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.
ソース情報
- リポジトリ
- adobe/data
- ソースの最終更新活動
- 2026年7月24日 01:18
- 検出された SKILL.md の言語
- 英語
- スター
- 12
- フォーク
- 8
インストール方法
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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SKILL.md を表示中
SKILL.md
ソースの指示 · 読み取り専用プレビュー- 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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