Maintain Cupertino + Lumina visual consistency for the AERA frontend. Use when creating or modifying UI, CSS/tokens, layouts, components, menus/overlays, tables, charts, or interaction states in this repo.

Single source of truth and librarian for ALL Cursor documentation. Manages local documentation storage, scraping, discovery, and resolution. Use when finding, locating, searching, or resolving Cursor documentation; discovering docs by keywords, category, tags, or natural language queries; scraping from llms.txt; managing index metadata (keywords, tags, aliases); or rebuilding index from filesystem. Run scripts to scrape, find, and resolve documentation. Handles doc_id resolution, keyword search, natural language queries, category/tag filtering, alias resolution, llms.txt parsing, markdown subsection extraction for internal use, hash-based drift detection, and comprehensive index maintenance.

Upload markdown (including docmgr ticket docs) to a reMarkable device as PDF via a local uploader script, with a safe dry-run and optional mirroring of ticket structure. Use when the user asks to send docs to reMarkable, export markdown to PDF for reMarkable, or troubleshoot rmapi/pandoc/xelatex.

Build scalable customer support systems including help centers, chatbots, ticketing systems, and self-service knowledge bases. Use when designing support infrastructure, reducing support load, improving customer satisfaction, or scaling support without linear hiring.

Real-world ROI case study for healthcare content automation pipeline. Clínica Mente Saudável case with validated metrics - 99.4% time reduction (4h15m to 1.5min), 92.4% cost reduction (R$192.50 to R$14.70), +180% monthly ROI turnaround. Includes detailed cost breakdown, optimization strategies, and business impact analysis. Use when evaluating ROI, presenting business case, or validating automation benefits.

Agent system taxonomy (A/B/C/D) based on capabilities - Type A (pure AI), Type B (AI+database context), Type C (AI+web grounding), Type D (AI+database+web). Includes latency/cost analysis, decision tree, healthcare pipeline mapping, and ROI optimization. Use when designing agent architecture, selecting agent type, optimizing costs, or implementing multi-agent workflows.

Complete 5-system healthcare content pipeline for regulated medical content generation. Includes LGPD data extraction (Type B), claims identification (Type A), scientific reference search (Type C), SEO optimization (Type B), and final consolidation (Type D). Validated ROI - 99.4% time reduction, 92.4% cost reduction. Use when implementing healthcare content automation, building regulated medical systems, or optimizing production pipelines.

Overview of Clojure + Google ADK + Vertex AI development environment. Comprehensive lab for building production AI agents using Clojure as primary language, integrating Google ADK via Java SDK and Python libraries via libpython-clj. Includes healthcare pipeline with validated ROI (-99.4% time, -92.4% cost). Use when starting new projects, understanding architecture, or needing general context about the stack.

Context management patterns for multi-source AI agents in Clojure+Vertex AI. Covers 4 context types (static/query/API/previous-result), lifecycle management (load/cache/invalidate), TTL strategies, and LGPD-compliant sensitive data handling. Includes production metrics (58% cost reduction via caching). Use when designing agent contexts, implementing multi-source data integration, optimizing cache strategies, or building LGPD-compliant systems.

Cost optimization strategies for production AI pipelines in Clojure+Vertex AI. Covers multi-model routing (70% Gemini/20% Haiku/10% Sonnet), token optimization (prompt engineering, output constraints), aggressive caching (58% cost reduction), batch processing, and real-time monitoring. Includes production metrics showing $0.391 to $0.162 per pipeline (-58%). Use when optimizing production costs, implementing multi-model strategies, designing budget controls, or scaling to high volume.

Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.

Use when creating maps, working with geographic projections, or processing GeoJSON data. Invoke for world maps, choropleth maps, projection types, geo path generators, spherical geometry, or geographic feature manipulation.

Use when creating interactive visualizations with transitions, animations, drag/zoom/brush behaviors, or DOM manipulation. Invoke for data binding with .join(), animated transitions, interactive behaviors, user input handling, or selection operations.

Create interactive data visualizations using D3.js. Use this when creating charts, graphs, network diagrams, geographic visualizations, or custom SVG-based data visualization.

End-to-end DAG execution orchestrator that decomposes arbitrary tasks into agent graphs and executes them in parallel. The intelligence layer that makes DAG Framework operational.

Parses complex problems into DAG (Directed Acyclic Graph) execution structures. Decomposes tasks into nodes with dependencies, identifies parallelization opportunities, and creates optimal execution plans. Activate on 'build dag', 'create workflow graph', 'decompose task', 'execution graph', 'task graph'. NOT for simple linear tasks or when an existing DAG structure is provided.

