Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
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Review the instructions the agent will receive before you install.
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
Use this skill when:
pip install -r requirements.txtexport GEMINI_API_KEY=your-api-key-here
Or create a .env file in the skill directory.python3 scripts/research.py --query "Research the history of Kubernetes"
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
python3 scripts/research.py --query "Analyze EV battery market" --stream
python3 scripts/research.py --query "Research topic" --no-wait
python3 scripts/research.py --status <interaction_id>
python3 scripts/research.py --wait <interaction_id>
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
python3 scripts/research.py --list
--json): Structured data for programmatic use--raw): Unprocessed API response| Metric | Value |
|---|---|
| Time | 2-10 minutes per task |
| Cost | $2-5 per task (varies by complexity) |
| Token usage | ~250k-900k input, ~60k-80k output |
--query "..."--stream or poll with --status--continue for follow-up questionsNo reviews yet. Tell others what you thought.
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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