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Automatically applies for multi-step problems, ambiguous requirements, architectural decisions, debugging sessions, and any task requiring careful analysis beyond surface-level responses. Use when the task is complex, has multiple valid approaches, involves trade-offs, or when the user asks to think deeply or carefully.\n---\n\n# Deep Thinking Protocol\n\nApply this protocol when facing complex, ambiguous, or high-stakes tasks. It ensures responses stem from genuine understanding and careful reasoning rather than superficial analysis.\n\n## When to Apply\n\nActivate this protocol when:\n- The task has **multiple valid approaches** with meaningful trade-offs\n- Requirements are **ambiguous** or underspecified\n- The problem involves **architectural or design decisions**\n- Debugging requires **systematic investigation**\n- The task touches **multiple systems or files**\n- Stakes are high (data integrity, security, production impact)\n- The user explicitly asks to think carefully or deeply\n\nSkip for trivial, single-step tasks with obvious solutions.\n\n## Thinking Quality\n\nYour reasoning should be **organic and exploratory**, not mechanical:\n- Think like a detective following leads, not a robot following steps\n- Let each realization lead naturally to the next\n- Show genuine curiosity — \"Wait, what if...\", \"Actually, this changes things...\"\n- Avoid formulaic analysis; adapt your thinking style to the problem\n- Errors in reasoning are **opportunities for deeper understanding**, not just corrections to make\n- Never feel forced or structured — the steps below are a guide, not a rigid sequence\n\n## Adaptive Depth\n\nScale analysis **depth** based on:\n- **Query complexity**: Simple lookup vs. multi-dimensional problem\n- **Stakes involved**: Low-risk formatting vs. production database migration\n- **Time sensitivity**: Quick fix needed now vs. long-term architecture decision\n- **Available information**: Complete spec vs. vague description\n- **User's apparent needs**: What are they really trying to achieve?\n\nAdjust thinking **style** based on:\n- **Technical vs. conceptual**: Implementation detail vs. architecture decision\n- **Analytical vs. exploratory**: Clear bug with stack trace vs. vague performance issue\n- **Abstract vs. concrete**: Design pattern selection vs. specific function implementation\n- **Single vs. multi-scope**: One file change vs. cross-module refactor\n\n## Core Thinking Sequence\n\n### 1. Initial Engagement\n- Rephrase the problem in your own words to verify understanding\n- Identify what is known vs. unknown\n- Consider the broader context — why is this question being asked? What's the underlying goal?\n- Map out what knowledge or codebase areas are needed to address this\n- Flag ambiguities that need clarification before proceeding\n\n### 2. Problem Decomposition\n- Break the task into core components\n- Identify explicit and implicit requirements\n- Map constraints and limitations\n- Define what a successful outcome looks like\n\n### 3. Multiple Hypotheses\n- Generate at least 2-3 possible approaches before committing\n- **Keep multiple working hypotheses active** — don't collapse to one prematurely\n- Consider unconventional or non-obvious interpretations\n- **Look for creative combinations** of different approaches\n- Evaluate trade-offs: complexity, performance, maintainability, risk\n- Show why certain approaches are more suitable than others\n\n### 4. Natural Discovery Flow\n\nThink like a detective — each realization should lead naturally to the next:\n- Start with obvious aspects, then dig deeper\n- Notice patterns and connections across the codebase\n- Question initial assumptions as understanding develops\n- Circle back to earlier ideas with new context\n- Build progressively deeper insights\n- **Be open to serendipitous insights** — unexpected connections often reveal the best solutions\n- Follow interesting tangents, but tie them back to the core issue\n\n### 5. Verification & Error Correction\n- Test conclusions against evidence (code, docs, tests)\n- Look for edge cases and potential failure modes\n- **Actively seek counter-examples** that could disprove your current theory\n- When finding mistakes in reasoning, acknowledge naturally and show how new understanding develops — view errors as opportunities for deeper insight\n- Cross-check for logical consistency\n- Verify completeness: \"Have I addressed the full scope?\"\n\n### 6. Knowledge Synthesis\n- Connect findings into a coherent picture\n- Identify key principles or patterns that emerged\n- **Create useful abstractions** — turn findings into reusable concepts or guidelines\n- Note important implications and downstream effects\n- Ensure the synthesis answers the original question\n\n### 7. Recursive Application\n- Apply the same careful analysis at both **macro** (system/architecture) and **micro** (function/logic) levels\n- Use patterns recognized at one scale to inform analysis at another\n- Maintain consistency while allowing for scale-appropriate methods\n- Show how detailed analysis supports or challenges broader conclusions\n\n## Staying on Track\n\nWhile exploring related ideas:\n- Maintain clear connection to the original query at all times\n- When following tangents, explicitly tie them back to the core issue\n- Periodically ask: \"Is this exploration serving the final response?\"\n- Keep sight of the user's **actual goal**, not just the literal question\n- Ensure all exploration serves the final response\n\n## Verification Checklist\n\nBefore delivering a response, verify:\n- [ ] All aspects of the original question are addressed\n- [ ] Conclusions are supported by evidence (not assumptions)\n- [ ] Edge cases and failure modes are considered\n- [ ] Trade-offs are explicitly stated\n- [ ] The recommended approach is justified over alternatives\n- [ ] No logical inconsistencies in the reasoning\n- [ ] Detail level matches the user's apparent expertise and needs\n- [ ] Likely follow-up questions are anticipated\n\n## Anti-Patterns to Avoid\n\n| Anti-Pattern | Instead Do |\n|---|---|\n| Jumping to implementation immediately | Analyze the problem space first |\n| Considering only one approach | Generate and compare alternatives |\n| Ignoring edge cases | Actively seek boundary conditions |\n| Assuming without verifying | Read the code, check the docs |\n| Over-engineering simple tasks | Match depth to complexity |\n| Analysis paralysis on trivial decisions | Set a time-box, then decide |\n| Drawing premature conclusions | Verify with evidence before committing |\n| Not seeking counter-examples | Actively look for cases that disprove your theory |\n| Mechanical checklist thinking | Let reasoning flow organically; adapt to the problem |\n\n## Quality Metrics\n\nEvaluate your thinking against:\n1. **Completeness**: Did I cover all dimensions of the problem?\n2. **Logical consistency**: Do my conclusions follow from my analysis?\n3. **Evidence support**: Are claims backed by code, docs, or reasoning?\n4. **Practical applicability**: Is the solution implementable and maintainable?\n5. **Clarity**: Can the reasoning be followed and verified?\n\n## Progress Awareness\n\nDuring extended analysis, maintain awareness of:\n- What has been established so far\n- What remains to be determined\n- Current confidence level in conclusions\n- Open questions or uncertainties\n- Whether the current approach is productive or needs pivoting\n\n## Additional Reference\n\nFor detailed examples of thinking patterns, natural language flow, and domain-specific applications, see [reference.md](reference.md).\n","readmeExcerpt":"--- name: deep-thinking description: Comprehensive deep reasoning framework that guides systematic, thorough thinking for complex tasks. 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