Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Convert an imperative Flet Python app in which controls are mutated and then page.update is called to declarative style using flet.component, flet.observable and state hooks.
Handle quick, repeatable Dagger repository maintenance chores. Use when the user asks for small operational changes and wants the same established edits and commit style applied quickly.
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.
Manage the evolution, merging, archiving, and cleanup of specifications and blueprints across the workspace.
Analytical navigation of A2UI Spec-Driven Development module and codebase blueprints.
Provides instructions on how to create a new language-agnostic A2UI Feature Blueprint, ensuring consistency and ease of cross-language implementation.
Provides instructions on implementing feature specs or module blueprint commit diffs in a specific codebase.
Step-by-step phased instructions for building new A2UI Core SDKs, Framework Adapters, and Inference SDKs from scratch in any client language.
MNN ARM CPU 算子和低 bit kernel 性能优化。重点覆盖正确性基线、C++ 标量 oracle、C++ SIMD 模拟、寄存器规划、ARM 汇编实现、dispatch/pack 集成、模型级回归和 roofline 性能分析。