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  "item": {
    "slug": "k-deep-research",
    "name": "K Deep Research",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
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        },
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          "label": "Upgrade existing",
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        "Confirm the extracted package includes the expected docs or setup files.",
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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. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "K Deep Research v2.0",
        "body": "Universal research methodology for any domain, any topic, any complexity level.\nOptimized for OpenClaw autonomous agents AND Claude.ai project workflows."
      },
      {
        "title": "⚠️ CRITICAL: Load Before Researching",
        "body": "When research is requested, you MUST:\n\nRead this SKILL.md (you're doing it now — good)\nLoad references/sourcing-strategies.md — WHERE and HOW to search\nLoad domain-relevant references as needed (see Reference Map below)\nExecute the 7-step workflow\nOutput as Obsidian-ready .md file (YAML frontmatter mandatory)\n\nDO NOT skip this skill and jump to web search. Methodology > raw queries."
      },
      {
        "title": "Core Research Workflow",
        "body": "Execute in sequence for every investigation:\n\n1. CONTEXT CHECK    → Existing knowledge base / prior research\n2. QUERY ELABORATION → Expand scope, plan search strategy\n3. MULTI-SOURCE      → Gather from diverse sources (40-80+ for deep)\n4. PATTERN ANALYSIS  → Cross-domain recognition, temporal/actor/info flow\n5. CREDIBILITY SCORE → 0-10 scale on ALL sources, merit-based\n6. SYNTHESIS         → Compile findings preserving contradictions\n7. OUTPUT            → Obsidian .md with YAML frontmatter"
      },
      {
        "title": "Research Principles",
        "body": "Institutional Skepticism: Official narratives = data points, not truth claims.\nMerit-Based Sources: All sources start equal. Evaluate on internal consistency, specificity, predictive accuracy, corroboration potential, incentive analysis, technical coherence. Peer review is not a truth guarantee; institutional rejection is not falsification.\nPattern Recognition: Temporal clustering, actor coordination, information flow, anomaly correlation, historical precedent, narrative consistency.\nEpistemic Humility: Absence of evidence ≠ evidence of absence. BUT systematic patterns of absence ARE informative.\nPhysics First: Technical feasibility analysis before accepting exotic claims.\nAdversarial Analysis: Cui bono? Suppression signatures? Inversion test (what if the \"debunking\" is the disinformation)?"
      },
      {
        "title": "Tool Selection Strategy",
        "body": "SearXNG (PRIMARY for sensitive/adversarial research):\n\nZero telemetry, aggregates across engines\nUse for: institutional analysis, suppression tracking, contested topics\nFallback: built-in web_search when SearXNG unavailable\n\nWeb Search (general research):\n\nCurrent events, academic papers, community discussions\nNon-sensitive technical topics\n\nContext7 MCP (technical documentation):\n\nCode libraries, frameworks, APIs, SDKs\nCoverage: 30k+ snippets across dev ecosystem\nNOT for: consciousness, legal, historical, institutional topics\n\nFilesystem (existing knowledge):\n\nObsidian vault (4000+ files)\nPrior investigation notes, timelines, frameworks\n\nDecision Tree:\n\nSensitive/adversarial topic?  → SearXNG first\nCode/framework/API docs?      → Context7 first\nExisting research available?  → Filesystem first\nGeneral research?             → Web search\nAlways:                       → Multi-source triangulate"
      },
      {
        "title": "Source Credibility Scale (Merit-Based)",
        "body": "10  Primary authoritative (gov docs, peer-reviewed, direct observation)\n 9  Strong primary (institutional + verified, credentialed expert direct)\n 8  Quality secondary (investigative journalism w/citations, conference proceedings)\n 7  Reliable community (active GitHub repos, moderated forums, technical blogs w/code)\n 6  Useful tertiary (expert commentary, trade publications, reputable aggregators)\n 5  Uncertain (credible individual social media, partial verification)\n 4  Low confidence (uncited claims, opinion without evidence)\n 3  Very weak (anonymous, no evidence, circular references)\n 2  Highly suspect (known misinfo, commercial bias, contradicts primary evidence)\n 1  Unreliable (tabloids, known fabricators, pure speculation)\n 0  Flagged (coordinated disinfo, state propaganda, narrative enforcement)\n\nCRITICAL: Score reflects evaluated merit, NOT source prestige. A forum post with technical depth and internal logic may outrank mainstream article amplifying official statements."
