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deterministically execute create_skill from create_category, execute skill after creation

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Denton Social
2026-09-24 02:31:12 -05:00
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@@ -41,7 +41,7 @@ All decisions are SemIf calls: `{state, question, options[]}`. State is the curr
- Structure: categories → skills → actions. Top level listed at each level.
- Navigation is a chain of SemIf choices, one per level, descending until a leaf skill matches.
- Navigation offers a `create_category` suggestion at the category level and a `create_skill` suggestion at the leaf level. Both are live: the decision model, driven in normal generation mode, authors a title + description (broad bucket for a category, single specific action for a skill), and the stub is persisted to the category registry and merged into the running tree. For a new skill the stub is then promoted to a runnable body: a separate, larger OpenAI-compatible model writes the `predict`/`act` code against the `SKILL.md` contract, persisted under `data/skills/` and hot-loaded, and the request re-dispatches to the new leaf.
- Navigation offers a `create_category` suggestion at the category level and a `create_skill` suggestion at the leaf level. Both are live: the decision model, driven in normal generation mode, authors a title + description (broad bucket for a category, single specific action for a skill), and the stub is persisted to the category registry and merged into the running tree. For a new skill the stub is then promoted to a runnable body: a separate, larger OpenAI-compatible model writes the `predict`/`act` code against the `SKILL.md` contract, persisted under `data/skills/` and hot-loaded. The newly created leaf is then executed directly (no re-dispatch through navigation) so the request that prompted creation is answered: `create_category` → `create_skill` → run, or `create_skill` → run.
### Skill manifest
- name, category, description, allowed inputs, action list, cost budget, decision log reference.