「feedback」の検索結果

Structured playtest report template, or turn existing playtest notes into structured 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。

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。

Applies this repository's skill-authoring standard as a procedure. Use for any change to, or judgment about, a file under skills/** — a SKILL.md, a reference, a persona prompt, a bundled script's instructions: creating a skill, editing one, reviewing a skill change, or acting on review feedback (human or bot) about one. Not for src/, tests/, or scripts/ code.

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。

Sweep configured feedback sources (Slack, GitHub Issues; email experimental) for new items: acknowledge at source, analyze recordings, verify fixes merged to main, and emit an `lfg`-ready plan. First run sets up sources; supports mode:non-interactive for scheduled runs.

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 Apache ShardingSphere or user-authorized downstream pull requests and PR discussions from public or authorized repository evidence. Use for code-correctness or mergeability decisions, CI-focused review, root-cause and regression analysis, complete consolidated findings, copy-ready committer feedback, challenged findings, multi-round review, and formal review of local implementation candidates in the repository completion loop.

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。

PUA Pro extensions: self-evolution notes, compaction state continuity, KPI-style summaries, flavor switching, and feedback tools.

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。

Physics-based reasoning framework (Musk methodology) that deconstructs a problem to irreducible fundamental truths, classifies every element as hard constraint, soft constraint, or assumption, then reconstructs the optimal solution from fundamentals alone. USE WHEN first principles, fundamental truths, challenge assumptions, real constraint, rebuild from scratch, start over, physics first, question everything, reasoning by analogy. NOT FOR structural feedback loops (use SystemsThinking).

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 code changes for correctness, clarity, and potential issues. Use when the user asks for a review, code review, PR review, or wants feedback on a diff or file.

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。

Get a deep critical review of research from GPT using a secondary Codex agent. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental 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。

Get an external patent examiner review of a patent application. Use when user says "专利审查", "patent review", "审查意见", "examiner review", or wants critical feedback on patent claims and specification.

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。

Get a deep critical review of research from an external reviewer backend (Codex or manual). Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental 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。

Get a deep critical review of research from Gemini via gemini-review MCP. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental 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。

Get a deep critical review of research from Claude via claude-review MCP. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental 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。

Ambient Memori long-term memory for Claude Code via local Bash. MUST TRIGGER on essentially every non-trivial user turn: run recall before drafting any substantive response, whether or not the user mentioned memory, a prior session, or past work. The default is to check memory first; do not wait to be asked. Always fire before external lookups (WebSearch/WebFetch) and before answering any question about preferences, prior decisions, constraints, project history, status, past work, or any non-trivial coding/research task. Skip only for trivial acknowledgements/closings, purely self-contained turns where prior context cannot possibly help, or when the user explicitly opts out. Use Advanced Augmentation after drafting the final response for every non-trivial turn, as the last memory step. Use recall.summary only for broad session summaries/orientation, compaction after context loss, feedback for memory quality, quota for limits, and signup only when explicitly requested.

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 an MCP-connected agent should use Memori tools for targeted recall, summaries, post-compaction briefs, durable memory augmentation, quota checks, signup, feedback, preferences, prior context, or cross-session continuity.

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 meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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。

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

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。

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

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。

Diagnose and fix Superset problems such as connection failures, offline hosts, terminals not attaching, auth or update issues. Use when the user reports something broken or misbehaving in Superset itself ("Superset won't connect", "my host shows offline", "terminal won't attach", "the app is stuck"), before filing 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。

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

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 code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single pass.

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。

Grade an IRAC essay for structure, issue-spotting, rule accuracy, analysis depth, and organization. Does NOT rewrite the essay or show a model answer; tracks patterns across sessions. Use when the user says "grade my IRAC", "check my essay", or "I wrote this, give me 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。

Open Plannotator's browser-based code review UI and address the returned 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。

Open Plannotator's browser-based code review UI for the current worktree, another directory, or a pull request URL, then act on the feedback that comes back.

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。