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meta-build

Iteratively build an application or game phase-by-phase in clean subagents, reviewing each phase and refining the build-* skills / rules until each is optimal, then summarizing.

معلومات المصدر

المستودع
adobe/data
آخر نشاط في المصدر
٢٩ يوليو ٢٠٢٦ في ٢٠:٢٠
لغة SKILL.md المكتشفة
الإنجليزية
النجوم
١٢
التفرعات
٨

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

عرض SKILL.md

SKILL.md
تعليمات المصدر · معاينة للقراءة فقط
name
meta-build
description
Iteratively build an application or game phase-by-phase in clean subagents, reviewing each phase and refining the build-* skills / rules until each is optimal, then summarizing.
Purpose: exercise and **polish the build-* skills and the rules they reference**, using a concrete app/game prompt as the test case. You are improving the *general* guidance, not the one-off output. ## Procedure 1. Resolve `target` to its **ordered phases**. A composite target expands to its pipe — `build-feature` → its `/build-*` layer steps; `build-application` / `build-game` → `build-feature` for the base and each peer, then `build-app-entry`. A single `build-<phase>` target is just that one phase. 2. For each phase, in order: a. **Run it in a fresh subagent** with constrained context (`/x-subagent`): give the subagent only the phase skill (`/<phase>`), the app/game prompt, and the files on disk from prior phases — nothing about this loop or earlier attempts, so every re-run differs only by any edits you made to the guidance. b. **Review** the phase output in a subagent with `/review`. c. If `/review` is not OPTIMAL: fix the **root cause in the guidance** — edit the phase skill or a rule it references. Keep every edit **concise and general**: improve it for all apps, never encode anything specific to this prompt. (For a pure `code` slip with sound guidance, just re-run.) Do **not** hand-patch the generated code. d. Re-run from (a), up to `maxIterations` (default 4) attempts. Move to the next phase once it reviews OPTIMAL — or the cap is hit (record that it did not converge). e. Record: the phase, its iteration count, and every skill/rule file you edited. 3. **Final review** (read-only): run `/review` over the whole product. **Change nothing.** Then summarize: - per-phase iteration counts, - which phase skills and which rules were edited, and why, - overall outcome (which phases converged, which hit the cap). Every phase runs in its own constrained subagent so re-executions during polishing stay consistent — the edited skill/rule is the only variable between a phase's attempts.
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