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Point Hermes at directories of source material (code, API docs, manuals, PDFs, configs) and it distills a reusable skill: ingest + classify -> draft SKILL.md via the main model -> sandboxed verification -> commit only when the verification tier meets the floor. Surfaces (all call the shared agent/skill_distill.py engine): - CLI: `hermes learn <dirs> [--hint --category --run --min-tier --json]` - In-session /learn slash command (CLI + TUI + every messaging platform) - Dashboard: Skills tab 'Learn from sources' dialog + /api/skills/learn endpoint Verification is an honest tier (executed / checked / unverified / failed), stamped into the skill frontmatter; never claims 'tested' when only parsed. --run executes only allowlisted read-only snippets in a throwaway temp dir, and is admin-gated over the gateway. Zero new model tools (footprint ladder rung 2). Synthesis uses call_llm(task='skill_distill') so it's main-model-first and cache-safe. 13 targeted engine tests; live-tested CLI + gateway end-to-end (reached the 'executed' tier in a sandbox). |
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