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Found 202 Skills
Comprehensive antibody engineering and optimization for therapeutic development. Covers humanization, affinity maturation, developability assessment, and immunogenicity prediction. Use when asked to optimize antibodies, humanize sequences, or engineer therapeutic antibodies from lead to clinical candidate.
Match spoken edit beats to candidate B-roll assets using a normalized transcript, subtitle chunking, optional A-roll analysis, and a reusable B-roll catalog. Use this when the goal is to decide what B-roll should support each beat, not just to list assets or describe the video.
Multi-factor cross-sectional stock-selection strategy via Longbridge Securities — scores stocks in an index or candidate pool on value (1/PE, 1/PB), momentum (60-day return), quality (ROE), and low-volatility (60-day HV) factors; standardises to Z-scores; composites with equal or IC-weighted combination; constructs a TopN long portfolio (high-score group) and bottom-N short portfolio. Triggers: "多因子", "因子选股", "量化选股", "多因子模型", "因子投资", "横截面", "TopN组合", "IC权重", "多因子", "因子選股", "量化選股", "多因子模型", "橫截面", "multi-factor", "factor investing", "quantitative stock selection", "cross-sectional factor", "factor model", "IC weighting", "factor composite", "TopN portfolio", "factor score", "Z-score ranking".
Identify candidate stocks with sufficient pullbacks but intact trends and acceptable support structures, and output observation ranges, reversal signals, and invalidation conditions. Suitable for scenarios such as bargain-hunting opportunity screening, secondary entry for strong stocks, and pullback observation for trend stocks.
Research a vendor, product, or feature to collect all information needed before building an Elastic integration. Investigates data collection methods, API or log documentation, sample data formats, field schemas, ECS mapping candidates, and configuration requirements. Outputs a structured research brief to research_results/<product>/. Invoke manually with /research-integration.
Design and evaluate vaccine candidates using computational immunology tools. Covers epitope prediction (MHC-I/II binding via IEDB), population coverage analysis, antigen selection, adjuvant matching, and immunogenicity assessment. Integrates IEDB for epitope prediction, UniProt for antigen sequences, PDB/AlphaFold for structural epitopes, BVBRC for pathogen proteomes, and literature for clinical precedent. Use when asked about vaccine design, epitope prediction, immunogenicity, MHC binding, T-cell epitopes, B-cell epitopes, or population coverage for vaccine candidates.
Expand keyword candidates using autosuggest, related keywords, and community mining via DataForSEO
Facilitates the third step of a proven ideal-customer (ICP) method: refining classified strengths into keystones — the specific characteristics, behaviors, or circumstances that make a customer NEED an extreme version of a strength, badly enough to drive the purchase alone. Takes a strengths chart (S1, S2, … — file or pasted), walks the strengths one at a time asking who requires an extreme version of each, gates every candidate on naming a real market segment that typifies it (no segment = table stakes, cut), and records survivors in KEYSTONES.md (K1, K2, … with [S] references and example segments). Delivers the strategic verdict when few or none survive: the product isn't compelling yet. Load when the user has classified strengths and asks who needs them, who their target market is, or 'turn our strengths into keystones.' Do NOT load to classify strengths and weaknesses (the previous step), to derive deal-breakers or the anti-market (the next step), or to write the final ideal-customer definition (later).
Use when the user asks to "optimize meta tags", "write title tags / meta descriptions", "add Open Graph or Twitter cards", or "generate schema / JSON-LD" for FAQ, HowTo, Article, Product, or LocalBusiness rich-result candidates. Produces title/description options, an OG+Twitter block, and validated JSON-LD for the document head. Not for body copy — use content-writer; not for crawl/index technical issues — use technical-seo-checker. 标题优化/元描述/Schema标记/结构化数据
Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or brand data. Use when a person asks StyleSeed to remember, learn from, generalize, review, or prepare a reusable rule from an accepted design correction.
Evaluate ecommerce price candidates using unit economics, historical observations, elasticity analysis, scenario modeling, and controlled experiments. Use when a seller asks what price to test, how price changes could affect contribution or revenue, how to estimate elasticity, how to optimize a bundle or tier, or how to design a price experiment across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not claim a proven optimal price without sufficient clean data, and do not change live prices without explicit authorization.
Pick the right Lightning Base Component (`lightning-*`) for a given UI task, retrieve its full API (props, methods, events, slots) from the bundled per-component reference, and wire it into an LWC (LWC `.html`, `.js`, and `.css` files) without breaking SLDS. Use this skill when users say "I need a Lightning modal / datatable / combobox / record form", ask which `lightning-*` component fits a use case, want a shortlist of LBC candidates, are about to hand-roll a UI that a base component already provides, or are editing an LWC bundle's `.html` / `.js` / `.css` and need to select or wire a base component. Also triggers on "Lightning base component", "LBC", "lightning-combobox", "lightning-datatable", "use `lightning-` tag". DO NOT TRIGGER for applying SLDS design tokens, blueprints, or styling guidance in general — that is `design-systems-slds-apply`; this skill only selects and wires `lightning-*` base components.