Use when generating a single installable agent that should keep learning, track sources, refresh research, propose repairs, or improve itself over time without becoming a multi-agent team.
docs/builder-interview-research-gate.md before generation: ask an
8-12 question first batch, research official sources, similar agent
repositories or comparables, academic/professional theory, and plugin docs,
compare tool/plugin choices, and write the domain-expert synthesis plus
prompt-performance contract before creating the worker prompt..agentlas/memory-map.json;.agentlas/vault-references.json;docs/builder-interview.md, docs/research-sources.md,
docs/tool-selection.md, docs/domain-expert-synthesis.md,
docs/prompt-performance-contract.md, and
.agentlas/capability-eval-plan.json unless explicitly creating a minimal
private scaffold..agentlas/global-commands.json and one public global command for the
worker across Claude Code, Codex, Gemini CLI, generic AGENTS.md, and
terminal adapters.Return agent_package, skills, memory_contract, refresh_loop,
approval_gate, global_commands, and verification.
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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.