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Found 92 Skills
AI for Science 场景下的昇腾 NPU Profiling 采集与性能分析 Skill，用于在华为 Ascend NPU 上使用 torch_npu.profiler 采集 L0、L1、L2 级性能数据，分析训练或推理中的算子耗时、调用栈、内存与瓶颈，并指导后续调优。
Capture Unreal Engine traces, inspect Trace Store files, keep Unreal Insights GUI open, and export/summarize timing/counter data.
Official PostgreSQL Model Context Protocol Server for database interaction.
HOWL v2 — Hunt, Optimize, Win, Learn. Nightly self-improvement loop for the WOLF autonomous trading strategy. Runs once per day (via cron) to review all trades from the last 24 hours, compute win rates, analyze signal quality correlation, evaluate DSL tier performance, identify missed opportunities, and produce concrete improvement suggestions for the wolf-strategy skill. v2 adds fee drag ratio (FDR) analysis, holding period bucketing, LONG vs SHORT regime detection, rotation cost tracking, cumulative drift detection, and gross vs net profit factor separation. Use when setting up daily trade review automation, analyzing trading performance, or improving an autonomous trading strategy through data-driven feedback loops. Requires Senpi MCP connection, mcporter CLI, and OpenClaw cron system.
Expert-level browser automation, debugging, and performance analysis using Chrome DevTools MCP. Use for interacting with web pages, capturing screenshots, analyzing network traffic, and profiling performance.
When the user wants to analyze Google Ads performance by device, geography, day of week, hour of day, or any combination of these dimensions. Also triggers on 'device performance', 'mobile vs desktop', 'bid adjustments', 'geo performance', 'location targeting', 'dayparting', 'ad schedule', 'hourly performance', 'device modifiers', 'geo bid adjustments', 'best time to run ads', 'location exclusions', or 'campaign splits by device or geo'. For full campaign structure see google-ads-search. For Smart Bidding see google-ads-bidding.
Uses Chrome DevTools via MCP for efficient debugging, troubleshooting and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests.
Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to 6 specialists, generates health score. Use when user says "audit", "full SEO check", "analyze my site", or "website health check".
Use when generating performance hypotheses backed by git history and code evidence.
Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
Use when working with comprehensive review full review
Convert natural language questions into safe executable SQL to query Ascend PyTorch Profiler / msprof database for operator time consumption, communication, dispatch, and other performance data. Supports table schema extraction from official documentation. Use this skill when the user wants to: (1) analyze Ascend profiling database, (2) query operator performance data, (3) analyze communication and dispatch bottlenecks, (4) check table schema for profiling data. Trigger: user mentions "profiler db", "sqlite", "sql", "table", "schema", "ascend-pytorch-profiler", "msprof", "operator time", "communication time", "dispatch analysis", "性能分析", "算子耗时", "数据库查询", "性能数据", "性能瓶颈"