一键导入
repo-onboarding
Auto-discoverable wrapper for `.hforge/library/skills/repo-onboarding/SKILL.md`.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Auto-discoverable wrapper for `.hforge/library/skills/repo-onboarding/SKILL.md`.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | repo-onboarding |
| description | Auto-discoverable wrapper for `.hforge/library/skills/repo-onboarding/SKILL.md`. |
.hforge/library/skills/repo-onboarding/SKILL.md.hforge/library/skills/repo-onboarding/references/.hforge/library/docs/authoring/enhanced-skill-import.md for import provenance, research summary, and validation notes behind the upgraded packRESEARCH-SOURCES.md for the pack-level research summaryVALIDATION.md for the pack-level validation notesUse the canonical skill under .hforge/library/skills/ for execution. Treat this wrapper as a discovery entrypoint only, and use the provenance doc when you need maintainer-facing context about why the onboarding guidance was deepened.
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.