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Found 264 Skills
Control Unreal Engine 5 editor via HTTP commands. Spawn/delete/transform actors, manage blueprints, materials, animation blueprints, and any UObject property via reflection. Use when the user asks to create, modify, or query anything in UE5 editor, or mentions UE5, Unreal, actors, blueprints, levels, materials, animation, input, or characters.
You MUST use this when a request needs design decisions before code — new features, product or UX behavior, architecture changes, unclear success criteria, or two materially different approaches. Explicitly specified mechanical refactors, localized fixes with known expected behavior, and single-outcome config changes go straight to implementation.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
Creates and edits Liftosaur workout programs using Liftoscript. Use for program authoring, exercise/set changes, progression setup (lp/dp/sum/custom), advanced custom progression logic, custom progress/update script debugging, and reuse/template refactors.
Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Supports the user's stated goal of understanding design choices as learning opportunities.
API reference: Swift Concurrency. async/await, Task, TaskGroup, actors, AsyncSequence, AsyncStream, continuations.
Build employee turnover prediction models to identify flight risk and retention drivers. Use this skill when the user needs to predict which employees are likely to leave, identify retention risk factors, or prioritize HR interventions — even if they say 'attrition prediction', 'who is going to quit', or 'employee retention model'.
Run large codebase migrations and multi-file refactors. Uses the Composio CLI to coordinate issue tracking, batched PRs, and CI verification while the agent executes the transforms locally across hundreds of files.
Write, review, or fix Swift concurrency code using actors, async/await, and structured concurrency. Use when implementing concurrent features, resolving data race warnings, migrating from GCD, enabling Swift 6 strict concurrency mode, or adopting Swift 6.2 approachable concurrency (@concurrent, main-actor-by-default, isolated conformances).
Conduct an interactive discovery interview to produce a structured product specification. Triggers: write a spec, PRD, feature spec, requirements, product requirements, scope a project, brainstorm a feature, flesh out an idea, plan a new project. Uses AskUserQuestion for all user choices; WebSearch/WebFetch when the user wants research. Outputs: user stories, acceptance criteria, technical constraints, prioritized requirements in docs/specs/ per SPEC_TEMPLATE.md. Do NOT use for: implementation, code review, debugging, refactors, or when the user already has a complete spec they only want edited.
Apply when deciding where and how a VTEX IO app should store and read data. Covers when to use app settings, configuration apps, Master Data, VBase, VTEX core APIs, or external stores, and how to avoid duplicating sources of truth or abusing configuration stores for operational data. Use for new data flows, caching decisions, refactors, or reviewing suspicious storage and access patterns in VTEX IO apps.
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".