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Found 160 Skills
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".
Builds site selection and cannibalization analysis workflows in CARTO. Triggers when the user mentions site selection, cannibalization, cannibalizing, new store location, where to open, optimal location, facility placement, network impact, overlapping catchments, twin areas, similar locations, look-alike areas, find locations like my best, store overlap, revenue impact of new store, commercial hotspots, demand hotspots, location scoring, location ranking, expand network, new branch, franchise placement, EV charging siting, or wants to evaluate candidate sites, quantify overlap between trade areas, or find areas that resemble top-performing locations.
Builds trade area and catchment analysis workflows in CARTO. Triggers when the user mentions trade area, catchment area, isochrone, site selection, where to open, best location, billboard, OOH, audience targeting, drive time, walk time, coverage area, commercial hotspot, site scoring, location ranking, or wants to generate isochrones, score candidate locations, or identify the best sites for retail, advertising, or services.
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.
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
Mine Claude Code session logs for skill idea candidates. Use when running the weekly skill generation pipeline to extract, score, and backlog new skill ideas from recent coding sessions.
This skill should be used when the user needs to create a personalized, compelling cover letter from a resume and job description. Use when writing job application letters, addressing specific role requirements, handling career change narratives, or structuring persuasive arguments for candidacy.
Route creator discovery requests into the right collection strategy before platform execution. Use this when the user wants to find creators, KOLs, KOCs, influencers, or partnership candidates and the request includes constraints such as follower range, niche, audience, geography, language, or collaboration fit. This skill decides whether to use handle-first, content-first, graph-first, or mixed discovery, then hands off to TikTok, Instagram, X, and creator-outreach skills.
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.
Analyze, prioritize, and document test cases in TMS (Jira/Xray) -- the bridge between manual QA and test automation. Use when creating Test/ATP/ATR artifacts, calculating ROI to choose which tests to automate, maintaining US-ATP-ATR-TC traceability, or repairing broken TMS links. Supports four scopes: module-driven (exhaustive module exploration), ticket-driven (QA-approved user story), bug-driven (regression TC for a closed bug), and ad-hoc/exploratory. Produces three outcomes per TC: Candidate (feeds test-automation), Manual (terminal), Deferred (terminal). Triggers on: document tests, create test cases in Jira/Xray, prioritize for automation, ROI analysis, which tests to automate, Candidate vs Manual, link ATP to ATR, fix TMS traceability, stage 4, turn this bug into a regression test. Do NOT use for writing test code (test-automation) or running suites (regression-testing).
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".
Suggests code simplification opportunities. Identifies extract method candidates, complex expressions, redundant code, refactoring opportunities.