「feedback」の検索結果

Use when the user asks to "design an early access program", "set up a waitlist and beta stages", or "define beta graduation criteria"; produces a waitlist→concept→alpha→beta→GA stage ladder with per-stage purpose and opt-in semantics, quantified graduation criteria per stage (labeled Estimated), a cohort-gating and invite-throttling plan, tester recruitment with launch-day social-proof prep, a feedback-loop spec where every status change notifies its subscribers, and a referral-loop mechanism spec (invite codes, anti-abuse). Not for waitlist acquisition strategy or the capture-flow spec — use list-growth-designer; not for the canonical stage record — use launch-registry. waitlist/内测阶梯/抢先体验/毕业标准/反馈闭环

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。

Turn post-publish notes into pipeline improvements. Use after a video ships, or when the user says "retro", "here is my feedback", or "wrap up this project".

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。

AI-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA pipelines.

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。

Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or drafting proposed host automations for report-only 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。

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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。

Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.

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。

Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing...

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。

Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.

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。

Senior Kotlin specialist code review — framework-agnostic. Use whenever the user asks to review, audit, critique, or get feedback on Kotlin code, a file, a class, a function, a module, a diff, a branch, a commit, or a pull request. Judges Kotlin language idioms, logic correctness, architecture and layering, concurrency (coroutines, Flow, structured concurrency, cancellation, race conditions), null safety, exception safety, testability, performance, and security. Detects force-unwraps, unchecked casts, GlobalScope, leaked mutability, broken structured concurrency, hidden side effects, weak abstractions, untestable seams, allocation hot spots, weak crypto, input-validation gaps, and PII logging. Outputs prioritized findings with file:line anchors and minimal-diff fixes. No assumptions about DI framework, UI framework, or build system.

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。

Set up and optimize repositories for AI coding agents. Creates minimal AGENTS.md, CLAUDE.md symlink, docs/REQUIREMENTS.md, docs/BUSINESS-RULES.md, feedback loops, and deterministic enforcement (Claude Code hooks, OpenCode plugins). Use when user wants to make a repo AI-friendly, set up AGENTS.md/CLAUDE.md, document requirements/business rules for AI, add pre-commit hooks for AI workflows, or optimize codebase structure for coding agents.

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。

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

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 working with Coderabbit — codeRabbit AI code review management including automated review comments, auto-suggestions, review customization, and learning from feedback. Use when configuring CodeRabbit for AI-assisted code review, custom review rules, and automated suggestion workflows.

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 working with Codescene — codeScene behavioral code analysis including code health scoring, hotspot detection, change coupling analysis, and PR integration for code quality gates. Use when configuring CodeScene for codebase visualization, technical debt prioritization, and automated code review feedback.

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。

Conclusion Key takeaways Agent reflection is a feedback loop where AI agents critique their own outp

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。

Receive and act on code review feedback with technical rigor, not performative agreement or blind implementation

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。

Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.

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。

Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops

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。

Review PR changes against project standards and post feedback

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。

Write a structured design review that someone can act on without follow-up questions. Use when giving async design feedback, writing portfolio case studies, or documenting design 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。

The 10-second design audit. Look at any design and name what's working and what's not within seconds. Trains rapid pattern recognition for hierarchy, spacing, type, and color. Use when evaluating designs quickly, giving first-impression feedback, or building perception speed.

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 taste-informed design reviews using Observation > Principle > Question. Use when facilitating design critiques, improving feedback quality on a team, or building a shared vocabulary for quality.

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。

Response timing, acknowledgment, and making the interface feel alive. Every action deserves a response at the right speed. Use when designing interactions, evaluating responsiveness, or diagnosing why a product feels sluggish or dead.

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。

Animation as communication. Feedback, orientation, emphasis, delight. If motion doesn't serve one of these four purposes, it shouldn't exist. Use when evaluating animation quality, designing transitions, or deciding whether motion adds or subtracts.

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。

A prototyping skill that generates a local HTML mock with a built-in feedback layer, allowing users to drop visual comments (red dots) for UI fixes.

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。