, or Gluten offloading issues.