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".
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。
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.
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。
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 tooling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。
概要と使いどころ
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".
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。
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."
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。
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.
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。
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.
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。
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.
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。
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.
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。