Executes DAG waves with controlled parallelism using the Task tool. Manages concurrent agent spawning, resource limits, and execution coordination. Activate on 'execute dag', 'parallel execution', 'concurrent tasks', 'run workflow', 'spawn agents'. NOT for scheduling (use dag-task-scheduler) or building DAGs (use dag-graph-builder).

dagster-orchestration

Expert on building decentralized applications with Shelby Protocol storage on Aptos. Helps with dApp architecture, wallet integration (Petra), browser SDK usage, React/Vue integration, file uploads, content delivery, and building Shelby-powered applications. Triggers on keywords Shelby dApp, build on Shelby, Shelby application, Petra wallet, browser storage, web3 app, decentralized app Shelby, React Shelby, Vue Shelby.

To apply automated Dart fixes, run `dart fix --apply` on the given roots to resolve suggested changes.

Create a new screen in the Multi-site Dashboard with automatic route registration

Мультиаккаунтный дашборд. Статистика по всем аккаунтам с детализацией до уровня объявлений.

Dashboard symbol_signals uses parallel lists (symbols[], signal_values[], gate_statuses[]) not dict keyed by symbol. Trigger when: (1) 'list' object has no attribute 'get', (2) .items() on symbol_signals fails.

Trading dashboard P&L visualization with profit tracker integration, win-rate overlays, R-multiples, and configurable settings

Auto-discover dashboard symbols from loaded RL models. Trigger when: (1) dashboard shows old/wrong symbols, (2) symbols mismatch between live trader and dashboard, (3) adding new models to system, (4) dashboard shows NO_MODEL for all symbols.

Aggregate and merge data from multiple sources including App Store sales, GitHub commits, Skillz events, and more. Use when combining data for reports, dashboards, or analysis.

data-analyst-export

Use this skill when the user needs to analyze, clean, or prepare datasets. Helps with listing columns, detecting data types (text, categorical, ordinal, numeric), identifying data quality issues, and cleaning values that don't fit expected patterns. Invoke when users mention data cleaning, data quality, column analysis, type detection, or preparing datasets.

Clean and standardize vehicle insurance data following established business rules.

Data cleaning, preprocessing, and quality assurance techniques

Explore and analyze pilot data sets to uncover patterns, anomalies, and initial insights. Use when performing ad-hoc data investigations, validating data quality, or preparing exploratory visualizations for hypothesis generation.

Procedures and playbooks for responding to data quality incidents, data loss, corruption, and pipeline failures.

Metabase REST API automation and troubleshooting: authenticate (API key preferred, session fallback), export/upsert questions (cards) and dashboards, standardize visualization_settings, and run/export results.

Build orchestration pipelines with idempotency.

Monitor and troubleshoot dual-pipeline data collection systems on GCP. This skill should be used when checking pipeline health, viewing logs, diagnosing failures, or monitoring long-running operations for data collection workflows. Supports Cloud Run Jobs (batch pipelines) and VM systemd services (real-time streams).

Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s manifests, GitHub Actions workflows, package.json, filter data.

Set up database replication for high availability and disaster recovery. Use when configuring master-slave replication, multi-master setups, or replication monitoring.

Documentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.

Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.

This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.

Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.

Provides expert design guidance for creating truthful, clear, beautiful data visualizations. Focuses on **DESIGN DECISIONS ONLY**—chart selection, color strategy, visual encoding, and validation. Assumes data is accurate and prepared. Auto-activates when user mentions: data viz, dashboard, chart type, visualization, infographic

Build mathematically correct, visually prominent data visualizations for time-series charts. Use this skill when creating charts with mathematical overlays (trendlines, patterns, indicators), fixing visual artifacts (wavy lines, domain mismatches), or validating chart correctness. Focuses on technical correctness and progressive validation, not aesthetic design.

Create visualizations that Seaborn users, Tufte readers, and everyone else will love. Marry NYT Graphics rigor with MoMA aesthetics, Nike energy, and On Kawara precision.

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Role assignment for Claude Agent #1 - Database schema architect for Lead Hunter Prime. Build ONLY database schema (11 tables, RLS policies, seed data). Do NOT build APIs, dashboards, or N8N workflows.

Upload estimation results to Supabase storage and register with Estimator API. Final phase of the estimation workflow.

Expert in creating database diagrams and visual representations. Use when generating ERDs, schema diagrams, or visualizing database relationships with Mermaid.js.

Master database design (SQL, NoSQL), system architecture, API design (REST, GraphQL), and building scalable systems. Learn PostgreSQL, MongoDB, system design patterns, and enterprise architectures.