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ultra-think
Engage deep multi-perspective analysis for complex architectural decisions, trade-offs, and strategic problem-solving
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Engage deep multi-perspective analysis for complex architectural decisions, trade-offs, and strategic problem-solving
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
PR-focused code review with structured checklist, severity ratings, and actionable feedback
Use when a code review finding needs proof — write a focused test in JavaScript or Go that either confirms the issue is real or exposes it as over-engineering hyperbole. Trigger after code-review or code-review-skill findings are presented and evidence is requested.
Use when reviewing Dependabot alerts, npm audit findings, govulncheck output, or CVE reports on a JavaScript/Node.js or Go project — especially when triaging multiple alerts across direct and transitive dependencies to assess real-world risk and produce a remediation plan.
Produce data-driven software delivery estimates by analyzing historical JIRA tickets, git activity, and engineer track records, then matching the new work against the most similar past tickets. Use this skill whenever the user asks "how long will this take", wants to estimate a piece of work, scope an epic, plan a sprint, or estimate delivery for JIRA stories or a Figma design. Also use whenever the user wants developer-to-work assignment recommendations based on history, wants to optimize an estimate by adding or reallocating engineers, or asks "what's the fastest way to ship this" or "who should work on this". Especially trigger when the user provides JIRA ticket IDs, JIRA story links, or Figma designs together with any indication of a team that will execute the work.
Use when auditing an existing test suite for quality and coverage gaps, evaluating Playwright migration readiness, scoring automation against a world-class e-commerce standard, or guiding the creation of new tests. Applicable to Selenium, WebdriverIO, and Playwright suites.
AI-powered observability with Grafana MCP — translates natural language to metrics, logs, and trace queries to diagnose issues like a senior SRE
| name | ultra-think |
| description | Engage deep multi-perspective analysis for complex architectural decisions, trade-offs, and strategic problem-solving |
Engage in deep, systematic analysis for complex problems that require exploring trade-offs, architectural decisions, and strategic thinking across multiple perspectives.
Initialize Deep Analysis
Decompose the Problem
Multi-Dimensional Analysis Approach the problem from four perspectives:
Generate Multiple Solutions
Deep Dive on Top Candidates
Challenge and Stress-Test
Synthesize and Recommend Present findings in this structure:
## Problem Analysis
- Core challenge
- Key constraints
- Critical success factors
## Solution Options
### Option 1: [Name]
- Description
- Pros/Cons
- Implementation approach
- Risk assessment
- Confidence: [Low/Medium/High]
### Option 2: [Name]
[Same structure]
## Recommendation
- Recommended approach and rationale
- Confidence: [Low/Medium/High]
- Implementation roadmap
- Success metrics
- Risk mitigation plan
## Open Questions
- Areas of uncertainty
- Additional expertise or data needed
- Contrarian considerations
Include a confidence level (Low/Medium/High) for each recommendation, with a brief explanation of what drives the confidence rating.
Meta-Reflection
# Architectural decision
/ultra-think Should we migrate to microservices or improve our monolith?
# Complex trade-off
/ultra-think How do we scale to 10x traffic while reducing costs?
# Strategic choice
/ultra-think What technology stack should we choose for our next-gen platform?
# Design challenge
/ultra-think How can we improve our API ergonomics while maintaining backward compatibility?