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Found 201 Skills
Conduct comprehensive web performance audits. Measure page speed, identify bottlenecks, and recommend optimizations to improve user experience and SEO.
Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.
Live cursors and multiplayer presence with Rivet Actors: per-connection cursor state, realtime updates over events or raw WebSockets, and throttling.
Expert mobile game developer specializing in iOS and Android game optimization, touch input design, battery and thermal management, device fragmentation handling, and App Store/Play Store submission. Deep knowledge of mobile-specific constraints and best practices for shipping performant, player-friendly mobile games. Use when "mobile game, ios game, android game, touch input, mobile optimization, mobile performance, app store, play store, mobile battery, thermal throttling, mobile porting, tablet game, phone game, mobile monetization, mobile build, mobile, ios, android, touch, optimization, performance, battery, thermal, app-store, play-store, game-development, unity-mobile, godot-mobile, device-fragmentation" mentioned.
VP of Engineering advisory for startups: delivery throughput (DORA 4 metrics + bottleneck identification), engineering hiring funnel (sourcing → screen → onsite → offer conversion + time-to-fill + pipeline gap), engineering team structure (squad/tribe/chapter design + tech-lead manager-trigger thresholds), and production discipline (on-call, deployment cadence, postmortem culture). Use when sprint velocity is dropping, eng hiring is broken, team structure is unclear, or deciding when to add a tech-lead manager. NOT a CTO skill (which owns architecture) — VPE owns delivery operations and how the team ships.
Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.
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).
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
Migrate vision/detection/segmentation small models to Ascend NPU, covering the full workflow: model structure analysis, migration verification, performance profiling, and optimization. Based on torch_npu and msprof Use this skill when the user wants to: (1) migrate encoder-only models like ResNet, YOLO, UNet to Ascend NPU, (2) analyze model structure for migration feasibility, (3) verify model inference on NPU, (4) identify performance bottlenecks and get optimization suggestions Trigger: user mentions "migrate", "migration", "Ascend", "NPU", "YOLO", "ResNet", "encoder-only", "detection", "segmentation", "adaptation", "adapt", "迁移", "昇腾迁移", "小模型", "适配", "昇腾适配", "GPU迁移", "NPU适配", "适配NPU", "适配昇腾"
Worker that checks DRY/KISS/YAGNI/architecture compliance with quantitative Code Quality Score. Validates architectural decisions via MCP Ref: (1) Optimality - is chosen approach the best? (2) Compliance - does it follow best practices? (3) Performance - algorithms, configs, bottlenecks. Reports issues with SEC-, PERF-, MNT-, ARCH-, BP-, OPT- prefixes.
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.