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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 254% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 153% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 57% | 0% |
docs/builder-interview-research-gate.md before generation: ask an8-12 question first batch, research the domain, compare tool/plugin choices, and write the prompt-performance contract before creating the worker prompt.
.agentlas/memory-map.json;.agentlas/vault-references.json;watchlist memory section, references, and optional scheduled workflow.
docs/builder-interview.md, docs/research-sources.md,docs/tool-selection.md, docs/prompt-performance-contract.md, and .agentlas/capability-eval-plan.json unless explicitly creating a minimal private scaffold.
for human approval before changing tools, connectors, secrets, or core instructions.
.agentlas/global-commands.json and one public global command for theworker 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.
Other measured skills in the registry, with their headline benchmark lift.