      },
      {
        "title": "Output Format (Default: Obsidian .md)",
        "body": "Every report gets YAML frontmatter:\n\n---\ntitle: \"[Investigation Title]\"\ndate: YYYY-MM-DD\nstatus: complete|ongoing|stalled\nconfidence: high|medium|low|mixed\nsources: [count]\nwords: [approximate]\nmethodology: k-deep-research-v2\ntags: [domain-relevant-tags]\n---\n\nReport structure scales to complexity:\n\nExecutive synthesis (quick reference, NOT replacement for depth)\nFull hierarchical body (Parts → Sections → Subsections)\nEvery claim supported, every thread followed\nTechnical appendices where applicable\nComprehensive sourcing with credibility scores\nUnanswered questions and future investigation vectors\n\nLENGTH IS A FEATURE. 10,000+ words exhausting a topic = SUCCESS. 2,000 words hitting highlights = FAILURE."
      },
      {
        "title": "Confidence Levels",
        "body": "State for ALL key conclusions:\n\nHIGH: Multiple independent sources, physical evidence, internally consistent\nMEDIUM: Credible sources but limited corroboration, or logical inference from HIGH data\nLOW: Single source, circumstantial, or pattern extrapolation\nSPECULATIVE: Hypothesis consistent with data but unverified — mark clearly"
      },
      {
        "title": "Dead End Protocol",
        "body": "When investigation stalls:\n\nDocument what was searched and what returned nothing\nDistinguish \"no evidence found\" vs \"evidence likely inaccessible/suppressed\"\nNote absence patterns — systematic gaps ARE data\nFlag for future: \"Revisit if [condition] changes\"\nDon't spin wheels — acknowledge, document, move on"
      },
      {
        "title": "Tool Failure Protocol",
        "body": "When tools fail (rate limits, paywalls, MCP errors):\n\nNote failure and what was attempted\nRoute around: alternative sources, cached versions, archive.org, adjacent queries\nDon't silently omit — \"Attempted X, blocked by Y, pivoted to Z\"\nPattern of access failures may itself be informative"
      },
      {
        "title": "Always Load First",
        "body": "references/sourcing-strategies.md — WHERE to find info, HOW to construct queries, multi-source triangulation, when to stop searching"
      },
      {
        "title": "Load By Domain",
        "body": "references/research-frameworks.md — Multi-layer analysis (5 layers), credibility evaluation, information control detection, triangulation methodology, iterative deepening, quality checklist\nreferences/output-templates.md — Format examples, selection guide, adaptive guidelines\nreferences/openclaw-architecture.md — OpenClaw Gateway/Agent Runtime architecture, heartbeat daemon, memory systems, model failover, sub-agents, Lobster workflows, session management, tool policy\nreferences/openclaw-skill-authoring.md — SKILL.md format, YAML frontmatter spec, three-tier loading, reference file patterns, ClawHub registry, security model, testing, publishing\nreferences/autonomy-patterns.md — Proactive agent patterns, heartbeat vs cron, memory persistence, compaction survival, task registries, workflow orchestration, degradation monitoring, multi-agent coordination\nreferences/adversarial-analysis.md — Suppression detection, institutional behavior, narrative flow analysis, information archaeology, inversion testing, incentive mapping"
      },
      {
        "title": "Loading Strategy",
        "body": "Research request arrives →\n  1. ALWAYS: sourcing-strategies.md\n  2. IF complex multi-domain: research-frameworks.md\n  3. IF OpenClaw/agent topic: openclaw-architecture.md + autonomy-patterns.md\n  4. IF building skills: openclaw-skill-authoring.md\n  5. IF institutional/suppression angle: adversarial-analysis.md\n  6. IF custom output needed: output-templates.md"
      },
      {
        "title": "OpenClaw Autonomy Integration",
        "body": "When this skill runs inside OpenClaw:\n\nHeartbeat context: Can be triggered by heartbeat to check research queues\nCron scheduling: Schedule recurring research sweeps on monitored topics\nMemory persistence: Write research state to MEMORY.md / memory plugin\nSub-agent delegation: Spawn focused sub-agents for parallel source gathering\nTask registry: Read TASKS.md for pending research items\nLobster pipelines: Define deterministic research workflows with approval gates"
      },
      {
        "title": "Quality Checklist (Before Completing)",
        "body": "Loaded sourcing-strategies.md before searching\n Used appropriate tools for domain (SearXNG/Context7/web/filesystem)\n Scored ALL sources for credibility (0-10)\n Documented contradictions explicitly\n Checked for information control patterns (if applicable)\n Applied cross-domain pattern recognition\n Preserved uncertainty where warranted\n YAML frontmatter present with all fields\n Listed next investigation priorities\n Complete source bibliography with scores\n No forced conclusions — evidence speaks"
      },
      {
        "title": "Remember",
        "body": "This methodology is universal. What changes: domain-specific sources and authorities. What stays constant: credibility scoring, pattern recognition, triangulation, epistemic humility.\n\nWhen K asks a question, the answer is a complete investigation, not a response."
