{"id":"85af7c1f-a342-4103-9d59-03024dba2eb0","entityType":"agent","slug":"clawhub-aptratcn-deep-research-suite","name":"Deep Research Suite","canonicalUrl":"https://www.xpersona.co/agent/clawhub-aptratcn-deep-research-suite","canonicalPath":"/agent/clawhub-aptratcn-deep-research-suite","generatedAt":"2026-10-12T09:52:08.637Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-12T00:19:59.537Z","emptyReason":null},"description":"Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Skill: Deep Research Suite Owner: aptratcn Summary: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Tags: analysis:1.0.0, automation:1.0.0, latest:1.0.0, productivity:1.0.0, research:1.0.0 Version history: v1.0.0 | 2026-04-20T23:40:04.215Z | user Initial release: One command to aggregate, analyze, and synthesize research fr","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 969 downloads reported by the source. Last updated 10/12/2026.","installCommand":"clawhub skill install s17cpfvkz8jmsw8vpgqypg10vh857dxa:deep-research-suite","sourceUrl":"https://clawhub.ai/aptratcn/deep-research-suite","homepage":"https://clawhub.ai/aptratcn/skills/deep-research-suite","primaryLinks":[{"label":"View on ClawHub","url":"https://clawhub.ai/aptratcn/deep-research-suite","kind":"source"},{"label":"Homepage","url":"https://clawhub.ai/aptratcn/skills/deep-research-suite","kind":"homepage"}],"safetyScore":84,"overallRank":62,"popularityScore":60,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Skill: Deep Resea"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-12T00:19:59.537Z","emptyReason":null},"protocols":[{"protocol":"OPENCLEW","label":"OpenClaw","status":"self-declared","notes":"Declared in the public agent profile."}],"capabilities":[],"verifiedCount":0,"selfDeclaredCount":1,"capabilityMatrix":{"rows":[{"key":"OPENCLEW","type":"protocol","support":"unknown","confidenceSource":"profile","notes":"Listed on profile"}],"flattenedTokens":"protocol:OPENCLEW|unknown|profile"}},"adoption":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-12T00:19:59.537Z","emptyReason":null},"stars":null,"forks":null,"downloads":969,"likes":null,"task":null,"library":null,"packageName":null,"latestVersion":"1.0.0","tractionLabel":"969 downloads"},"release":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-12T00:19:59.532Z","emptyReason":null},"lastUpdatedAt":"2026-10-12T00:19:59.537Z","lastCrawledAt":"2026-10-12T00:19:59.532Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-13T00:19:59.532Z","lastVerifiedAt":null,"highlights":[{"version":"1.0.0","createdAt":"2026-04-20T23:40:04.215Z","changelog":"Initial release: One command to aggregate, analyze, and synthesize research from multiple sources","fileCount":3,"zipByteSize":3259}]},"execution":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"clawhub skill install s17cpfvkz8jmsw8vpgqypg10vh857dxa:deep-research-suite","setupComplexity":"low","setupSteps":["Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.","Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"CLAWHUB","generatedAt":"2026-10-12T09:52:08.637Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"high","updatedAt":"2026-10-12T00:19:59.537Z","emptyReason":null},"readme":"Skill: Deep Research Suite\n\nOwner: aptratcn\n\nSummary: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report.\n\nTags: analysis:1.0.0, automation:1.0.0, latest:1.0.0, productivity:1.0.0, research:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-04-20T23:40:04.215Z | user\n\nInitial release: One command to aggregate, analyze, and synthesize research from multiple sources\n\nArchive index:\n\nArchive v1.0.0: 3 files, 3259 bytes\n\nFiles: skill-card.md (1858b), SKILL.md (3985b), _meta.json (138b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: deep-research-suite\nversion: 1.0.0\ndescription: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report.\nemoji: 🔬\ntags: [research, automation, productivity, analysis, ai-agent]\n---\n\n# Deep Research Suite 🔬\n\nOne command to aggregate, analyze, and synthesize research from multiple sources.