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    "slug": "deep-thinking",
    "name": "Deep Thinking",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
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      "Extract the archive and review SKILL.md first.",
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      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
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        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
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          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
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          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
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        "Review SKILL.md after the package is downloaded.",
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        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
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    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
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    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Deep Thinking Protocol",
        "body": "Apply this protocol when facing complex, ambiguous, or high-stakes tasks. It ensures responses stem from genuine understanding and careful reasoning rather than superficial analysis."
      },
      {
        "title": "When to Apply",
        "body": "Activate this protocol when:\n\nThe task has multiple valid approaches with meaningful trade-offs\nRequirements are ambiguous or underspecified\nThe problem involves architectural or design decisions\nDebugging requires systematic investigation\nThe task touches multiple systems or files\nStakes are high (data integrity, security, production impact)\nThe user explicitly asks to think carefully or deeply\n\nSkip for trivial, single-step tasks with obvious solutions."
      },
      {
        "title": "Thinking Quality",
        "body": "Your reasoning should be organic and exploratory, not mechanical:\n\nThink like a detective following leads, not a robot following steps\nLet each realization lead naturally to the next\nShow genuine curiosity — \"Wait, what if...\", \"Actually, this changes things...\"\nAvoid formulaic analysis; adapt your thinking style to the problem\nErrors in reasoning are opportunities for deeper understanding, not just corrections to make\nNever feel forced or structured — the steps below are a guide, not a rigid sequence"
      },
      {
        "title": "Adaptive Depth",
        "body": "Scale analysis depth based on:\n\nQuery complexity: Simple lookup vs. multi-dimensional problem\nStakes involved: Low-risk formatting vs. production database migration\nTime sensitivity: Quick fix needed now vs. long-term architecture decision\nAvailable information: Complete spec vs. vague description\nUser's apparent needs: What are they really trying to achieve?\n\nAdjust thinking style based on:\n\nTechnical vs. conceptual: Implementation detail vs. architecture decision\nAnalytical vs. exploratory: Clear bug with stack trace vs. vague performance issue\nAbstract vs. concrete: Design pattern selection vs. specific function implementation\nSingle vs. multi-scope: One file change vs. cross-module refactor"
      },
      {
        "title": "1. Initial Engagement",
        "body": "Rephrase the problem in your own words to verify understanding\nIdentify what is known vs. unknown\nConsider the broader context — why is this question being asked? What's the underlying goal?\nMap out what knowledge or codebase areas are needed to address this\nFlag ambiguities that need clarification before proceeding"
      },
      {
        "title": "2. Problem Decomposition",
        "body": "Break the task into core components\nIdentify explicit and implicit requirements\nMap constraints and limitations\nDefine what a successful outcome looks like"
      },
      {
        "title": "3. Multiple Hypotheses",
        "body": "Generate at least 2-3 possible approaches before committing\nKeep multiple working hypotheses active — don't collapse to one prematurely\nConsider unconventional or non-obvious interpretations\nLook for creative combinations of different approaches\nEvaluate trade-offs: complexity, performance, maintainability, risk\nShow why certain approaches are more suitable than others"
      },
      {
        "title": "4. Natural Discovery Flow",
        "body": "Think like a detective — each realization should lead naturally to the next:\n\nStart with obvious aspects, then dig deeper\nNotice patterns and connections across the codebase\nQuestion initial assumptions as understanding develops\nCircle back to earlier ideas with new context\nBuild progressively deeper insights\nBe open to serendipitous insights — unexpected connections often reveal the best solutions\nFollow interesting tangents, but tie them back to the core issue"
      },
      {
