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Found 201 Skills
Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.
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.
Aspire platform help — word-of-mouth commerce for influencer marketing, product seeding, affiliate tracking, UGC sourcing, and paid social. Covers Discovery (170M+ profiles, Quickmatch AI, image recognition), Campaign Management (lifecycle tracking, content approval, term sheets), Product Seeding (Shopify gifting, shipping), Affiliate Tracking (promo codes, attribution), UGC & Content (library, repurposing for ads), Paid Social (TikTok Spark Ads, Meta whitelisting), Creator Payments (free processing). Integrates with Shopify, WooCommerce, Meta, TikTok, Pinterest, Klaviyo, CJ, Impact, ShareASale/Awin. Use when Aspire discovery isn't surfacing the right creators, product seeding orders aren't syncing with Shopify, affiliate tracking isn't attributing sales, content approvals are bottlenecked, not sure which Aspire plan fits, or integrations aren't connecting properly. Do NOT use for influencer strategy across platforms (use /sales-influencer-marketing) or affiliate program design (use /sales-affiliate-program).
[Hyper] Optimize an existing codebase through baseline-first experiments, binary evaluation, and one-mutation-at-a-time iteration. Use for codebase autoresearch, measured bottleneck reduction, benchmarked code optimization, and evidence-backed refactors.
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.
Interpret Apache Doris query runtime profiles, especially profile bottleneck triage, misleading wait counters, per-operator metric priority, scan, join-order/runtime-filter analysis, and evidence-bounded performance explanations. Use when given a Doris profile, query id, profile URL/text, or a request to explain Doris query performance.
Multi-Model Collaboration — Invoke gemini-agent and codex-agent for auxiliary analysis **Trigger Scenarios** (Proactive Use): - In-depth code analysis: algorithm understanding, performance bottleneck identification, architecture sorting - Large-scale exploration: 5+ files, module dependency tracking, call chain tracing - Complex reasoning: solution evaluation, logic verification, concurrent security analysis - Multi-perspective decision-making: requiring analysis from different angles before comprehensive judgment **Non-Trigger Scenarios**: - Simple modifications (clear changes in 1-2 files) - File searching (use Explore or Glob/Grep) - Read/write operations on known paths **Core Principle**: You are the decision-maker and executor, while external models are consultants.
Use this skill when the user asks for a review, audit, evaluation or analysis of a codebase, to identify bugs, security vulnerabilities, performance bottlenecks, or code quality concerns.
Tests API rate limiting implementations for bypass vulnerabilities by manipulating request headers, IP addresses, HTTP methods, API versions, and encoding schemes to circumvent request throttling controls. The tester identifies rate limit headers, determines enforcement mechanisms, and attempts bypasses including X-Forwarded-For spoofing, parameter pollution, case variation, and endpoint path manipulation. Maps to OWASP API4:2023 Unrestricted Resource Consumption. Activates for requests involving rate limit bypass, API throttling evasion, brute force protection testing, or API abuse prevention assessment.
Conducts comprehensive backend design reviews covering API design quality, database architecture validation, microservices patterns assessment, integration strategies evaluation, security design review, and scalability analysis. Evaluates API specifications (REST, GraphQL, gRPC), database schemas, service boundaries, authentication/authorization flows, caching strategies, message queues, and deployment architectures. Identifies design flaws, security vulnerabilities, performance bottlenecks, and scalability issues. Produces detailed design review reports with severity-rated findings, architecture diagrams, and implementation recommendations. Use when reviewing backend system designs, validating API specifications, assessing database schemas, evaluating microservices architectures, reviewing integration patterns, or when users mention backend design review, API design validation, database design review, microservices assessment, or backend architecture evaluation.
Application performance profiling and bottleneck identification — Node.js profiling, Chrome DevTools, flame graphs, memory leak detection, CPU profiling, React rendering performance. Activate on "profiling", "performance bottleneck", "flame graph", "memory leak", "slow app", "CPU profiling", "heap snapshot", "React re-renders", "EXPLAIN ANALYZE", "event loop lag", "clinic.js", "Core Web Vitals". NOT for infrastructure monitoring or observability (use logging-observability), load testing (use a load-testing skill), or database schema optimization.
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or audit such a base, or initialize a new one. Trigger on phrases like "add this to my wiki", "ingest this paper", "compile this into the knowledge base", "what does my wiki say about X", "lint the wiki", "build a knowledge base from these documents", "research notes", "second brain", "personal knowledge base", or any reference to LLM Wiki / OmegaWiki. Trigger even when the user does not say "wiki" — if they are accumulating sources over time and want them organized, this applies. The skill scales — sharded indexes, atomic pages, YAML frontmatter, and a bundled search script keep the wiki from becoming a context bottleneck at hundreds or thousands of pages.