\n\n## What It Does\n\n```\nInput: \"Research AI agent memory management trends 2026\"\n\nOutput:\n1. Search 5+ sources\n2. Extract key findings\n3. Identify patterns\n4. Generate structured report\n5. Save to file for reference\n```\n\n## Research Pipeline\n\n### Stage 1: Multi-Source Search\n```\nSources to check:\n- Web search (general)\n- GitHub (code/examples)\n- Hacker News (discussions)\n- ArXiv (papers, if relevant)\n- Reddit (community opinions)\n- News sites (recent articles)\n```\n\n### Stage 2: Content Extraction\n```\nFor each source:\n1. Fetch content\n2. Extract main points\n3. Identify key facts/statistics\n4. Note source credibility\n5. Tag by topic relevance\n```\n\n### Stage 3: Synthesis\n```\nCombine findings:\n- Group by theme\n- Identify consensus views\n- Note contradictions\n- Highlight emerging trends\n- Flag outdated info\n```\n\n### Stage 4: Report Generation\n```\nOutput format:\n\n# Research Report: [Topic]\n**Date**: YYYY-MM-DD\n**Sources**: X sources analyzed\n\n## Executive Summary\n[2-3 sentence overview]\n\n## Key Findings\n\n### Trend 1: [Name]\n- Source: X, Y, Z\n- Evidence: ...\n- Implications: ...\n\n### Trend 2: [Name]\n...\n\n## Contradictions / Debates\n- View A says: ... (Source: X)\n- View B says: ... (Source: Y)\n- Assessment: ...\n\n## Actionable Insights\n1. ...\n2. ...\n\n## Sources\n- [Source 1](url) - relevance: high\n- [Source 2](url) - relevance: medium\n```\n\n## Usage Examples\n\n### Quick Research\n```\nUser: \"What's new in LLM context window management?\"\n\nAgent:\n[Searching 5 sources...]\n[Extracting key findings...]\n[Synthesizing...]\n\n## LLM Context Window Management - 2026 Update\n\n### Key Trends\n1. **Dynamic Context Sizing** - Models now auto-adjust context based on task complexity\n2. **Semantic Chunking** - Replace fixed-size chunks with meaning-based segments\n3. **Context Compression** - 10x compression with minimal info loss\n\n[Full report saved to memory/research/llm-context-2026-04-21.md]\n```\n\n### Deep Dive\n```\nUser: \"Deep research on AI agent subagent architecture patterns\"\n\nAgent:\n[This will take 5-10 minutes for comprehensive analysis]\n\nStage 1/4: Multi-source search...\n- GitHub: 15 repos found\n- Papers: 3 relevant\n- Discussions: 12 threads\n\nStage 2/4: Content extraction...\n- Analyzing top 10 GitHub repos...\n- Extracting paper findings...\n- Summarizing discussions...\n\nStage 3/4: Synthesis...\n- Identified 4 architecture patterns\n- Found 2 debates\n- 3 emerging trends\n\nStage 4/4: Report generation...\n\n[Report saved to memory/research/subagent-architecture-2026-04-21.md]\n```\n\n### Competitive Analysis\n```\nUser: \"Compare popular AI agent memory solutions\"\n\nAgent:\n[Generating comparison matrix...]\n\n| Solution | Approach | Pros | Cons | Stars |\n|----------|----------|------|------|-------|\n| Mem0 | Persistent memory | Easy integration | Limited context | 25k |\n| Letta | Stateful agents | Full state | Complex setup | 15k |\n| LangGraph | Graph memory | Flexible | Learning curve | 100k |\n\n[Full comparison saved to memory/research/memory-solutions-comparison.md]\n```\n\n## Output Files\n\nAll research saved to `memory/research/`:\n```\nmemory/research/\n├── llm-context-2026-04-21.md\n├── subagent-architecture-2026-04-21.md\n└── memory-solutions-comparison.md\n```\n\n## Integration with Other Skills\n\n- **Workflow Checkpoint** - Research is a multi-step workflow\n- **Memory Guard** - Save key findings to long-term memory\n- **Content Creator** - Generate polished reports\n\n## Anti-Patterns\n\n❌ Don't rely on single source\n❌ Don't skip source credibility check\n❌ Don't present outdated info as current\n❌ Don't fabricate sources or statistics\n\n## License\n\nMIT\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn74w4pv5ms0vhet3bstvw95a5833msw\",\n  \"slug\": \"deep-research-suite\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776728404215\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nDeep Research Suite guides an agent through multi-source search, extraction, synthesis, and structured research report generation.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[aptratcn](https://clawhub.ai/user/aptratcn)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nResearchers, analysts, and developers use this skill to gather public-source information, compare findings, and produce concise research reports for decision support or follow-up work.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Research outputs may persist under memory/research/ and retain sensitive prompts, summaries, or source notes.\n\nMitigation: Avoid private or sensitive prompts unless local storage is acceptable, and delete generated reports when they are no longer needed.\n\nRisk: Public-source research can include outdated, contradictory, or low-credibility information.\n\nMitigation: Check source dates, credibility, contradictions, and cited URLs before relying on the report.