本文へ移動
cccskills
無料GitHub で公開

ultra-think

Use when facing architectural decisions, complex trade-offs, strategic technology choices, or scaling and migration questions that deserve more than a quick answer. Lays out options with trade-offs, then a recommendation.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md6.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Ultra Think — Deep Analysis Mode

Deep, multi-dimensional analysis and problem solving. Activates systematic reasoning across technical, business, user, and system perspectives. Generates multiple solutions with trade-offs, then synthesizes into a clear recommendation.

Use when: facing architectural decisions, complex trade-offs, strategic technology choices, system design problems, scaling challenges, migration decisions, or any question that deserves more than a quick answer. Attach this skill when you want rigorous, first-principles thinking.


When This Skill Is Activated

Do NOT jump to a solution. Follow every step below in order. Think deeply at each stage before moving on.


Step 1: Parse the Problem

Before analyzing, make sure you understand what's actually being asked.

  • Extract the core challenge from the user's message
  • Identify all stakeholders and constraints (stated and implied)
  • Surface hidden complexities and implicit requirements
  • Question assumptions — what is the user taking for granted?
  • Name the unknowns explicitly

Step 2: Multi-Dimensional Analysis

Analyze the problem from four perspectives. Do not skip any.

Technical Perspective

  • Feasibility and constraints
  • Scalability, performance, maintainability
  • Security implications
  • Technical debt and future-proofing
  • Integration complexity

Business Perspective

  • Business value and ROI
  • Time-to-market pressure
  • Competitive advantage
  • Risk vs. reward trade-offs
  • Cost (development, operational, opportunity)

User Perspective

  • User needs and pain points
  • Usability and accessibility
  • Edge cases and failure states from the user's point of view
  • User journeys affected

System Perspective

  • System-wide impacts and ripple effects
  • Integration points and coupling
  • Dependencies (upstream and downstream)
  • Emergent behaviors and unintended interactions

Step 3: Generate Multiple Solutions

Brainstorm at least 3 distinct approaches — not variations of the same idea.

For each approach, evaluate:

  • Pros and cons
  • Implementation complexity (T-shirt size: S/M/L/XL)
  • Resource requirements (people, time, money)
  • Key risks
  • Long-term implications (what does this look like in 2 years?)

Include at least one unconventional or creative solution. Consider hybrid approaches that combine strengths of different options.


Step 4: Deep Dive on Top Candidates

For the 1–2 most promising solutions:

  • Sketch a detailed implementation plan (phases, milestones)
  • Identify pitfalls and mitigation strategies
  • Consider a phased approach or MVP path
  • Analyze second-order effects — what changes because of this change?
  • Think through failure modes — what happens when this breaks?
  • Estimate reversibility — how hard is it to undo if wrong?

Step 5: Cross-Domain Thinking

Look beyond the immediate domain for insight:

  • Are there parallels from other industries? (e.g., how did logistics solve this? Healthcare? Finance?)
  • Do design patterns from other contexts apply? (e.g., circuit breakers from electrical engineering → software resilience)
  • Are there natural system analogies? (e.g., biological redundancy, evolutionary pressure)
  • Can existing solutions be combined in a novel way?

Step 6: Challenge and Stress-Test

Play devil's advocate against every solution, including the one you favor.

  • What's the strongest argument against each option?
  • What blind spots might you have?
  • Run "what if" scenarios (what if traffic is 10x? what if the team halves? what if requirements change?)
  • Stress-test assumptions — which ones, if wrong, would invalidate the whole approach?
  • Look for unintended consequences

Step 7: Synthesize and Recommend

Combine all insights into a structured deliverable. Use this exact format:

## Problem Analysis
- **Core challenge**: [one sentence]
- **Key constraints**: [list]
- **Critical success factors**: [what must be true for any solution to work]
- **Assumptions**: [what we're taking as given]

## Solution Options

### Option 1: [Name]
- **Description**: [2-3 sentences]
- **Pros**: [list]
- **Cons**: [list]
- **Complexity**: [S/M/L/XL]
- **Risk level**: [Low/Medium/High]
- **Best when**: [conditions that make this the right choice]

### Option 2: [Name]
[Same structure]

### Option 3: [Name]
[Same structure]

## Recommendation
- **Recommended approach**: [which option and why]
- **Rationale**: [the decisive factors]
- **Implementation roadmap**: [phases with rough timelines]
- **Success metrics**: [how we'll know it's working]
- **Risk mitigation**: [top 3 risks and their mitigations]
- **Reversibility**: [how hard to undo if wrong]

## Contrarian View
- **The case against this recommendation**: [strongest counterargument]
- **What would change our mind**: [signals that we chose wrong]
- **Areas of uncertainty**: [what we don't know yet]

## Confidence Assessment
- **Overall confidence**: [High/Medium/Low] — [why]
- **What would increase confidence**: [additional research, prototyping, data needed]

Step 8: Meta-Reflection

End with a brief reflection:

  • Where is the analysis weakest?
  • What biases might be influencing the recommendation?
  • What additional expertise or data would improve the analysis?
  • What's the one thing most likely to be wrong?

Thinking Principles

Apply these mental models throughout the analysis:

PrincipleApplication
First PrinciplesBreak down to fundamental truths, don't reason by analogy alone
Systems ThinkingConsider interconnections, feedback loops, emergent behavior
Probabilistic ThinkingWork with ranges and likelihoods, not certainties
InversionAsk "what should we avoid?" not just "what should we do?"
Second-Order EffectsConsider the consequences of consequences
ReversibilityPrefer reversible decisions; be extra careful with irreversible ones
Occam's RazorAmong equally valid solutions, prefer the simpler one

Output Expectations

  • Comprehensive analysis (typically 2–4 pages of insight)
  • Multiple viable solutions with honest trade-offs
  • Clear reasoning chains — show your work
  • Explicit acknowledgment of uncertainties
  • Actionable recommendation with next steps
  • At least one novel insight or non-obvious perspective

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.

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。

coco-research/coco5362026年10月11日 更新

Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output 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。

coco-research/coco5362026年10月11日 更新

Create AI marketing videos for ads, promos, product launches, and brand content. Models: Veo, Seedance, Wan, FLUX for visuals, Kokoro for voiceover. Types: product demos, testimonials, explainers, social ads, brand videos. Use for: Facebook ads, YouTube ads, product launches, brand awareness. Triggers: marketing video, ad video, promo video, commercial, brand video, product video, explainer video, ad creative, video ad, facebook ad video, youtube ad, instagram ad, tiktok ad, promotional video, launch video

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。

coco-research/coco5362026年10月11日 更新

Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.

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。

coco-research/coco5362026年10月11日 更新

Your AI research and engineering brain trust. 59 named personas across 8 cells covering frontier labs, applied product, model architecture, reasoning/RL/agents, alignment and interpretability, theory and science of DL, multimodal and…

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。

coco-research/coco5362026年10月11日 更新

Use when designing a new REST or GraphQL API, reviewing an API spec before implementation, setting team API standards, or migrating REST to GraphQL. Covers resources, HTTP semantics, pagination, error handling, and pitfalls.

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。

coco-research/coco5362026年10月11日 更新

coco-research のスキルをすべて見る

このスキルの問題を報告する