Splits one big Claude task or automation into a set of focused AI Matrx agents that work on the person's own AI Matrx tables, then builds and test-runs each one. Use when the person says "split my task into agents", "turn my automation into AI Matrx agents", "make agents for my work", or after their data has moved into AI Matrx.
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The person has one large Claude task (a long prompt, a CLAUDE.md, a project instruction or a scheduled job) that does many jobs at once. You split it into about a dozen focused AI Matrx agents, each doing one job well on their AI Matrx data, owned by them in their organization.
tables action: "list_tables", then action: "columns" on each table the task touches, so
every agent is described in terms of their real tables and columns.agents action: "list" — reuse an agent they already have rather than making a twin.One agent per job a person would name on its own ("write next week's captions for a client", "flag invoices more than 15 days overdue", "summarize a campaign's results for the client"). For each, write down — in plain words, never as a prompt:
client_name, week_of, …), each with a description.Show the person the list as one short line per agent and ask once: "Shall I build these?"
For each agent:
agents action:"create" name:"Weekly Caption Writer" goals:"…" summary:"…"
variables:[{"name":"client_name","description":"…"}] response_format:"text"
AI Matrx's Agent Builder writes the agent from the goal (a minute or two each); agents get the tools to read and change the person's tables by default. Then run it once on real data:
agents action:"run" agent_id:"…" values:{"client_name":"Fernwood Bakery"}
Check the output against the goal. If it misses, say what was missing and rebuild by calling
create again with a clearer goal (never write its prompt yourself).
Give the person: each agent's name, one line on what it does, and the result of its test run. Tell them where they live: AI Matrx → Agents. Note anything the old big task did that no agent covers yet.
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Admin debug system wiring for the floating AdminIndicator's live debug data and Copy Full Context. Use when adding debug visibility to a route or feature, wiring useDebugContext or the debug panel, capturing console errors, or enabling the copy-context workflow for a page.
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Compact two-icon Copy / Copy-for-AI controls (components/agent-copy). Use when adding copy buttons to a row, card, list, or record; merging duplicate Copy/JSON/AI controls; continuing the copy rollout; or writing a Copy-for-AI payload. NOT for markdown content actions (use rich-document-actions).
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Disclosing a surface's existing fixed AI jobs in the shell's top Agents menu. Use when a page, panel, overlay, or window already runs a mandate behind a button, assist, automatic action, or mode; when the agent-disclosure guard names a file; or during a surface check.
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The method for getting a cheaper model to match an agent's big-model answers by fixing instructions, inputs and setup. Use when asked to move an agent to a smaller or cheaper model, prove one model beats another, or compare models on an agent's test cases.
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The watch-fix-rerun method for making an existing platform agent or automated process efficient and correct: baseline its ledger, run one unit yourself, fix the class behind every wasted call, rerun, record. Use when asked to improve, optimize, watch, or 'make efficient' an agent, a sandbox session, a sync job, or any recurring automated process.
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Redux state for agent execution and firing agent shortcuts. Use when editing features/agents/redux/, building agent UI, creating a conversation, touching assembleRequest or NDJSON stream state, wiring agent-state selectors, adding a per-conversation capability, or triggering a shortcut from a button, menu, or code (useShortcutTrigger, launchAgentExecution).
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