\n\n## Reference(s):\n\n- [Source skill artifact](artifact/SKILL.md)\n- [ClawHub skill page](https://clawhub.ai/aptratcn/skills/deep-research-suite)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance, files]\n\n**Output Format:** [Markdown research reports with source lists and optional comparison tables]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Reports may be saved under memory/research/ when the workflow is followed.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata and SKILL.md frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: Deep Research Suite Owner: aptratcn Summary: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Tags: analysis:1.0.0, automation:1.0.0, latest:1.0.0, productivity:1.0.0, research:1.0.0 Version history: v1.0.0 | 2026-04-20T23:40:04.215Z | user Initial release: One command to aggregate, analyze, and synthesize research fr","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"Input: \"Research AI agent memory management trends 2026\"\n\nOutput:\n1. Search 5+ sources\n2. Extract key findings\n3. Identify patterns\n4. Generate structured report\n5. Save to file for reference"},{"language":"text","snippet":"Sources to check:\n- Web search (general)\n- GitHub (code/examples)\n- Hacker News (discussions)\n- ArXiv (papers, if relevant)\n- Reddit (community opinions)\n- News sites (recent articles)"},{"language":"text","snippet":"For each source:\n1. Fetch content\n2. Extract main points\n3. Identify key facts/statistics\n4. Note source credibility\n5. Tag by topic relevance"},{"language":"text","snippet":"Combine findings:\n- Group by theme\n- Identify consensus views\n- Note contradictions\n- Highlight emerging trends\n- Flag outdated info"},{"language":"text","snippet":"Output format:\n\n# Research Report: [Topic]\n**Date**: YYYY-MM-DD\n**Sources**: X sources analyzed\n\n## Executive Summary\n[2-3 sentence overview]\n\n## Key Findings\n\n### Trend 1: [Name]\n- Source: X, Y, Z\n- Evidence: ...\n- Implications: ...\n\n### Trend 2: [Name]\n...\n\n## Contradictions / Debates\n- View A says: ... (Source: X)\n- View B says: ... (Source: Y)\n- Assessment: ...\n\n## Actionable Insights\n1. ...\n2. ...\n\n## Sources\n- [Source 1](url) - relevance: high\n- [Source 2](url) - relevance: medium"},{"language":"text","snippet":"User: \"What's new in LLM context window management?\"\n\nAgent:\n[Searching 5 sources...]\n[Extracting key findings...]\n[Synthesizing...]\n\n## LLM Context Window Management - 2026 Update\n\n### Key Trends\n1. **Dynamic Context Sizing** - Models now auto-adjust context based on task complexity\n2. **Semantic Chunking** - Replace fixed-size chunks with meaning-based segments\n3. **Context Compression** - 10x compression with minimal info loss\n\n[Full report saved to memory/research/llm-context-2026-04-21.md]"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: deep-research-suite\nversion: 1.0.0\ndescription: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report.\nemoji: 🔬\ntags: [research, automation, productivity, analysis, ai-agent]\n---\n\n# Deep Research Suite 🔬\n\nOne command to aggregate, analyze, and synthesize research from multiple sources.\n\n## What It Does\n\n```\nInput: \"Research AI agent memory management trends 2026\"\n\nOutput:\n1. Search 5+ sources\n2. Extract key findings\n3. Identify patterns\n4. Generate structured report\n5. Save to file for reference\n```\n\n## Research Pipeline\n\n### Stage 1: Multi-Source Search\n```\nSources to check:\n- Web search (general)\n- GitHub (code/examples)\n- Hacker News (discussions)\n- ArXiv (papers, if relevant)\n- Reddit (community opinions)\n- News sites (recent articles)\n```\n\n### Stage 2: Content Extraction\n```\nFor each source:\n1. Fetch content\n2. Extract main points\n3. Identify key facts/statistics\n4. Note source credibility\n5. Tag by topic relevance\n```\n\n### Stage 3: Synthesis\n```\nCombine findings:\n- Group by theme\n- Identify consensus views\n- Note contradictions\n- Highlight emerging trends\n- Flag outdated info\n```\n\n### Stage 4: Report Generation\n```\nOutput format:\n\n# Research Report: [Topic]\n**Date**: YYYY-MM-DD\n**Sources**: X sources analyzed\n\n## Executive Summary\n[2-3 sentence overview]\n\n## Key Findings\n\n### Trend 1: [Name]\n- Source: X, Y, Z\n- Evidence: ...\n- Implications: ...\n\n### Trend 2: [Name]\n...\n\n## Contradictions / Debates\n- View A says: ... (Source: X)\n- View B says: ... (Source: Y)\n- Assessment: ...\n\n## Actionable Insights\n1. ...\n2. ...\n\n## Sources\n- [Source 1](url) - relevance: high\n- [Source 2](url) - relevance: medium\n```\n\n## Usage Examples\n\n### Quick Research\n```\nUser: \"What's new in LLM context window management?