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Found 1,946 Skills
Industry / sector panorama report — generates a comprehensive industry overview covering market dynamics, competitive landscape, key players, thematic trends, valuation ranges, and catalysts/risks. Outputs industry sizing, growth rate, major-player market share estimates, and valuation bands. Triggers: "行业概览", "行业报告", "板块报告", "行业全景", "竞争格局", "行业分析", "板块分析", "行業概覽", "行業報告", "板塊報告", "行業全景", "競爭格局", "industry overview", "sector overview", "industry report", "sector analysis", "market landscape", "competitive landscape", "industry sizing", "sector deep dive", "semiconductor industry", "AI sector overview".
Buffett-style stock screener — "What would Buffett buy now?" Generates 3–5 candidate stocks from a market / sector / preference query via a two-layer model: hard quant filter (ROE 5y ≥15%, debt/asset ≤50%, FCF positive 3y, listed ≥5y, gross margin ≥30%) → qualitative moat scoring (moat 35% / capital allocation 20% / earnings predictability 20% / valuation 15% / runway 10%). Longbridge CLI first, MCP fallback, WebSearch for gaps only. Output: candidate cards with moat-type tag, quantitative highlights, verdict (🟢 likely buy / 🟡 wait for price / 🔴 not at this price), deep-dive CTA to `longbridge-buffett-moat-analyzer`. Mandatory holding-period education + data-source appendix. Disqualifies airlines, pre-revenue biotech, ST, listing<5y. Triggers: "巴菲特会买什么", "巴菲特选股", "巴菲特风格的股票", "护城河选股", "宽护城河股票", "价值投资选股", "10年不动的股票", "定价权强的公司", "巴菲特會買什麼", "巴菲特選股", "護城河選股", "寬護城河股票", "Buffett screener", "what would Buffett buy", "wide-moat screener", "quality compounder screen", "Berkshire-style screen", "pricing-power screen".
Scores completed OKR sets at cycle close with KR-level scoring per the canonical OKR type enum (committed | aspirational | learning | operational_health | compliance_or_safety), committed-vs-aspirational interpretation, evidence quality assessment, learning synthesis, and next-cycle recommendations. Refuses to retroactively change targets or shrink committed scope, average away guardrail KRs, treat 0.7 as success for committed or compliance_or_safety KRs, equate effort with impact, or use scores for individual performance. Hands off to iterate-lessons-log, iterate-retrospective, define-hypothesis, measure-dashboard-requirements, measure-instrumentation-spec, and foundation-okr-writer.
Assess IT vendors and third-party partners with multi-factor risk scoring and regulatory compliance checklists. Use when evaluating technology vendors.
Guide counterparty credit risk measurement and management for OTC and securities trading. Use when measuring current or potential future exposure to a counterparty, setting or reviewing counterparty credit limits, evaluating ISDA Master Agreement netting benefits, designing collateral management or CSA terms, assessing central clearing mandates under Dodd-Frank or EMIR, monitoring counterparty creditworthiness via CDS spreads or ratings, managing Herstatt or settlement risk in FX, quantifying wrong-way risk, or building real-time exposure dashboards. Also use for counterparty default scenarios, credit deterioration events, EAD and SA-CCR calculations, and CVA capital charges.
End-to-end pipeline from unlabeled ml_app traces to a bootstrapped evaluator suite. Runs trace classification → root cause analysis → eval bootstrap in sequence with user checkpoints. Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", or wants a guided walkthrough from production data to evaluator code.
Use when the user has audio or video and wants a timestamped transcript (SRT) in the source language. Routes by source language — Chinese defaults to Volcano (豆包) ASR; other languages (Spanish, English, Portuguese, French, Italian, Japanese, Korean, etc.) use OpenAI Whisper API with word-level timestamps and self-assembled cues. Outputs SRT with punctuation-bounded cues capped for on-screen reading. Triggers — "转写", "转成字幕", "做 SRT", "transcribe", "make subtitles", "speech to text", "出字幕".
Build accretion/dilution analysis for M&A transactions. Models pro forma EPS impact, synergy sensitivities, and purchase price allocation. Use when evaluating a potential acquisition, preparing merger consequences analysis for a pitch, or advising on deal terms. Triggers on "merger model", "accretion dilution", "M&A model", "pro forma EPS", "merger consequences", or "deal impact analysis".
Create mathematical animations with Manim Community Edition(manimce). Generates distinctive, production-grade animations that avoid generic "AI slop" aesthetics. Use when user wants to animate concepts, equations, illustrate proofs, visualize algorithms, create math explainers, or produce 3Blue1Brown-style videos.
ELI5-style explanations with analogies and multiple examples. Explains concepts at different levels (ELI5, high school, undergraduate, graduate). Uses real-world analogies and visual metaphors. Use when explaining difficult concepts, clarifying confusing topics, or learning new subjects. Triggers - explain concept, ELI5, explain like I'm 5, what is, how does, why does, analogy for, simple explanation.
Self-improving agent toolkit — forge runtime tools, adapt personality traits, manage skills dynamically, compose multi-step workflows, and self-evaluate performance with bounded autonomy.
Visual ChangeNet for binary image classification and segmentation in AOI defect detection. Use when training, evaluating, exporting, or running inference for PCB defect detection or visual inspection, comparing image pairs for PASS/NO_PASS classification, or producing change-segmentation masks. Trigger phrases include "train Visual ChangeNet", "ChangeNet classify", "ChangeNet segment", "AOI defect detection", "PCB inspection model".