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概要と使いどころ
When the user wants to plan a product launch, feature announcement, or release strategy. Also use when the user mentions 'launch,' 'Product Hunt,' 'feature release,' 'announcement,' 'go-to-market,' 'beta launch,' 'early access,' 'waitlist,' or 'product update.' This skill covers phased launches, channel strategy, and ongoing launch momentum.
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。
When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.
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。
Run a compliance check on a proposed action, product feature, or business initiative, surfacing applicable regulations, required approvals, and risk areas. Use when launching a feature that touches personal data, when marketing or product proposes something with regulatory implications, or when you need to know which approvals and jurisdictional requirements apply before proceeding.
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。
Create a competitive analysis brief for one or more competitors or a feature area. Use when informing product strategy or feature prioritization, building sales battle cards, prepping board or investor materials, or deciding where to differentiate vs. achieve parity.
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。
Smoke test the Agent Builder feature branch end-to-end against a hermetic project scaffolded by the skill (linked to the current worktree). Covers workspace reconciliation, stored agents/skills CRUD, ownership, visibility, stars, registry/library Copy flow, picker allowlists, model policy, RBAC role gating, role impersonation UI, builder defaults, infrastructure diagnostics, channels, and Studio + Agent Builder UI. Trigger when validating the agent-builder feature branch, PRs that touch packages/server, packages/playground, packages/playground-ui agent-builder routes, or builder EE code paths.
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。
Implement a Table v9 plugin when built-ins and typed meta are insufficient. Covers FeatureMaps, runtime lifecycle hooks, prototypes, and a complete checked example.
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。
Use when reproducing an existing Codex or Claude Code feature in Qwen Code or another agent CLI by choosing a reference agent, capturing HTTP request bodies, prompts, tool/function schemas, terminal output, and then implementing the matching behavior in the target repo.
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。
End-to-end workflow for implementing a non-trivial qwen-code feature. Covers requirements investigation, design, E2E test planning, baseline dry-run, implementation, verification, self-audit, code review, and iteration.
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。
When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.
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。
Analyzes competitor products and companies by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structured competitive intelligence. Produces feature comparison matrices scored across 12 dimensions, SWOT analyses, positioning maps, UX audits, pricing model breakdowns, action item roadmaps, and stakeholder presentation templates. Use when conducting competitor analysis, comparing products against competitors, researching the competitive landscape, building battle cards for sales, preparing for a product strategy or roadmap session, responding to a competitor's new feature or pricing change, or performing a quarterly competitive review.
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。
When the user wants to plan a product launch, feature announcement, or release strategy. Also use when the user mentions 'launch,' 'Product Hunt,' 'feature release,' 'announcement,' 'go-to-market,' 'beta launch,' 'early access,' 'waitlist,' 'product update,' 'GTM plan,' 'launch checklist,' or 'launch momentum.' This skill covers phased launches, channel strategy, and ongoing launch momentum.
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。
Run parallel feature work safely with Git worktrees. Standardizes branch isolation, port allocation, environment sync, and cleanup so each worktree behaves like an independent local app. Optimized for multi-agent workflows where each agent or terminal session owns one worktree. Use when running multiple feature branches simultaneously, isolating experimental work, or coordinating multi-agent development across the same repo.
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。
Use when the user asks to fix, debug, or make a specific feature/module/area work end-to-end. Triggers: 'make X work', 'fix the Y feature', 'the Z module is broken', 'focus on [area]'. Not for quick single-bug fixes — this is for systematic deep-dive repair across all files and dependencies.
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。
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.
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。
Brainstorm feature ideas for a new product in initial discovery from PM, Designer, and Engineer perspectives. Use when starting product discovery for a new product, exploring features for a startup idea, or doing initial ideation.
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。
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
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。
Polish a working feature through user-directed live browser feedback. Use when a functional feature needs focused UX refinement before shipping.
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。
Use whenever you are about to create, push, open, update, or edit a pull request in the Handsontable monorepo – load this BEFORE running `gh pr create` or pushing a feature/docs/fix branch, not only when the user says "PR". Triggers: create/open/submit a PR, push the branch, commit and push, ship it, ready to review, update or fix a PR description, any PR URL, finishing work on a `feature/*`/`docs/*`/`fix/*` branch, or a completed ClickUp/DEV task ready to submit. Covers branch naming, pre-flight lint/tests, the PR-first then changelog flow, `gh auth` fallback, and filling the GitHub PR template.
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。
Use when adding or changing a demo the visual regression suite photographs - the Vite-based demo apps in examples/next/visual-tests/<framework>/demo/, the js Navigo router with one /<feature>-demo route per feature, the shared / grid that must stay identical across frameworks, and the sample-data patterns
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。
How to do feature work in this repo with git worktrees via `pnpm worktree`. Each worktree gets its own branch, its own block of app ports, its own node_modules, and by default shares the primary checkout's standard local Supabase DB for fast setup; pass `--db` for its own database (a copy of the primary inside the same Postgres) when the work needs migrations or destructive data changes. Load WHENEVER starting a feature, bugfix, refactor, experiment, or any change you'll want to run/test in isolation; whenever the user mentions worktrees, isolated/parallel dev instances, running multiple branches at once, or 'spin up a worktree'; and whenever you need the exact non-interactive `pnpm worktree` commands and flags. Enforces: one canonical branch per objective, one worktree per branch — join the existing worktree instead of creating another.
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。
Weekly reusable-session feature-flag cleanup for {{target_repo}}. Inventories every feature flag, classifies each as fully rolled out, long dead, partial rollout, or active experiment, removes the flag and the dead branch it guards on an isolated branch when the suite is green, and opens a PR — never touching a flag still mid-rollout.
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。
Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is unreliable across sessions — forgets context, drifts out of scope, claims "done" before tests pass, or starts each session inconsistently — or when creating or assessing AGENTS.md, CLAUDE.md, feature_list.json, init.sh, progress.md, or session-handoff files. Reach for it even if the user never says the word "harness."
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。
Create GitHub issues using the gh CLI. Use when the user wants to create a new issue, report a bug, request a feature, or create a task in GitHub. Trigger keywords - create issue, new issue, file bug, report bug, feature request, github issue.
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。
This skill should be used when the user asks to "test the harness", "run integration tests", "validate features with real API", "test with real model calls", "run agent loop tests", "verify end-to-end", or needs to verify OpenHarness features on a real codebase with actual LLM calls.
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。