Use when Spark engineering knowledge must be placed, deduplicated, migrated, or validated across AGENTS, Notes, Roles, Skills, and Workflows.
日本語の概要は準備中です。原文の説明を表示しています。
zendev-lab/spark22026年10月11日 更新
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spark-feature-planning
Use when a Spark feature needs repository-first research, explicit option selection, and an owner-aligned implementation plan before code changes.
含まれるファイル（1）
- SKILL.md1.7 KB
SKILL.md（原文）
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Spark feature planning
Turn a requested capability into an evidence-backed decision and the smallest plan that can prove it.
Procedure
- Reconstruct the user problem and current behavior from the owning code, specifications, package README, nearby tests, and machine-readable inventory.
- Separate repository facts, external facts, assumptions, constraints, and unresolved product decisions. Research only questions whose answers can change the selection or acceptance boundary; prefer primary sources for external claims.
- Compare the status quo and viable options against owner fit, semantic duplication, dependency direction, compatibility cost, lifecycle and failure modes, operational burden, and validation cost.
- Select one option only when the evidence and confirmed constraints distinguish it. Explain why rejected options lose without inventing future requirements.
- Produce a dependency-ordered plan whose first slice proves the highest-risk behavior. Name the owner, affected surfaces, observable acceptance criteria, failure cases, compatibility evidence, documentation owner, and validation commands.
- Keep adjacent cleanup and speculative extensibility out of the feature plan. Record them as rejected or out of scope rather than prebuilding them.
Stop with needs-decision when a missing product choice changes user-visible
behavior, authority, ownership, or irreversible cost. Return problemEvidence,
options, selection, plan, acceptanceCriteria, risks, outOfScope,
validationCommands, openQuestions, and verdict.
レビュー
まだレビューはありません。使ってみた感想をお寄せください。
同じリポジトリのスキル
概要と使いどころ
Use when adding or updating Spark AI provider models or their compact and preflight metrics, especially baidu-oneapi, Grok, Claude, GPT, DeepSeek, OneAPI, context windows, output budgets, prices, thinking maps, or transports.
日本語の概要は準備中です。原文の説明を表示しています。
zendev-lab/spark22026年10月11日 更新
Use when a Spark change needs its authoritative owner, affected boundaries, risks, acceptance criteria, and smallest verifiable slice established before implementation or review.
日本語の概要は準備中です。原文の説明を表示しています。
zendev-lab/spark22026年10月11日 更新
Use when a Spark implementation needs an evidence-based review for correctness, ownership, compatibility, failure handling, maintainability, and unnecessary complexity.
日本語の概要は準備中です。原文の説明を表示しています。
zendev-lab/spark22026年10月11日 更新
Use when a working Spark implementation should be simplified without changing its observed behavior, ownership, compatibility, or public contract.
日本語の概要は準備中です。原文の説明を表示しています。
zendev-lab/spark22026年10月11日 更新
Use when a running Spark or DSH web service needs real-browser verification — page load, client-plugin boot errors, console errors, failed requests, or onboarding/dialog rendering — with the repository's own Playwright chromium.
日本語の概要は準備中です。原文の説明を表示しています。
zendev-lab/spark22026年10月11日 更新