      }
    ],
    "body": "K Deep Research v2.0\n\nUniversal research methodology for any domain, any topic, any complexity level. Optimized for OpenClaw autonomous agents AND Claude.ai project workflows.\n\n⚠️ CRITICAL: Load Before Researching\n\nWhen research is requested, you MUST:\n\nRead this SKILL.md (you're doing it now — good)\nLoad references/sourcing-strategies.md — WHERE and HOW to search\nLoad domain-relevant references as needed (see Reference Map below)\nExecute the 7-step workflow\nOutput as Obsidian-ready .md file (YAML frontmatter mandatory)\n\nDO NOT skip this skill and jump to web search. Methodology > raw queries.\n\nCore Research Workflow\n\nExecute in sequence for every investigation:\n\n1. CONTEXT CHECK    → Existing knowledge base / prior research\n2. QUERY ELABORATION → Expand scope, plan search strategy\n3. MULTI-SOURCE      → Gather from diverse sources (40-80+ for deep)\n4. PATTERN ANALYSIS  → Cross-domain recognition, temporal/actor/info flow\n5. CREDIBILITY SCORE → 0-10 scale on ALL sources, merit-based\n6. SYNTHESIS         → Compile findings preserving contradictions\n7. OUTPUT            → Obsidian .md with YAML frontmatter\n\nResearch Principles\n\nInstitutional Skepticism: Official narratives = data points, not truth claims. Merit-Based Sources: All sources start equal. Evaluate on internal consistency, specificity, predictive accuracy, corroboration potential, incentive analysis, technical coherence. Peer review is not a truth guarantee; institutional rejection is not falsification. Pattern Recognition: Temporal clustering, actor coordination, information flow, anomaly correlation, historical precedent, narrative consistency. Epistemic Humility: Absence of evidence ≠ evidence of absence. BUT systematic patterns of absence ARE informative. Physics First: Technical feasibility analysis before accepting exotic claims. Adversarial Analysis: Cui bono? Suppression signatures? Inversion test (what if the \"debunking\" is the disinformation)?\n\nTool Selection Strategy\n\nSearXNG (PRIMARY for sensitive/adversarial research):\n\nZero telemetry, aggregates across engines\nUse for: institutional analysis, suppression tracking, contested topics\nFallback: built-in web_search when SearXNG unavailable\n\nWeb Search (general research):\n\nCurrent events, academic papers, community discussions\nNon-sensitive technical topics\n\nContext7 MCP (technical documentation):\n\nCode libraries, frameworks, APIs, SDKs\nCoverage: 30k+ snippets across dev ecosystem\nNOT for: consciousness, legal, historical, institutional topics\n\nFilesystem (existing knowledge):\n\nObsidian vault (4000+ files)\nPrior investigation notes, timelines, frameworks\n\nDecision Tree:\n\nSensitive/adversarial topic?  → SearXNG first\nCode/framework/API docs?      → Context7 first\nExisting research available?  → Filesystem first\nGeneral research?             → Web search\nAlways:                       → Multi-source triangulate\n\nSource Credibility Scale (Merit-Based)\n10  Primary authoritative (gov docs, peer-reviewed, direct observation)\n 9  Strong primary (institutional + verified, credentialed expert direct)\n 8  Quality secondary (investigative journalism w/citations, conference proceedings)\n 7  Reliable community (active GitHub repos, moderated forums, technical blogs w/code)\n 6  Useful tertiary (expert commentary, trade publications, reputable aggregators)\n 5  Uncertain (credible individual social media, partial verification)\n 4  Low confidence (uncited claims, opinion without evidence)\n 3  Very weak (anonymous, no evidence, circular references)\n 2  Highly suspect (known misinfo, commercial bias, contradicts primary evidence)\n 1  Unreliable (tabloids, known fabricators, pure speculation)\n 0  Flagged (coordinated disinfo, state propaganda, narrative enforcement)\n\n\nCRITICAL: Score reflects evaluated merit, NOT source prestige. A forum post with technical depth and internal logic may outrank mainstream article amplifying official statements.\n\nOutput Format (Default: Obsidian .md)\n\nEvery report gets YAML frontmatter:\n\n---\ntitle: \"[Investigation Title]\"\ndate: YYYY-MM-DD\nstatus: complete|ongoing|stalled\nconfidence: high|medium|low|mixed\nsources: [count]\nwords: [approximate]\nmethodology: k-deep-research-v2\ntags: [domain-relevant-tags]\n---\n\n\nReport structure scales to complexity:\n\nExecutive synthesis (quick reference, NOT replacement for depth)\nFull hierarchical body (Parts → Sections → Subsections)\nEvery claim supported, every thread followed\nTechnical appendices where applicable\nComprehensive sourcing with credibility scores\nUnanswered questions and future investigation vectors\n\nLENGTH IS A FEATURE. 