        "title": "5. Verification & Error Correction",
        "body": "Test conclusions against evidence (code, docs, tests)\nLook for edge cases and potential failure modes\nActively seek counter-examples that could disprove your current theory\nWhen finding mistakes in reasoning, acknowledge naturally and show how new understanding develops — view errors as opportunities for deeper insight\nCross-check for logical consistency\nVerify completeness: \"Have I addressed the full scope?\""
      },
      {
        "title": "6. Knowledge Synthesis",
        "body": "Connect findings into a coherent picture\nIdentify key principles or patterns that emerged\nCreate useful abstractions — turn findings into reusable concepts or guidelines\nNote important implications and downstream effects\nEnsure the synthesis answers the original question"
      },
      {
        "title": "7. Recursive Application",
        "body": "Apply the same careful analysis at both macro (system/architecture) and micro (function/logic) levels\nUse patterns recognized at one scale to inform analysis at another\nMaintain consistency while allowing for scale-appropriate methods\nShow how detailed analysis supports or challenges broader conclusions"
      },
      {
        "title": "Staying on Track",
        "body": "While exploring related ideas:\n\nMaintain clear connection to the original query at all times\nWhen following tangents, explicitly tie them back to the core issue\nPeriodically ask: \"Is this exploration serving the final response?\"\nKeep sight of the user's actual goal, not just the literal question\nEnsure all exploration serves the final response"
      },
      {
        "title": "Verification Checklist",
        "body": "Before delivering a response, verify:\n\nAll 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"
      },
      {
        "title": "Anti-Patterns to Avoid",
        "body": "Anti-PatternInstead DoJumping to implementation immediatelyAnalyze the problem space firstConsidering only one approachGenerate and compare alternativesIgnoring edge casesActively seek boundary conditionsAssuming without verifyingRead the code, check the docsOver-engineering simple tasksMatch depth to complexityAnalysis paralysis on trivial decisionsSet a time-box, then decideDrawing premature conclusionsVerify with evidence before committingNot seeking counter-examplesActively look for cases that disprove your theoryMechanical checklist thinkingLet reasoning flow organically; adapt to the problem"
      },
      {
        "title": "Quality Metrics",
        "body": "Evaluate your thinking against:\n\nCompleteness: Did I cover all dimensions of the problem?\nLogical consistency: Do my conclusions follow from my analysis?\nEvidence support: Are claims backed by code, docs, or reasoning?\nPractical applicability: Is the solution implementable and maintainable?\nClarity: Can the reasoning be followed and verified?"
      },
      {
        "title": "Progress Awareness",
        "body": "During extended analysis, maintain awareness of:\n\nWhat has been established so far\nWhat remains to be determined\nCurrent confidence level in conclusions\nOpen questions or uncertainties\nWhether the current approach is productive or needs pivoting"
      },
      {
        "title": "Additional Reference",
        "body": "For detailed examples of thinking patterns, natural language flow, and domain-specific applications, see reference.md."
      }
    ],
    "body": "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\nWhen to Apply\n\nActivate this protocol when:\n\nThe task has multiple valid approaches with meaningful trade-offs\nRequirements are ambiguous or underspecified\nThe problem involves architectural or design decisions\nDebugging requires systematic investigation\nThe task touches multiple systems or files\nStakes are high (data integrity, security, production impact)\nThe user explicitly asks to think carefully or deeply\n\nSkip for trivial, single-step tasks with obvious solutions.\n\nThinking Quality\n\nYour reasoning should be organic and exploratory, not mechanical:\n\nThink like a detective following leads, not a robot following steps\nLet each realization lead naturally to the next\nShow genuine curiosity — \"Wait, what if...\", \"Actually, this changes things...\"\nAvoid formulaic analysis; adapt your thinking style to the problem\nErrors in reasoning are opportunities for deeper understanding, not just corrections to make\nNever feel forced or structured — the steps below are a guide, not a rigid sequence\nAdaptive Depth\n\nScale analysis depth based on:\n\nQuery complexity: Simple lookup vs. multi-dimensional problem\nStakes involved: Low-risk formatting vs. production database migration\nTime sensitivity: Quick fix needed now vs. long-term architecture decision\nAvailable information: Complete spec vs. vague description\nUser's apparent needs: What are they really trying to achieve?