auto-dream
Background memory consolidation and learning graduation — overnight knowledge lifecycle.
Background memory consolidation and learning graduation — overnight knowledge lifecycle.
Security threat model: scan toolkit for attack surface, supply-chain risks.
Generate growth strategies and ready-to-publish marketing content for product growth. Use when planning launches, assessing product-market fit, creating marketing content, designing experiments, optimizing activation/onboarding, or developing acquisition strategies. Incorporates proven frameworks from Sean Ellis (PMF testing, ICE prioritization) and Nikita Bier (viral consumer growth, activation optimization). Covers product-market fit assessment, activation optimization, viral loops, SEO, email marketing, social media, landing pages, and growth experiments.
File-based message queue for inter-agent coordination. Used by workers AND board directors to communicate. Provides: progress updates, task completion signals, file locking, board deliberation. Core infrastructure for parallel execution.
Parallel execution engine for dispatching worker agents. Used by conductor-orchestrator to spawn multiple workers simultaneously from DAG parallel groups. Handles dispatch, monitoring, aggregation, and failure recovery.
Execute ES|QL (Elasticsearch Query Language) queries, use when the user wants to query Elasticsearch data, analyze logs, aggregate metrics, explore data, or create charts and dashboards from ES|QL results.
Assists with creating complete ITBench scenarios by applying fault mechanisms to specific services, populating scenario files, and generating groundtruth DSL with fault propagations and alert predictions.
Salesforce data operations with 130-point scoring. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, or needs data factory patterns for Apex tests. DO NOT TRIGGER when: SOQL query writing only (use sf-soql), Apex test execution (use sf-testing), or metadata deployment (use sf-deploy).
Salesforce Data Cloud Prepare phase. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks about ingestion into Data Cloud. DO NOT TRIGGER when: the task is connection setup only (use sf-datacloud-connect), DMOs and identity resolution (use sf-datacloud-harmonize), or query/search work (use sf-datacloud-retrieve).
Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
OmniStudio Data Mapper (formerly DataRaptor) creation and validation with 100-point scoring. Use when building Extract, Transform, Load, or Turbo Extract Data Mappers, mapping Salesforce object fields, or reviewing existing Data Mapper configurations. TRIGGER when: user creates Data Mappers, configures field mappings, works with OmniDataTransform metadata, or asks about DataRaptor/Data Mapper patterns. DO NOT TRIGGER when: building Integration Procedures (use sf-industry-commoncore-integration-procedure), authoring OmniScripts (use sf-industry-commoncore-omniscript), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
Use when creating a new walkerOS transformer. Example-driven workflow for validation, enrichment, or redaction transformers.
Use when working with transformers, understanding event validation/enrichment/redaction, or learning about transformer chaining. Covers interface, return values, and pipeline integration.
Use when transforming events at any point in the flow (source→collector or collector→destination), configuring data/map/loop/condition, or understanding value extraction. Covers all mapping strategies.
Run IV, DiD, and RDD analyses in R with proper diagnostics
Run regression analyses in Stata with publication-ready output tables.
Panel data analysis with Python using linearmodels and pandas.
Clean and transform messy data in Stata with reproducible workflows
Generate publication-ready regression tables in LaTeX.
Query ClickHouse system tables to inspect query logs, monitor cluster health, check replication status, and analyze slow queries. Use when the user mentions "system tables", "query_log", "ClickHouse monitoring", "cluster status", "slow queries", or asks to diagnose ClickHouse operational issues.
Expert system for generating, validating, and optimizing ClickHouse SQL. Use this when the user needs data, queries, or analysis.
OpenClaw-native domain cascading. Use when users need cost/latency reduction via cascading, domain-aware model assignment, OpenClaw-native event handling, and command setup including /model cflow and optional /cascade stats commands.