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Found 2,059 Skills
Generate professional academic PowerPoint (PPTX) presentations from paper PDFs, structured outlines, or plain text. Use for thesis defense, seminar reports, literature presentations, and graduate school applications. Supports automatic figure extraction, LaTeX formula rendering, and bilingual (Chinese/English) layouts.
Configures the rollout shape of a PostHog experiment — the variant split (50/50, 80/20, A/B/C ratios), the overall rollout percentage that gates how many users enter the experiment, and the disambiguation when a percentage like "roll out to 25%" could mean either. Use when the user mentions a rollout percentage, variant split, or traffic distribution; gives a ratio like 60/40, 70/30, or 80/20; asks "who sees the test variant?"; wants to increase, decrease, or change the rollout or split on a draft or running experiment; weighs equal vs uneven splits; or proposes a mid-experiment split change (often an anti-pattern that needs reset or end-and-restart).
Generate a concise 4-5 page equity research earnings preview for a single company. Analyzes the most recent earnings transcript, competitor landscape, valuation, and recent news to produce a professional HTML report.
Find, evaluate, and download low-level common standard CAD parts from step.parts, such as screws, bolts, nuts, washers, bearings, standoffs, electronics parts, motors, connectors, and other off-the-shelf components. Use when Codex needs to search the hosted step.parts catalog, resolve fuzzy part names, standards, aliases, or dimensions, choose a matching part, fetch a canonical .step file, verify checksums, or use the step.parts API/OpenAPI/catalog endpoints for standard part discovery.
Use when the user has an SRT (or transcript text) in one language and wants it translated to another, with punctuation-bounded re-segmentation so cues end at real sentence breaks. Simplified Chinese (zh-CN) and English (en) are first-class targets; other targets follow the same rules. Outputs a target-language SRT or bilingual SRT — no audio, no burn-in. Triggers — "翻译字幕", "翻成中文", "translate this SRT", "中英双语字幕", "把这个 SRT 翻译成 X", "bilingual subtitles".
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.
This skill should be used when the user wants to interact with their paper database — listing papers, searching content, showing paper details, adding papers, or exporting context. Matches queries like "search papers for X", "add this arXiv paper", "show equations from paper Y", "what papers do I have". Prefer CLI over MCP RAG tools for direct lookups.
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".