jnMetaCode/agency-orchestrator
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Triggered when user asks to run a YAML workflow or multi-role collaboration.
100 tagged multi agent, measured the same way as everything else here.
Browse within: fastapi 29electron 24desktop 23institutional-memory 21Autonomous Agents 13ai-inference 13autonomous-ai 13mathematics 13theoretical-physics 13langgraph 8Orchestration 7agent-engineering 6agentic-workflows 6coding-agents 6
jnMetaCode/agency-orchestrator
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Triggered when user asks to run a YAML workflow or multi-role collaboration.
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Guidelines for developing pydantic-ai agents, tools, and handlers.
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Python code style and recurring patterns (config, logging, errors, paths).
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Core project architecture, patterns, and conventions for the AI Documentation Generator.
CopilotKit/open-multi-agent-canvas
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Cursor rule "cursorrules" from CopilotKit/open-multi-agent-canvas, covering instructions, additional command options and notes.
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A pointer to the main rules for Clutch, a desktop application for coordinating and supervising multiple AI agents. The full instructions are kept in a file named CLAUDE.md.
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When adding or changing Agent CLI whitelist entries, update code and docs together (D19).
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Hybrid deployment contract — genericmode, hosted sandbox, proxy auth, web-team boundary, updater policy.
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Compiler architecture, paper construction phases, validation, RAG, and Lean rigor flow.
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Autonomous research tiers, brainstorm-to-paper workflow, Tier 3, proof checks, and persistence.
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Rules for investigating product AI failures through conversation logs. A trace ID is a 32-character event identifier, while a conversation ID is a UUID that identifies a session.
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An architecture rule for running one product interface across desktop, narrow screens, and a mobile app shell. A mobile shell is the native wrapper around the shared web interface; narrow screens remove capabilities that cannot work there.
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A deployment rulebook for a product with a backend, website, mobile apps, desktop apps, and an admin panel. It defines release order, checks, credential handling, and platform-specific publishing steps.
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Run a full-spectrum, multi-agent audit covering every layer of the application: architecture, implementation, testing, security, frontend/UX, product alignment, and blind code review. All rounds run in parallel — contamination is prevented by instruction, not by timing. Phase 2 begins only after all active rounds…
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Eva reads this file at pipeline start for detailed operational procedures.
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Loads automatically when Eva reads {pipelinestatedir}/ files. Contains ALWAYS-loaded content: mandatory gates, brain capture model, pipeline flow, agent standards, terminal transition. JIT sections load from sibling reference files on the triggers listed below.
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Gate AI done/shipped/fixed claims on ground truth from the DOS kernel (dos verify, dos commit-audit) — git ancestry and the commit's own diff, never the agent's own narration.
Emily2040/rapid-domain-mastery
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Use the rapid-domain-mastery workflow for source-heavy learning, literature onboarding, qualifying exam prep, and deep diagnostic tutoring.
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Core ControlFlow identity, source hierarchy, and offline validations for the repository.
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Guidelines for implementation, testing, review, and governance alignment.
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ControlFlow orchestration: state machine, tier routing, Task delegation to controlflow subagents.
Mnemosyne-OS/Mnemosyne-Neural-OS
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You are an AI agent building the module: {{MODULENAME}} This module is part of the Mnemosyne Neural OS ecosystem by XPACEGEMS LLC.
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Env setup for command execution.
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Repository rules for every coding agent.