Skill UI

This skill performs a comprehensive, setup-wide health check for agentic developers. It audits various components—including skills, memory, tools, configuration, and workflow habits—to identify inefficiencies and potential improvements. It provides a detailed report and guides the user through applying targeted fixes, ensuring the agent environment remains lean and optimized.

SkillOpt-Sleep enables an AI agent to undergo a simulated "sleep cycle" for continuous, measurable self-improvement. It harvests past sessions, mines recurring tasks, and re-runs them offline. It then consolidates findings into the agent's memory and skills, ensuring only validated improvements are adopted. This process maximizes long-term competence from short-term interactions.

Reconcile code, docs, rules, memory, and workspace residue so the next session starts from one current source of truth. Best for project handoff, stale documentation, and closing out knowledge drift.

This tool generates comprehensive marketing intelligence briefings by synthesizing accumulated knowledge across all brand agents and channels. It surfaces cross-agent patterns, confidence scores, and actionable playbooks, transforming raw data into strategic advantages. Use it for quarterly planning, strategy reviews, knowledge gap analysis, or onboarding new team members to maximize decision-making confidence.

This skill facilitates the persistent storage of critical brand knowledge, including campaign learnings, brand guidelines, and competitive intelligence, into a specialized memory layer (such as a vector database). It automatically indexes content, deduplicates entries via hashing, and applies rich metadata tagging, ensuring that valuable institutional knowledge is preserved and retrievable through natural language search across different sessions.

This tool batches syncs all accumulated session learnings, campaign insights, and performance data to a persistent memory layer. It ensures valuable knowledge is automatically retained across multiple work sessions without manual saving. It supports incremental syncing based on checkpoints, making it fast and safe for continuous knowledge capture.

A comprehensive tool for distributing marketing tasks to team members. It intelligently matches tasks based on roles, channel expertise, regional alignment, and current capacity. Features include assigning new tasks with smart matching, viewing assignments, and generating workload dashboards to prevent resource bottlenecks and skill mismatches.

The Unified Memory system provides a durable, inspectable context layer for sharing state and handoffs between multiple autonomous agents (harnesses) like Claude, Codex, and Hermes. It allows agents to save work state, transfer context, resume tasks, and search shared project knowledge across different scopes (project, team, user).

A comprehensive guide for authors submitting revisions to top-tier academic journals, such as AJPS. It details the strategy, structure, and specific constraints required for the response memorandum following a Revise & Resubmit (R&R) decision. The guide emphasizes addressing every reviewer comment, reconciling conflicting demands, maintaining double-blind anonymity, and ensuring all new analyses are fully reproducible for third-party verification.

This guide provides detailed protocols for achieving high levels of reproducibility in Computer Aided Verification (CAV) research. It emphasizes the necessity of pinning benchmark sets, recording exact tool versions, documenting resource limits (time/memory), and providing checkable proof witnesses to ensure that evaluation claims can be independently validated by competitors or reviewers.

This skill utilizes a configured graph database (Codebase Memory) to accelerate code discovery, understand system architecture, perform symbol lookups, and trace dependencies (callers, callees, data flow). It is crucial for navigating unfamiliar or complex codebases, providing insights into impact analysis and module relationships. Always confirm graph findings with local source code or files.

This guide provides a comprehensive framework for revising Communication Research (CR) manuscripts following a revise-and-resubmit (R&R). It addresses the core challenge of satisfying two independent, often skeptical, expert reviewers simultaneously—specifically concerning measurement validity, tested mechanisms, and confound control—all while adhering to strict page limits. It focuses on strategic revision planning, managing the page budget, and structuring the response memo.