productivity▌

Swift Concurrency review and remediation for Swift 6.2+ codebases. \n \n Diagnoses and fixes actor isolation, Sendable safety, and data-race warnings through targeted edits that preserve existing behavior \n Applies minimal, concrete fixes: @MainActor annotations, actor isolation, Sendable conformance, and completion-handler-to-async/await migrations \n Triages compiler diagnostics by checking project concurrency settings, actor context, and whether code is UI-bound or background work \n Verifie

Establish bidirectional links between Figma design components and code implementations using Code Connect. \n \n Requires Figma MCP server connection and published components on Organization or Enterprise plans \n Automatically identifies unmapped components in Figma selections, then scans your codebase for matching code components by name, structure, and props \n Presents matched components to you for review before creating mappings, allowing selective acceptance or rejection \n Supports multip

Complete App Store Optimization toolkit for researching, optimizing, and tracking mobile app performance across Apple App Store and Google Play Store. \n \n Covers keyword research, competitor analysis, metadata optimization (titles, descriptions, keywords), and visual asset strategies with platform-specific character limit validation \n Includes review sentiment analysis, rating improvement tactics, and A/B testing frameworks for continuous conversion optimization \n Provides ASO health scoring

Configure persistent memory search for Moltbot/Clawdbot agents to retain context across sessions. \n \n Add memorySearch config block with provider (Voyage, OpenAI, or local), sources (memory files and/or sessions), and relevance thresholds \n Create a workspace structure with MEMORY.md for curated long-term facts and memory/logs/ for daily timestamped logs \n Supports three embedding providers; Voyage recommended but local option available without API keys \n Includes troubleshooting for common

Write correct, performant SQL across all major data warehouse dialects. \n \n Covers five major dialects: PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks with dialect-specific syntax for date/time, string functions, arrays, and JSON handling \n Includes common analytical patterns: window functions, CTEs, cohort retention, funnel analysis, and deduplication with ready-to-use examples \n Provides performance optimization tips per dialect, such as clustering keys in Snowflake, partition p

Automated analysis of market-moving news from the past 10 days with impact-ranked reporting. \n \n Collects news across six categories (monetary policy, inflation, earnings, geopolitical, commodities, corporate) using WebSearch and WebFetch, prioritizing Tier 1 sources \n Ranks events by impact score combining price movement magnitude, breadth across asset classes, and forward-looking significance \n Analyzes multi-asset reactions (equities, bonds, commodities, currencies) and compares actual ma

Framework for writing job descriptions that attract the right candidates by defining business impact. \n \n Start with 12-month success: define what changes in the business after the hire, not a list of daily tasks \n Identify the spike: determine one or two areas where the candidate must excel, rather than seeking a generalist \n Replace proxies with outcomes: frame requirements around specific capabilities and progress, not years of experience or tool lists \n Use polarizing language intention

Create and maintain minimal, high-signal agent documentation under 60 lines. \n \n Enforces research-backed best practices for agent-facing docs; instruction quality degrades with length \n Requires three core sections: Package Manager, File-Scoped Commands (per-file test/lint/typecheck), and Commit Attribution with agent identity \n Analyzes project structure (lock files, linter configs, CI commands, monorepo indicators) to determine what belongs in the file \n Uses headers and bullets only; re

Comprehensive sentiment analysis report for individual stocks with bull and bear breakdowns. \n \n Aggregates sentiment scores and viewpoint counts across multiple bloggers for a single ticker \n Retrieves detailed arguments from both bullish and bearish perspectives with direct quotes and attribution \n Identifies blogger positions and conviction levels (heavy/light holdings) to gauge skin-in-the-game confidence \n Compiles risk factors and presents findings in a structured report format withou

Multi-agent DAG execution framework for decomposing large features into independently verifiable work units with quality gates. \n \n Structures complex features as directed acyclic graphs with explicit dependencies, complexity tiers (isolated edits to schema/security changes), and per-unit acceptance criteria \n Enforces a seven-stage pipeline: RFC intake, DAG decomposition, unit assignment, implementation, validation, merge queue processing, and final system verification \n Implements merge qu