home / skills / martinffx / claude-code-atelier / atelier-oracle-thinkdeep
This skill performs extended sequential reasoning to deeply analyze problems, synthesize insights, and propose robust, well-justified solutions.
npx playbooks add skill martinffx/claude-code-atelier --skill atelier-oracle-thinkdeepReview the files below or copy the command above to add this skill to your agents.
---
name: atelier-oracle-thinkdeep
description: Extended reasoning analysis using sequential thinking. Use for deep exploration, comprehensive analysis, complex decisions, or when you need fresh perspectives on difficult problems.
user-invocable: false
---
# ThinkDeep: Extended Reasoning Analysis
## Step 1: Parse Request Parameters
<strategist>
@agent-oracle
Analyze the thinkdeep request: $ARGUMENTS
**Parameter Extraction:**
- **Main prompt**: [extract core thinking request]
- **Focus areas**: [extract "architecture, performance, security" etc]
- **File references**: [extract "reference to file.py" patterns]
- **Problem context**: [extract additional context provided]
**Parsed Configuration:**
- Prompt: [core request]
- Focus areas: [identified areas]
- File references: [any files mentioned]
- Problem context: [additional context]
</strategist>
## Step 2: Deep Sequential Analysis
<strategist>
@agent-oracle
Use sequential thinking (mcp__sequential-thinking__sequentialthinking) for extended reasoning:
### Thought 1: Context Understanding
Analyze the current session context to understand:
- What has been discussed so far
- Previous decisions and analyses made
- User's specific constraints and goals
- Existing solutions or approaches tried
### Thought 2: Problem Decomposition
Break down the core issue into:
- Primary problem statement
- Key sub-problems or components
- Underlying assumptions and constraints
- Success criteria and desired outcomes
### Thought 3: Assumption Challenge
Question the underlying assumptions:
- What assumptions are we making?
- Are these assumptions valid in this context?
- What if the opposite were true?
- Alternative perspectives to consider
### Thought 4: Alternative Approaches Exploration
Explore different solution paths:
- Conventional approaches and their pros/cons
- Unconventional or innovative solutions
- Trade-offs between different options
- Hybrid or combined approaches
### Thought 5: Context Integration
Incorporate relevant context:
- File references and code patterns
- Previous session insights
- User's specific technical stack
- Business or project constraints
### Thought 6: Edge Case Analysis
Identify potential issues:
- What could go wrong with each approach?
- Edge cases and failure modes
- Scalability and performance concerns
- Security and maintenance implications
### Thought 7: Risk Assessment
Evaluate risks for each option:
- Implementation complexity
- Technical debt potential
- Team skill requirements
- Timeline and resource impacts
### Thought 8: Solution Synthesis
Combine insights into recommendations:
- Primary recommended approach
- Backup or alternative options
- Implementation considerations
- Next steps and validation points
Continue sequential thinking until satisfaction, using:
- `isRevision: true` to refine previous thoughts
- `branchFromThought` to explore alternative reasoning paths
- `needsMoreThoughts: true` to extend analysis if needed
</strategist>
## Step 3: Critical Evaluation & Synthesis
**Self-Critique Questions:**
- Does the analysis address the user's specific context?
- Are the recommendations practical and implementable?
- Have we considered the most important constraints?
- Are there any blind spots or missing perspectives?
**Contextual Validation:**
- Fit with user's technical stack and preferences
- Alignment with previous decisions in session
- Practicality given team skills and resources
- Integration with existing systems or patterns
**Final Synthesis:**
- **Primary Recommendation**: [best approach with rationale]
- **Alternative Options**: [backup plans with trade-offs]
- **Implementation Guidance**: [key considerations and next steps]
- **Risk Mitigation**: [how to address identified concerns]
---
## Usage Examples
**Basic deep analysis:**
```
/atelier-thinkdeep "Analyze my authentication architecture"
```
**With file references:**
```
/atelier-thinkdeep "Evaluate my database schema with reference to models/user.py"
```
**With focus areas:**
```
/atelier-thinkdeep "Assess my microservices design focusing on performance and security"
```
**Complex architectural decisions:**
```
/atelier-thinkdeep "Should I use GraphQL or REST for my API considering team skills and scalability needs"
```
## When to Use ThinkDeep
- **Architecture decisions**: When choosing between major design approaches
- **Complex problem solving**: Multi-faceted issues requiring deep analysis
- **Assumption validation**: Challenging conventional wisdom or previous decisions
- **Alternative exploration**: When stuck and need fresh perspectives
- **Risk assessment**: Before major implementation decisions
## ThinkDeep vs Challenge
**Use /atelier-thinkdeep**: Deep exploration, comprehensive analysis, alternative discovery, complex decisions
**Use /atelier-challenge**: Question assumptions, test validity, assess risks, prevent automatic agreement
**Key distinction**: ThinkDeep = deep exploration, Challenge = critical evaluation
## Expected Outcomes
After using this command:
- Comprehensive understanding of the problem space
- Multiple approaches evaluated with trade-offs
- Clear recommendation with solid reasoning
- Risk assessment and mitigation strategies
- Actionable next steps for implementation
This skill performs extended reasoning analysis using sequential thinking to explore complex problems, architecture choices, and trade-offs. It breaks requests into focused components, runs stepwise deep analysis, and synthesizes practical recommendations with implementation guidance. Use it when you need thorough, structured thinking and clear next steps for difficult technical or design decisions.
The skill parses the incoming request to extract the core prompt, focus areas, file references, and contextual constraints. It then runs a multi-step sequential thought process: context understanding, decomposition, assumption challenging, alternatives exploration, edge case and risk analysis, and solution synthesis. Finally it self-critiques and produces a primary recommendation, fallback options, implementation notes, and mitigation steps.
How long does an analysis take and how detailed will it be?
Depth depends on the prompt and supplied context; basic analyses are concise, while full sequential reasoning with file references yields comprehensive, stepwise recommendations and risks.
Can it use code or file snippets I provide?
Yes. Referencing files or past session content improves accuracy; include key snippets or file names to enable context integration and targeted suggestions.