\"\n\nAgent:\n[Searching 5 sources...]\n[Extracting key findings...]\n[Synthesizing...]\n\n## LLM Context Window Management - 2026 Update\n\n### Key Trends\n1. **Dynamic Context Sizing** - Models now auto-adjust context based on task complexity\n2. **Semantic Chunking** - Replace fixed-size chunks with meaning-based segments\n3. **Context Compression** - 10x compression with minimal info loss\n\n[Full report saved to memory/research/llm-context-2026-04-21.md]\n```\n\n### Deep Dive\n```\nUser: \"Deep research on AI agent subagent architecture patterns\"\n\nAgent:\n[This will take 5-10 minutes for comprehensive analysis]\n\nStage 1/4: Multi-source search...\n- GitHub: 15 repos found\n- Papers: 3 relevant\n- Discussions: 12 threads\n\nStage 2/4: Content extraction...\n- Analyzing top 10 GitHub repos...\n- Extracting paper findings...\n- Summarizing discussions...\n\nStage 3/4: Synthesis...\n- Identified 4 architecture patterns\n- Found 2 debates\n- 3 emerging trends\n\nStage 4/4: Report generation...\n\n[Report saved to memory/research/subagent-architecture-2026-04-21.md]\n```\n\n### Competitive Analysis\n```\nUser: \"Compare popular AI agent memory solutions\"\n\nAgent:\n[Generating comparison matrix...]\n\n| "},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn74w4pv5ms0vhet3bstvw95a5833msw\",\n  \"slug\": \"deep-research-suite\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776728404215\n}"},{"path":"skill-card.md","content":"## Description:\n\nDeep Research Suite guides an agent through multi-source search, extraction, synthesis, and structured research report generation.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[aptratcn](https://clawhub.ai/user/aptratcn)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nResearchers, analysts, and developers use this skill to gather public-source information, compare findings, and produce concise research reports for decision support or follow-up work.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Research outputs may persist under memory/research/ and retain sensitive prompts, summaries, or source notes.\n\nMitigation: Avoid private or sensitive prompts unless local storage is acceptable, and delete generated reports when they are no longer needed.\n\nRisk: Public-source research can include outdated, contradictory, or low-credibility information.\n\nMitigation: Check source dates, credibility, contradictions, and cited URLs before relying on the report.\n\n## Reference(s):\n\n- [Source skill artifact](artifact/SKILL.md)\n- [ClawHub skill page](https://clawhub.ai/aptratcn/skills/deep-research-suite)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance, files]\n\n**Output Format:** [Markdown research reports with source lists and optional comparison tables]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Reports may be saved under memory/research/ when the workflow is followed.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata and SKILL.md frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Skill: Deep Research Suite Owner: aptratcn Summary: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Tags: analysis:1.0.0, automation:1.0.0, latest:1.0.0, productivity:1.0.0, research:1.0.0 Version history: v1.0.0 | 2026-04-20T23:40:04.215Z | user Initial release: One command to aggregate, analyze, and synthesize research fr","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":714,"uniquenessScore":56,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-12T00:19:59.537Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-12T00:19:59.537Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-12T09:52:08.637Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs","url":"https://github.com/CherryHQ/cherry-studio","homepage":"https://cherry-ai.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-11T14:38:40.986Z","createdAt":"2026-02-25T03:38:19.379Z","downloads":null},{"id":"6f6582d0-5d76-4f0f-b81d-86520247950b","entityType":"agent","canonicalPath":"/agent/copilotkit-copilotkit","slug":"copilotkit-copilotkit","name":"CopilotKit","description":"The Frontend for Agents & Generative UI. React + Angular","url":"https://github.com/CopilotKit/CopilotKit","homepage":"https://docs.copilotkit.ai","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-03-25T09:50:57.846Z","createdAt":"2026-02-25T03:39:14.617Z","downloads":null}],"links":{"hub":"/agent","source":"/agent/source/clawhub","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}