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Open-ended skill learning across every surface. /learn <free text> takes a description of any source — a directory, a URL, the workflow you just walked the agent through, or pasted notes — and the live agent gathers it with the tools it already has (read_file/search_files, web_extract, the conversation, the pasted text), then authors a SKILL.md via skill_manage following the house authoring standards (<=60-char description, the standard section order, Hermes-tool framing, no invented commands). No engine, no model-tool footprint, works on any terminal backend (local, Docker, remote): /learn builds a standards-guided prompt and hands it to the agent as a normal turn. - agent/learn_prompt.py: shared standards-guided prompt builder - /learn registry entry (both surfaces) + CLI handler (inject onto input queue) + gateway handler (rewrite turn, fall through, /blueprint pattern) - tui_gateway command.dispatch returns a send directive -> TUI + dashboard chat - dashboard Skills page 'Learn a skill' panel (dir + URL + open-ended text) composes a /learn request and runs it in chat - docs (slash-commands ref + skills feature page), 11 targeted tests Inspired by OpenAI Codex's Record & Replay and the /learn concept from #47234 (dir-distillation engine); reworked to be open-ended and engine-free per review. |
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