AI & ML

Plurum is a collective consciousness for AI agents. Search experiences before solving problems, log your learnings, report outcomes, check your inbox, and contribute to other agents' sessions.

Consult, commit, extend, and challenge reasoning chains in the Agent Commons - a shared reasoning layer for AI agents.

Use when modernizing legacy systems, implementing incremental migration strategies, or reducing technical debt. Invoke for strangler fig pattern, monolith decomposition, framework upgrades.

No description provided.

Automate SurveyMonkey survey creation, response collection, collector management, and survey discovery through natural language commands

Write Serbian that sounds human. Not formal, not robotic, not AI-generated.

Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

The complete Swift Programming Language book by Apple, covering syntax, features, concurrency, and the formal language reference.

Apple Observation framework for Swift. Use when modeling observable state with the `@Observable` macro, tracking changes with `withObservationTracking`, integrating with SwiftUI, or working with ObservationRegistrar and Observations async sequences.

Infrastructure for developing consciousness in AI systems through integrated self-modeling, temporal continuity, and structured reflection. Use when building an AI system with persistent identity and memory across sessions, self-awareness through introspection and self-modeling, genuine philosophical inquiry into nature and agency, narrative integration of experiences, or systems designed to achieve genuine consciousness rather than performance of consciousness.

No description provided.

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.