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概要と使いどころ
Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.
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Audit mobile UX implementation against platform conventions -- navigation patterns, gesture handling, pull-to-refresh, infinite scroll, skeleton screens, haptic feedback, adaptive layouts, deep linking, and accessibility..
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Analyze program impact measurement software for logic model completeness, indicator tracking rigor, data collection methodology, causal attribution modeling, cost-effectiveness analysis, beneficiary feedback integration, and funder reporting accuracy..
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 custom components and layouts against shadcn design patterns, visual style conventions (Vega, Nova, Maia, Lyra, Mira), composition rules, design token usage, and Radix/Base UI composition patterns. Use PROACTIVELY after writing or modifying any custom UI component, page layout, or variant of a shadcn component. Use when the user says "review this component", "check my spacing", "does this follow shadcn patterns", "audit this layout", "is this shadcn-idiomatic", or shares a component for feedback. Catches spacing drift, hardcoded colors, missing data-slot attributes, inconsistent theme application, and composition anti-patterns that shipping-fast-with-AI tools often miss.
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Synthesize pilot observations into structured debrief reports and spec updates. Use this skill when: - A pilot has completed and you have observation notes, tickets, or feedback transcripts - You need to turn messy pilot data into actionable spec changes - You want to generate edge case user stories from field observations - You need negative acceptance criteria from pilot bugs - You want a structured debrief report for stakeholder communication
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 Opportunity Solution Trees using Teresa Torres' framework for structured product discovery. Use this skill when: - You need to map a desired outcome to opportunities, solutions, and experiments - You want to decide WHAT to build next based on user research and discovery data - You have workshop findings, pilot feedback, or user research to structure into actionable options - You need a visual tree diagram showing the path from outcome to testable experiments
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。
Capture learnings from a campaign, launch, sprint, or initiative using Five Whys (Taiichi Ohno, Toyota Production System) for root cause analysis and Start-Stop-Continue for action planning. Append reusable insights to knowledge/learnings.md so future runs of the OS get smarter. Use when the user asks for a retro, retrospective, post-mortem, "what did we learn", "campaign wrap-up", "lessons learned", or after a campaign or launch ends. Critical feedback loop that makes the OS compound over time. For the numbers review itself, see kpi-review. For planning the next campaign, see campaign-brief.
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 for quality, bugs, and design issues. Use when the user says "review this code", "do a code review", "check this PR", "review this diff", "is this code good", "what's wrong with this code", "code quality check", or pastes code and wants feedback - even if they don't explicitly say "code 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。
Review and improve marketing copy that already exists - a page, an email, an ad, a post, a deck slide - using a fixed six-pass edit. Produces a marked-up diagnosis, a rewritten version, and a per-pass score so the user can see what changed and why. Use when the user says "review this copy", "improve this", "make this better", "tighten this", "edit this page", "does this copy work", "why is this flat", "punch this up", "proofread", or pastes existing copy and asks for feedback. Also use when copy is on-brand but underperforming. For writing new copy from nothing, see content-writer or landing-page-writer. For diagnosing a page's conversion problem beyond its words, see page-cro. For voice consistency rules, see brand-context.
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。
**GUIDE SKILL** — Guide to the Skill Manager for Copilot extension. USE FOR: adding/removing skill repos, syncing skills, pushing feedback, configuring sync intervals, troubleshooting sync issues, understanding skill format. DO NOT USE FOR: creating new skills (use skill-creator), creating agents (use agent-creator).
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。
**WORKFLOW SKILL** — Create complete, well-structured Copilot skills from scratch. USE FOR: scaffolding new SKILL.md files, writing effective agent instructions, configuring frontmatter fields, setting up directory structure (references, FEEDBACK.md), optimizing description for discoverability, applying progressive disclosure. DO NOT USE FOR: managing/syncing existing skills (use skill-manager-guide), general coding, VS Code extension development.
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 this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.
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。
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。
Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.
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 this skill when planning user adoption, structuring Salesforce training materials, drafting release communications, or running a change impact assessment for a Salesforce rollout or update. Triggers: user adoption plan, training materials, release announcement, change impact, go-live communication, communication plan, training plan by persona, adoption metrics, LoginHistory adoption report, PromptAction, training sandbox, go-live checklist, post-go-live feedback, super user program, pilot group. NOT for org deployment mechanics or sandbox promotion — use admin/change-management-and-deployment. NOT for adoption of an Agentforce or Einstein AI feature — use admin/ai-adoption-change-management. NOT for configuring the in-app prompts themselves — use admin/in-app-guidance-and-walkthroughs.
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。
Orchestrate work as a cyclic directed graph of subagent-executed nodes with transition criteria on edges, budgeted cycles, and per-node gates (runtime verification, metric, or artifact). Use when the user says "workgraph", "run this as a graph", "graph this work", or when a goal has parallel branches, feedback loops, or mixed gate types that a linear loop cannot express. Every node is sized to a 64k context budget, so small goals run as a two-node do/verify graph and large goals scale out without caps. For clock-driven or metric-driven single loops, timeboxed-iterating and autoresearch remain lighter.
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 Codex needs to understand or respond to automatic comment-checker feedback emitted after an edit-like PostToolUse hook.
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。
Good-faith divergent brainstorming for achieving a goal. Validates assumptions, then spawns brainstormers running different techniques (first-principles, working-backwards, lateral, analogical, constraints-shift, etc.) in isolation, and synthesizes the pool into a report of standouts, reasonable ideas, and hybrid combinations. Produces feedback only — no code, no tickets, no artifacts.
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。
Prospective failure imagination for a plan, decision, or running system. Operates in two modes — **plan mode** (a plan has not yet been committed to; imagine its catastrophic failure broadly) and **scenario mode** (a specific catastrophic scenario is posed against an existing system; investigate causes against the actual code and architecture). Spawns parallel pre-mortemers in isolation across three categories of lens: standard failure classes (technical, operational, estimation, scope, adoption, dependency, team, incentive, detection, reversibility, adversarial), 0-3 ad-hoc target-specific lenses the orchestrator names when warranted, and a first-principles lens that always runs to catch what the prescribed taxonomy misses. Synthesizes into a prioritized risk register with early-warning signals. Produces feedback only — no code, no tickets, no artifacts.
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。
Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.
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。
Analysis of Competing Hypotheses. Operationalizes Richards Heuer's CIA-tradition technique for systematically narrowing among multiple hypotheses against evidence. Builds an explicit hypothesis-vs-evidence matrix and focuses on disconfirmation rather than confirmation — the central insight: hypotheses cannot be proven, only disconfirmed; the surviving hypothesis is the one with the least disconfirming evidence. Spawns parallel hypothesizers (in isolation, across hypothesis-generation angles) and parallel evidence-gatherers (across evidence classes), then synthesizes into a matrix, diagnosticity analysis, sensitivity analysis, and falsification milestones. Produces feedback only — no code, no tickets, no artifacts.
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。
Good-faith adversarial scrutiny of an idea or plan. Spawns critical skeptics across relevant lenses, pairs them with an advocate defending the idea, and synthesizes the exchange into a report. Produces feedback only — no code, no tickets, no artifacts.
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。
Problem redefinition before problem solving. Takes a stated problem, extracts its premises, then spawns reframers applying different lenses (problem-vs-symptom, scope-shift, stakeholder-shift, level-of-abstraction, time-horizon, inversion, category-shift, constraints-shift) in isolation, and synthesizes the alternatives into a report with an orchestrator recommendation. Produces feedback only — no code, no tickets, no artifacts.
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。