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parallel-worktree-supervisor
Auto-discoverable wrapper for `.hforge/library/skills/parallel-worktree-supervisor/SKILL.md`.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Auto-discoverable wrapper for `.hforge/library/skills/parallel-worktree-supervisor/SKILL.md`.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | parallel-worktree-supervisor |
| description | Auto-discoverable wrapper for `.hforge/library/skills/parallel-worktree-supervisor/SKILL.md`. |
.hforge/library/skills/parallel-worktree-supervisor/SKILL.md.hforge/library/skills/parallel-worktree-supervisor/references/.hforge/library/docs/authoring/enhanced-skill-import.md for the provenance behind the imported worktree and stacked-diff guidanceRESEARCH-SOURCES.md for the pack-level research summaryVALIDATION.md for the pack-level validation notesUse the canonical .hforge/library/skills/parallel-worktree-supervisor/ surface for planning and supervision. This wrapper is the discovery layer that routes agents to the project-owned flow.
Auto-discoverable wrapper for `.hforge/library/skills/bug-investigation/SKILL.md`.
Auto-discoverable wrapper for `.hforge/library/skills/double-diamond-feature/SKILL.md`.
Use for defects, regressions, outages, flaky tests, and unexpected behavior. Guides the agent through triage, containment, reproduction, hypotheses, root-cause evidence, fix verification, and recurrence prevention. Do not use as the primary workflow for new features; use double-diamond-feature instead.
Use for meaningful feature development before coding. Guides the agent through Discover, Define, Develop, and Deliver with evidence, options, validation, and rollback notes. Do not use for bugs, regressions, outages, or flaky tests; use bug-investigation instead.
Auto-discoverable wrapper for `.hforge/library/skills/complex-task-protocol/SKILL.md`.
Automatic operating protocol for complex agent tasks. Use when work is multi-step, risky, cross-module, orchestration-heavy, or likely to benefit from bounded sidecar help, verification discipline, recovery checkpoints, and durable learning capture.