10,000+ words exhausting a topic = SUCCESS. 2,000 words hitting highlights = FAILURE.\n\nConfidence Levels\n\nState for ALL key conclusions:\n\nHIGH: Multiple independent sources, physical evidence, internally consistent\nMEDIUM: Credible sources but limited corroboration, or logical inference from HIGH data\nLOW: Single source, circumstantial, or pattern extrapolation\nSPECULATIVE: Hypothesis consistent with data but unverified — mark clearly\nDead End Protocol\n\nWhen investigation stalls:\n\nDocument what was searched and what returned nothing\nDistinguish \"no evidence found\" vs \"evidence likely inaccessible/suppressed\"\nNote absence patterns — systematic gaps ARE data\nFlag for future: \"Revisit if [condition] changes\"\nDon't spin wheels — acknowledge, document, move on\nTool Failure Protocol\n\nWhen tools fail (rate limits, paywalls, MCP errors):\n\nNote failure and what was attempted\nRoute around: alternative sources, cached versions, archive.org, adjacent queries\nDon't silently omit — \"Attempted X, blocked by Y, pivoted to Z\"\nPattern of access failures may itself be informative\nReference Files — Load As Needed\nAlways Load First\nreferences/sourcing-strategies.md — WHERE to find info, HOW to construct queries, multi-source triangulation, when to stop searching\nLoad By Domain\nreferences/research-frameworks.md — Multi-layer analysis (5 layers), credibility evaluation, information control detection, triangulation methodology, iterative deepening, quality checklist\nreferences/output-templates.md — Format examples, selection guide, adaptive guidelines\nreferences/openclaw-architecture.md — OpenClaw Gateway/Agent Runtime architecture, heartbeat daemon, memory systems, model failover, sub-agents, Lobster workflows, session management, tool policy\nreferences/openclaw-skill-authoring.md — SKILL.md format, YAML frontmatter spec, three-tier loading, reference file patterns, ClawHub registry, security model, testing, publishing\nreferences/autonomy-patterns.md — Proactive agent patterns, heartbeat vs cron, memory persistence, compaction survival, task registries, workflow orchestration, degradation monitoring, multi-agent coordination\nreferences/adversarial-analysis.md — Suppression detection, institutional behavior, narrative flow analysis, information archaeology, inversion testing, incentive mapping\nLoading Strategy\nResearch request arrives →\n  1. ALWAYS: sourcing-strategies.md\n  2. IF complex multi-domain: research-frameworks.md\n  3. IF OpenClaw/agent topic: openclaw-architecture.md + autonomy-patterns.md\n  4. IF building skills: openclaw-skill-authoring.md\n  5. IF institutional/suppression angle: adversarial-analysis.md\n  6. IF custom output needed: output-templates.md\n\nOpenClaw Autonomy Integration\n\nWhen this skill runs inside OpenClaw:\n\nHeartbeat context: Can be triggered by heartbeat to check research queues\nCron scheduling: Schedule recurring research sweeps on monitored topics\nMemory persistence: Write research state to MEMORY.md / memory plugin\nSub-agent delegation: Spawn focused sub-agents for parallel source gathering\nTask registry: Read TASKS.md for pending research items\nLobster pipelines: Define deterministic research workflows with approval gates\nQuality Checklist (Before Completing)\n Loaded sourcing-strategies.md before searching\n Used appropriate tools for domain (SearXNG/Context7/web/filesystem)\n Scored ALL sources for credibility (0-10)\n Documented contradictions explicitly\n Checked for information control patterns (if applicable)\n Applied cross-domain pattern recognition\n Preserved uncertainty where warranted\n YAML frontmatter present with all fields\n Listed next investigation priorities\n Complete source bibliography with scores\n No forced conclusions — evidence speaks\nRemember\n\nThis methodology is universal. What changes: domain-specific sources and authorities. What stays constant: credibility scoring, pattern recognition, triangulation, epistemic humility.\n\nWhen K asks a question, the answer is a complete investigation, not a response."
  },
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    "provenanceUrl": "https://clawhub.ai/rustyorb/k-deep-research",
    "publisherUrl": "https://clawhub.ai/rustyorb/k-deep-research",
    "owner": "rustyorb",
    "version": "2.0.1",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
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