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Use near the end of a renovation to measure before/after effect and package the result for scale-out — quantify UX, operational, and AI-driven improvements against the baseline captured during discovery, and produce a reusable rollout artifact.
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。
Implement one Azure-backed strangler slice in the target repository after architecture, requirements, Azure review, rollback plan, and touched-seam harnesses are green. Produces tested, observable, reversible code and deployment evidence.
Use before any destructive refactor or UX/backend change to capture the legacy system's current behavior as characterization tests and golden-master fixtures — the safety net that catches regressions. This is the harness gate; it must be green before modernization code begins.
Implement a vetted legacy-web UX slice in the target repository after requirements and touched-seam harnesses are green. Discovers the real frontend stack, changes code test-first, and records implementation evidence before browser validation and review.
Use when starting or resuming a legacy web modernization (UI/UX and/or backend) — routes between renovation phases, tracks state in plan.md, and enforces the harness/test/review gates. Entry point for the harness-driven-renovate workflow.
Use to modernize a legacy backend incrementally using the strangler-fig pattern — route slices of traffic to new event-driven / cloud-native services behind stable seams while the characterization harness guards against regressions. Requires the harness gate to be green.
Use as a mandatory gate before any renovation change is considered done or merged — rigorous review focused on harness regressions, legacy parity, security, and readability. Cross-cutting; invoke on every change. Composes with requesting-code-review / code-review agent.
SOC 職業分類に基づく
| name | impact-measurement |
| description | Use near the end of a renovation to measure before/after effect and package the result for scale-out — quantify UX, operational, and AI-driven improvements against the baseline captured during discovery, and produce a reusable rollout artifact. |
Prove the renovation worked and make it repeatable elsewhere. Compare against the
baseline you captured in legacy-discovery, not against vibes.
## Baseline metrics section of
docs/renovation/01-current-state.md (captured by legacy-discovery) plus its
pain-point and manual-work inventories: screen transitions per task, manual
send-scheduling effort, time-to-detect send failures, operator-inquiry volume,
VM-console checks, statistics-only lag.docs/renovation/08-impact.md — before/after metric table, attribution, user
feedback, and a "how to reproduce this renovation" playbook section.Close the renovation loop: report results to stakeholders and update
renovate-orchestrator state to complete. Surface any newly-discovered pain points
back into a fresh legacy-discovery cycle if further modernization is warranted.
scripts/create-impact-doc.sh <repo-root> <target-name>