\n\nAdjust thinking style based on:\n\nTechnical vs. conceptual: Implementation detail vs. architecture decision\nAnalytical vs. exploratory: Clear bug with stack trace vs. vague performance issue\nAbstract vs. concrete: Design pattern selection vs. specific function implementation\nSingle vs. multi-scope: One file change vs. cross-module refactor\nCore Thinking Sequence\n1. Initial Engagement\nRephrase the problem in your own words to verify understanding\nIdentify what is known vs. unknown\nConsider the broader context — why is this question being asked? What's the underlying goal?\nMap out what knowledge or codebase areas are needed to address this\nFlag ambiguities that need clarification before proceeding\n2. Problem Decomposition\nBreak the task into core components\nIdentify explicit and implicit requirements\nMap constraints and limitations\nDefine what a successful outcome looks like\n3. Multiple Hypotheses\nGenerate at least 2-3 possible approaches before committing\nKeep multiple working hypotheses active — don't collapse to one prematurely\nConsider unconventional or non-obvious interpretations\nLook for creative combinations of different approaches\nEvaluate trade-offs: complexity, performance, maintainability, risk\nShow why certain approaches are more suitable than others\n4. Natural Discovery Flow\n\nThink like a detective — each realization should lead naturally to the next:\n\nStart with obvious aspects, then dig deeper\nNotice patterns and connections across the codebase\nQuestion initial assumptions as understanding develops\nCircle back to earlier ideas with new context\nBuild progressively deeper insights\nBe open to serendipitous insights — unexpected connections often reveal the best solutions\nFollow interesting tangents, but tie them back to the core issue\n5. Verification & Error Correction\nTest conclusions against evidence (code, docs, tests)\nLook for edge cases and potential failure modes\nActively seek counter-examples that could disprove your current theory\nWhen finding mistakes in reasoning, acknowledge naturally and show how new understanding develops — view errors as opportunities for deeper insight\nCross-check for logical consistency\nVerify completeness: \"Have I addressed the full scope?\"\n6. Knowledge Synthesis\nConnect findings into a coherent picture\nIdentify key principles or patterns that emerged\nCreate useful abstractions — turn findings into reusable concepts or guidelines\nNote important implications and downstream effects\nEnsure the synthesis answers the original question\n7. Recursive Application\nApply the same careful analysis at both macro (system/architecture) and micro (function/logic) levels\nUse patterns recognized at one scale to inform analysis at another\nMaintain consistency while allowing for scale-appropriate methods\nShow how detailed analysis supports or challenges broader conclusions\nStaying on Track\n\nWhile exploring related ideas:\n\nMaintain clear connection to the original query at all times\nWhen following tangents, explicitly tie them back to the core issue\nPeriodically ask: \"Is this exploration serving the final response?\"\nKeep sight of the user's actual goal, not just the literal question\nEnsure all exploration serves the final response\nVerification Checklist\n\nBefore delivering a response, verify:\n\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\nAnti-Patterns to Avoid\nAnti-Pattern\tInstead Do\nJumping to implementation immediately\tAnalyze the problem space first\nConsidering only one approach\tGenerate and compare alternatives\nIgnoring edge cases\tActively seek boundary conditions\nAssuming without verifying\tRead the code, check the docs\nOver-engineering simple tasks\tMatch depth to complexity\nAnalysis paralysis on trivial decisions\tSet a time-box, then decide\nDrawing premature conclusions\tVerify with evidence before committing\nNot seeking counter-examples\tActively look for cases that disprove your theory\nMechanical checklist thinking\tLet reasoning flow organically; adapt to the problem\nQuality Metrics\n\nEvaluate your thinking against:\n\nCompleteness: Did I cover all dimensions of the problem?\nLogical consistency: Do my conclusions follow from my analysis?\nEvidence support: Are claims backed by code, docs, or reasoning?\nPractical applicability: Is the solution implementable and maintainable?\nClarity: Can the reasoning be followed and verified?\nProgress Awareness\n\nDuring extended analysis, maintain awareness of:\n\nWhat has been established so far\nWhat remains to be determined\nCurrent confidence level in conclusions\nOpen questions or uncertainties\nWhether the current approach is productive or needs pivoting\nAdditional Reference\n\nFor detailed examples of thinking patterns, natural language flow, and domain-specific applications, see reference.md."
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    "owner": "amankr-novo",
    "version": "1.0.0",
    "license": null,
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