ultra-think
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.
What it does
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:
| Principle | Application |
|---|---|
| First Principles | Break down to fundamental truths, don't reason by analogy alone |
| Systems Thinking | Consider interconnections, feedback loops, emergent behavior |
| Probabilistic Thinking | Work with ranges and likelihoods, not certainties |
| Inversion | Ask "what should we avoid?" not just "what should we do?" |
| Second-Order Effects | Consider the consequences of consequences |
| Reversibility | Prefer reversible decisions; be extra careful with irreversible ones |
| Occam's Razor | Among 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
Capabilities
Install
Quality
deterministic score 0.45 from registry signals: · indexed on github topic:agent-skills · 7 github stars · SKILL.md body (6,383 chars)