Loading...
Loading...
Found 1,928 Skills
Generate and curate evaluation datasets — structured generation via dimensions-tuples-NL, quick from description, expansion from existing data, plus dataset maintenance through deduplication, rebalancing, and gap-filling. Use when creating eval data, expanding test coverage, or cleaning datasets. Do NOT use when sufficient real production data exists (use analyze-trace-failures instead). Do NOT use for evaluator creation (use build-evaluator).
Investment proposal generation via Longbridge Securities — produces a structured investment memo covering: executive summary, company overview, investment thesis (3–5 core points), financial analysis, valuation, catalysts and timeline, risk factors, and position recommendation. Triggers: "投资提案", "投资建议书", "投资报告", "投资摘要", "核心逻辑", "投资理由", "建仓建议", "投資提案", "投資建議書", "投資報告", "投資摘要", "核心邏輯", "建倉建議", "investment proposal", "investment memo", "investment summary", "investment rationale", "position recommendation", "investment case", "buy memo".
Financial statements, business segments, dividends, valuation multiples (PE/PB/PS), industry comparison, operating data, corporate actions, company and executive profiles, cross-stock comparison, and valuation ranking via Longbridge. Also: DCF models, value investing screens (low PE/PB, margin of safety), and behavioral finance analysis frameworks. Triggers: "财报", "三表", "利润表", "资产负债", "现金流", "估值", "PE", "PB", "分红", "公司信息", "高管", "行业估值", "并购", "DCF", "内在价值", "低估值", "安全边际", "行为金融", "小盘成长", "专精特新", "財報", "估值", "分紅", "內在價值", "安全邊際", "financial report", "income statement", "balance sheet", "valuation", "dividend", "company info", "industry valuation", "DCF", "value screen", "behavioral finance", "利潤表", "資產負債", "現金流", "行業估值", "併購", "行為金融", "小盤成長"
Audit npm, pip, and Go dependencies that OpenClaw skills try to install. Checks for known vulnerabilities, typosquatting, and malicious packages.
Create structured handoff for session continuation. Triggers: handoff, pause, save context, end session, pick up later, continue later.
Validate built features through conversational testing, running UAT, user acceptance testing, checking if features work, or verifying implementation. Triggers include "verify work", "test features", "UAT", "user testing", "check if it works", and "validate features".
Use when designing visual interfaces, data visualizations, educational content, or presentations and need to ensure they align with how humans naturally perceive, process, and remember information. Invoke when user mentions cognitive load, visual hierarchy, dashboard design, form design, e-learning, infographics, or wants to improve clarity and reduce user confusion. Also applies when evaluating existing designs for cognitive alignment or choosing between design alternatives.
AWS cost optimization and FinOps workflows. Use for finding unused resources, analyzing Reserved Instance opportunities, detecting cost anomalies, rightsizing instances, evaluating Spot instances, migrating to newer generation instances, implementing FinOps best practices, optimizing storage/network/database costs, and managing cloud financial operations. Includes automated analysis scripts and comprehensive reference documentation.
Range bar evaluation metrics for quant trading. TRIGGERS - range bar metrics, Sharpe ratio, WFO metrics, PSR DSR MinTRL.
GoPlus AgentGuard — AI agent security guard. Automatically blocks dangerous commands, prevents data leaks, and protects secrets. Use when reviewing third-party code, auditing skills, checking for vulnerabilities, evaluating action safety, or viewing security logs.
Use when user wants to find a note to publish as a blog post. Triggers on「选一篇笔记发博客」「note to blog」「写博客」「博客选题」. Scans Obsidian notes via Python script, evaluates blog-readiness, supports batch selection with fast/deep dual-track and parallel Agent dispatch.
Run Confused and GuardDog to detect dependency confusion and typosquatting risks. Checks if internal package names exist on public registries and identifies malicious packages.