This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Translate product specs (PRDs, user stories) into a ship-ready repo plan: ticket decomposition, branch strategy, and PR sequencing. Use when breaking a PRD into tickets or designing the branch/PR sequence.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
A delivery-focused skill that bridges product spec to repository work. Where PRD-writing skills focus on what to build, this skill focuses on how to break it down for execution — the ticket decomposition, branch strategy, PR sequencing, and acceptance criteria that make a spec actually ship.
Before generating the repo plan, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
prd_to_tickets_decomposer.py with the user stories + technical
notes to surface a candidate ticket tree (epic → tickets → subtasks)
with size estimates and dependencies.python3 spec-to-repo/scripts/prd_to_tickets_decomposer.py \
--input prd.json --format markdown
pr_scope_analyzer.py to flag oversized PRs, missing tests,
missing telemetry, and risky merges.python3 spec-to-repo/scripts/pr_scope_analyzer.py \
--input pr_plan.json --format markdown
git branch --list).branch_naming_validator.py to flag non-conformant names.python3 spec-to-repo/scripts/branch_naming_validator.py \
--input branches.txt --format markdown
| Size | Effort | Description |
|---|---|---|
| XS | < 0.5 day | Trivial; usually skip ticketing |
| S | 0.5–1 day | One simple change |
| M | 1–3 days | Single feature, well-scoped |
| L | 3–5 days | Multi-day work; should split if possible |
| XL | > 5 days | Always split — too big for confident estimate |
A ticket that's L or XL almost always hides a missing decomposition. Push back on yourself.
Epic — large product feature ("Notifications v2")
├── Story — user-facing capability ("As a user I can mute by channel")
│ ├── Ticket — one engineering work item (backend, frontend, infra)
│ │ └── Subtask — atomic step (optional)
Most orgs:
Best ticket: ships a small user-visible improvement end-to-end.
When you can't slice vertically (e.g., backend is weeks before frontend):
For a multi-PR feature:
Each PR < 400 lines if possible. Reviewability collapses above 400.
Per ticket:
Per epic:
references/spec-to-ticket-decomposition.md — patterns for breaking specs into ticketsreferences/branch-strategy-for-features.md — branching, feature flags, dark-launchreferences/pr-discipline-and-conventions.md — PR size, review, definition-of-doneproduct-team/agile-product-owner — sprint planning, prioritizationengineering/feature-flags-architect — flag strategyengineering/observability-designer — SLO / telemetryc-level-advisor/vpe-advisor — broader delivery contextproject-management/ skills — ticket / sprint management tooling아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.