/learn told the agent to fill the skill `author` field, and the system
prompt environment probe surfaces the OS login name (user=$(whoami) in
prompt_builder.py), so the model wrote the host username into published
SKILL.md frontmatter — a privacy leak the user never opted into, and
inconsistent run to run as the most-salient identity changed.
The /learn authoring prompt now sets `author` to the literal value
`Hermes` and explicitly forbids deriving it from the host environment
(OS/login user, git config, or any probeable identity). The skill names
itself as the tool that wrote it.
Closes#52368.
The /learn authoring prompt taught a subset of the HARDLINE skill rules,
and stated the <=60-char description rule without making the model enforce
it — so generated descriptions overshot (up to 202 chars), which the
60-char system-prompt skill index then silently truncates.
- description: add the index-truncation rationale, a count-and-trim
self-check, and a good/bad length example so the model actually hits <=60.
- add platforms-gating rule (OS-bound primitives -> declare platforms:).
- add author-credits-human-first rule.
- round out the Hermes-tool framing with the full wrapped-tool mapping and
references/templates layout.
Closes#52367.
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.
