Manage Bernstein agents — list active agents, inspect their output, kill stalled agents, or stream live logs. Use when the user asks about agents, wants to see what an agent is doing, or needs to kill one. --- # Bernstein Agent Management Inspect, monitor, and control active Bernstein agents. ## When to Use - User asks "what agents are running?" or "show me the agents" - User wants to see what a specific agent is working on - User says "kill that agent" or "stop the backend agent" - User asks "why is that agent stuck?" or wants to inspect agent output - User wants to see agent logs ## Instructions ### List agents 1. Run `scripts/agents.sh list` to get all active agents. 2. Present them clearly: ``` ## Active Agents (3) | Agent | Role | Model | Status | Task | Runtime | Cost | |-------|------|-------|--------|------|---------|------| | ses-a1b2 | backend | claude-sonnet-4 | alive | TASK-042: Fix auth | 4m 12s | $0.32 | | ses-c3d4 | qa | gpt-4.1 | alive | TASK-043: Write tests | 2m 45s | $0.18 | | ses-e5f6 | frontend | claude-sonnet-4 | stalled | TASK-044: Update UI | 8m 03s | $0.51 | ``` ### Inspect agent 3. To see what an agent is doing: `scripts/agents.sh logs <session_id>` 4. Show the last ~20 lines of output. ### Kill agent 5. To kill a stalled or misbehaving agent: `scripts/agents.sh kill <session_id>` 6. Confirm: "Agent ses-e5f6 terminated. Task TASK-044 returned to open queue." ### Stall detection 7. If any agent shows `stalled` status, proactively suggest killing it. 8. An agent is stalled if it hasn't sent a heartbeat in >60 seconds.
Guide for creating and configuring AI agents in the DEVS platform. Use this when asked to create new agents, modify agent behavior, or work with agent YAML/JSON files.
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.
End-to-end workflow to create a coded Business Central agent using the Agent SDK. Follows the official Agent Template project structure. Generates all required objects with correct interface signatures. Use when creating a new BC agent.
End-to-end workflow to create a coded Business Central agent using the Agent SDK. Follows the official Agent Template project structure. Generates all required objects with correct interface signatures. Use when creating a new BC agent.
- 📄 agent.md
- 📄 being.md
- 📄 browser.md
The Skill plugin gives the agent a persistent, structured knowledge base. Knowledge is organised into named **skills**, each containing any number of **items** — individual pieces of information. Items are indexed semantically, so the agent can search across all skills by meaning rather than exact keywords.
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Architects, generates, and validates Agent Skills. Enforces specification and best practices. Used any time an agent skill must be created or updated.
Guide for creating optimized Claude Code agents. Use to design a new specialized agent, a swarm sub-agent, or a multi-agent workflow. Follows the Skill → Agent (preloaded skills) → Skill orchestration pattern.
- 📁 agents/
- 📁 assets/
- 📁 references/
- 📄 SKILL.md
End-to-end GECX/CXAS/CES conversational agent lifecycle -- build agents from requirements (PRD-to-agent), create and run evals (goldens, simulations, tool tests, callback tests), debug failures, and iterate to production quality. Use this skill whenever the user mentions GECX, CXAS, CES, SCRAPI, conversational agents, voice agents, audio agents, agent evals, pushing/pulling/linting agents, or agent instructions/callbacks/tools on the Google Customer Engagement Suite platform.
Use Microsoft Agent Lightning to trace, optimize, and evaluate agent performance.
- 📁 cmd/
- 📁 docker/
- 📁 docs/
- 📄 .gitignore
- 📄 CHANGELOG.md
- 📄 go.mod
Manage AI agent teams with agencycli — a CLI tool for organising AI agents (Claude Code, Codex, Gemini, Cursor, etc.) into hierarchical teams (Agency → Team → Role → Project → Agent). Key capabilities: create agencies and teams, hire agents with merged context layers, assign and run tasks with priority queues, manage autonomous playbooks (wakeup.md), send async inbox messages between human and agents, configure heartbeat schedules and cron jobs, run agents inside Docker sandboxes, forward/confirm tasks via inbox, manage templates, and more. Use this skill whenever you need to: create or manage an agencycli workspace, hire/fire/sync agents, add/run/cancel tasks, check inbox confirmations, send messages, configure heartbeats or crons, start the scheduler, or work with agency templates.
