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Found 1,931 Skills
Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.
Analyze the bond futures basis by pricing futures, identifying the cheapest-to-deliver, and comparing with yield curves to assess delivery option value and basis trading opportunities. Use when analyzing bond futures, computing the basis, identifying CTD bonds, calculating implied repo rates, or evaluating basis trades.
Investment idea generation — systematically surfaces new investment opportunities by combining quantitative screening (low valuation / high momentum / improving fundamentals), thematic research (sector trends / policy catalysts), and pattern recognition (historical analogues), producing a long/short candidate list. Triggers: "投资想法", "选股灵感", "投资机会", "找股票", "发掘机会", "多头机会", "空头机会", "主题投资", "投資想法", "選股靈感", "投資機會", "找股票", "多頭機會", "空頭機會", "主題投資", "investment ideas", "stock ideas", "investment opportunities", "idea generation", "long ideas", "short ideas", "thematic investing", "stock discovery", "find me stocks", "what should I buy".
Company tear sheet / one-pager via Longbridge Securities — generates a high-density 1–2 page company snapshot: business overview, key financials (revenue / net income / EPS / ROE), valuation multiples (PE / PB / EV-EBITDA), price performance, major shareholders, and recent catalysts. Triggers: "公司单页", "公司快照", "公司简报", "公司画像", "一页纸分析", "公司概要", "股票简报", "公司單頁", "公司快照", "公司簡報", "公司畫像", "一頁紙分析", "company tearsheet", "company profile", "company snapshot", "one-pager", "company brief", "stock summary", "company factsheet".
Generates sustainability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify environmental impact requirements, and provide actionable recommendations to build, deploy, and manage the workload sustainably in Google Cloud.
AI-powered stock and crypto analysis using the aipa CLI. Use this skill whenever the user asks to analyze a ticker, compare stocks, get technical analysis, or answer any financial market question about Vietnamese stocks (VIC, VCB, FPT...), cryptocurrencies (BTC, ETH...), or global assets. Also use for price action analysis, moving average analysis, support/resistance questions, sector comparison, Wyckoff analysis, or trading insights. Also handles fundamental analysis when the user explicitly asks for fundamentals, PE, ROE, NPL, CAR, valuation, or "phân tích cơ bản" — use `aipa fundamentals` commands to enrich technical analysis with financial ratios, company info, and fundamental screening/ranking. For raw price data without AI, use the aipa-data skill instead.
Use when writing QGIS expressions for filtering, labeling, symbology, or field calculations. Prevents expression syntax errors and context misconfiguration. Covers QgsExpression parsing, evaluation contexts, field calculator, data-defined properties, and custom functions. Keywords: QgsExpression, expression, field calculator, label expression, data-defined, @qgsfunction, filter, evaluate, calculate field, formula, conditional label, dynamic value.
Create structured technology trade-off analysis documents with scored comparison matrices. Use this skill whenever the user wants to compare technologies, evaluate architectural options, analyze build-vs-buy decisions, assess migration strategies, or produce any decision document that compares multiple approaches across weighted dimensions. Triggers on: 'trade-off analysis', 'tradeoff', 'comparison matrix', 'evaluate options', 'which technology should we use', 'compare approaches', 'pros and cons of', 'build vs buy', 'migration analysis', 'consolidation analysis', 'technology selection'. Also use when the user has completed technical research and wants to structure findings into a decision document.
Generate a Well-Architected-aligned Architecture Decision Record (ADR) that documents a design decision with context, options evaluated, trade-offs, and WA pillar impact.
Decide where files live in an ML experimentation project: reusable code in `src/<pkg>/`, one `# %%` script per experiment in `experiments/`, design notes + index in `journal/`, reports in `reports/`, agent-only probes in `scratch/`, narrative digest in `overview/summary.md`. Owns the layout, the file-creation rules (one file per experiment, ask before editing), and the jupytext `# %%` script convention. Never imposes `data/` — the user owns that. TRIGGER — any of: - Starting a new ML project / scaffolding a workspace. - About to create the first experiment file in a project. - About to create `src/<pkg>/data.py` / `features.py` / `pipeline.py` / `evaluate.py` for the first time. - About to write a `.ipynb` for experimentation — redirect to a `# %%` script under `experiments/`. - User asks where something should live, how to organize the project, or how to set up the workspace. - About to add a new experiment iteration — decide new file vs edit existing (ask the user). SKIP when: the file is clearly part of an already-populated module (e.g., adding a function to existing `features.py`); pure refactor inside a single existing file; pipeline declaration mechanics (`build-ml-pipeline`); evaluation mechanics (`evaluate-ml-pipeline`); skore symbol lookup (`python-api`). HOW TO USE: **first run the Detection table** below — if any signal matches, glue to existing conventions (do not rename or move folders). If no signal matches, scaffold the default layout. **Emit the Pre-flight checklist as visible text and read the Stop conditions before any file is created or edited.** Use templates in `templates/`; copy and adapt, do not rewrite from scratch.
Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable attention across camera views and time for end-to-end 3D perception, with an instance bank for temporal tracking. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Sparse4D model. Trigger phrases include "train Sparse4D", "multi-camera 3D detection", "temporal 3D tracker", "sparse query 3D perception".
Pressure-tests a product idea before the founder invests in planning, building, or launching. Surfaces fatal flaws, tests whether the problem is real, maps real competition (including current behavior), plans first 10 customers, defines a 2-week MVP test, returns a strong/weak/pivot verdict, then sharpens `docs/product-idea.md` based on the founder's direction calls. Use when the founder says "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "stress test my idea", or otherwise wants to evaluate an idea before committing.