القائمة
Verify Zombie Roguelike changes by running the TypeScript build and checking the dev server log for errors. Use before claiming a task done.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | verify |
| description | Verify Zombie Roguelike changes by running the TypeScript build and checking the dev server log for errors. Use before claiming a task done. |
Run from the project root (D:\obsidian\OrbitOS\20_Project\Game\Zombie Roguelike).
cd app && npm run build
This runs tsc (strict, noUnusedLocals, noUnusedParameters) and vite build. If it fails, fix the reported errors before continuing — dead imports and unused parameters count as failures.
Start the dev server in the background and wait ~5 seconds for Vite to finish the initial module graph:
cd app && npm run dev
Tail the log. Acceptable output includes VITE v5.x.x ready in <ms> and the local URL line. Fail the check if you see:
[vite] Internal server error / Pre-transform errorSyntaxError, TypeError, or ReferenceError at bootFailed to resolve import "…")Stop the dev server after the check.
Tell the user:
npm run build passed or failed (paste the first error line if failed)Do not mark the task done if either step failed.
Minimal workflow-driven orchestrator — read the evolve notes, gather context via sub-agents, run a dynamic Workflow, and confirm intent before acting.
Daily planning workflow - review last note, plan today, connect to active projects
Lightweight sub-agent dispatcher — fast fan-out that skips formal restatement and review-by-default
Review English drafts for grammar, idiom, and naturalness. Correct inline with terse explanations.
Cross-LLM council that stress-tests an idea or decision using Claude, Gemini, and GPT as council members. User relays prompts to Gemini and GPT. Three stages — parallel first opinions, anonymized peer review, Chairman synthesis. Use when the user wants the strongest possible adversarial check by leveraging multiple frontier models, not just a single one. Inspired by karpathy/llm-council.
Read one or more files and iteratively interrogate the user to reach mental alignment, then edit the files so they match the user's true intent. Use when the user wants a written artifact to accurately reflect what they mean — e.g. "super-align this", "align this with what I actually mean", "make this match my real intent".