DonchitosandClaude Opus 5.5
be8993bbc5
Fix release for 1.1.2: the 14 longest skills keep their later phases after a compaction, a question nobody answered is never taken as an answer, settings read the same from a subfolder, the commit check works for a project in a subfolder of a larger repository, every skill opens with a title, and the shipped allow rules no longer approve deleting a branch or writing files without asking. Details in CHANGELOG.md; upgrade steps in UPGRADING.md (v1.1.2 → v1.1.3). Refs #83 Refs #133 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0185en4t76fPn7315q4c3nko
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/setup-engine — Section 6: Determine Knowledge Gap
Part of
/setup-engine.SKILL.mdnames when to read this file; the settings it resolved and the rules under its later headings still apply here.
6. Determine Knowledge Gap
Check whether the engine version is likely beyond the LLM's training data.
Known approximate coverage — the same figures the shipped
docs/engine-reference/*/VERSION.md files record:
- LLM knowledge cutoff: May 2025
- Godot: training data likely covers up to ~4.3
- Unity: training data likely covers up to ~2022 LTS (2022.3)
- Unreal: training data likely covers up to ~5.3
This table goes stale silently, and a stale table fails in the dangerous direction only if it is too new. A cutoff left unchanged for a year after it stopped being true over-classifies risk — safe, but every project then pays for full reference docs it may not have needed. The opposite error is the harmful one: a cutoff claimed later than the model's actual one marks post-cutoff versions LOW RISK and suppresses the reference docs that exist to stop invented APIs.
If the model running this skill states an earlier training cutoff than the one above, do not trust the table. Say so, and treat the engine version as
HIGH RISKregardless of the comparison — a cutoff the model does not have is not a cutoff that was met.
Compare the user's chosen version against these baselines:
- Within training data →
LOW RISK— reference docs optional but recommended - Near the edge →
MEDIUM RISK— reference docs recommended - Beyond training data →
HIGH RISK— reference docs required
Inform the user which category they're in and why.