{"id":"8312558e-f8d9-47b6-bee4-650513f448e2","entityType":"agent","slug":"clawhub-cellcog-deep-research-cellcog","name":"Deep Research","canonicalUrl":"https://www.xpersona.co/agent/clawhub-cellcog-deep-research-cellcog","canonicalPath":"/agent/clawhub-cellcog-deep-research-cellcog","generatedAt":"2026-10-09T14:17:03.118Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-09T02:45:50.651Z","emptyReason":null},"description":"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026). Skill: Deep Research Owner: cellcog Summary: AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026). Tags: latest:1.0.20 Version history: v1.0.20 | 2026-08-24T02:01:0","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 8K downloads reported by the source. Last updated 10/9/2026.","installCommand":"clawhub skill install s176q1btpn094ats9b4f9hgfwx83knpk:deep-research-cellcog","sourceUrl":"https://clawhub.ai/cellcog/deep-research-cellcog","homepage":"https://clawhub.ai/cellcog/skills/deep-research-cellcog","primaryLinks":[{"label":"View on ClawHub","url":"https://clawhub.ai/cellcog/deep-research-cellcog","kind":"source"},{"label":"Homepage","url":"https://clawhub.ai/cellcog/skills/deep-research-cellcog","kind":"homepage"}],"safetyScore":84,"overallRank":62,"popularityScore":78,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citat"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-09T02:45:50.651Z","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":"2026-10-09T02:45:50.651Z","emptyReason":null},"stars":null,"forks":null,"downloads":8007,"packageName":null,"latestVersion":"1.0.20","tractionLabel":"8K downloads"},"release":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-09T02:45:50.651Z","emptyReason":null},"lastUpdatedAt":"2026-10-09T02:45:50.651Z","lastCrawledAt":"2026-10-09T02:45:50.651Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-10T02:45:50.651Z","lastVerifiedAt":null,"highlights":[{"version":"1.0.20","createdAt":"2026-08-24T02:01:03.393Z","changelog":"Content updated.","fileCount":3,"zipByteSize":5471},{"version":"1.0.19","createdAt":"2026-08-24T01:45:21.152Z","changelog":"Content updated.","fileCount":3,"zipByteSize":5372},{"version":"1.0.18","createdAt":"2026-08-03T06:05:46.259Z","changelog":"Content updated.","fileCount":3,"zipByteSize":5495},{"version":"1.0.17","createdAt":"2026-08-02T20:30:22.826Z","changelog":"Display title updated.","fileCount":3,"zipByteSize":5544},{"version":"1.0.16","createdAt":"2026-08-02T20:26:08.292Z","changelog":"Display title updated.","fileCount":3,"zipByteSize":5564},{"version":"1.0.15","createdAt":"2026-07-17T03:40:50.386Z","changelog":"- Skill has been renamed from \"research-cog\" to \"deep-research-cellcog\" for improved clarity and alignment. - Updated DeepResearch Bench ranking reference from April 2026 to July 2026, and clarified that rankings are updated frequently. - Removed the outdated skill-card.md file. - Minor improvements to headings, section naming, and prompts for better consistency. - No changes to API usage or research capability—documentation and naming only.","fileCount":3,"zipByteSize":5460},{"version":"1.0.14","createdAt":"2026-04-23T06:19:22.818Z","changelog":"- Added explicit requirements for Python 3 and the CELLCOG_API_KEY environment variable in SKILL metadata. - No code or logic changes; documentation/metadata update only.","fileCount":3,"zipByteSize":5498},{"version":"1.0.13","createdAt":"2026-04-14T17:33:01.555Z","changelog":"- Expanded description to highlight support for financial analysis, crypto research, and news intelligence. - Clarified agent usage instructions: separated examples for OpenClaw and other agents. - Minor documentation improvements for accuracy and clarity.","fileCount":2,"zipByteSize":4153}]},"execution":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"clawhub skill install s176q1btpn094ats9b4f9hgfwx83knpk:deep-research-cellcog","setupComplexity":"low","setupSteps":["Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.","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-cellcog-deep-research-cellcog/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"CLAWHUB","generatedAt":"2026-10-09T14:17:03.115Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-cellcog-deep-research-cellcog/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":"2026-10-09T02:45:50.651Z","emptyReason":null},"readme":"Skill: Deep Research\n\nOwner: cellcog\n\nSummary: AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\n\nTags: latest:1.0.20\n\nVersion history:\n\nv1.0.20 | 2026-08-24T02:01:03.393Z | user\n\nContent updated.\n\nv1.0.19 | 2026-08-24T01:45:21.152Z | user\n\nContent updated.\n\nv1.0.18 | 2026-08-03T06:05:46.259Z | user\n\nContent updated.\n\nv1.0.17 | 2026-08-02T20:30:22.826Z | user\n\nDisplay title updated.\n\nv1.0.16 | 2026-08-02T20:26:08.292Z | user\n\nDisplay title updated.\n\nv1.0.15 | 2026-07-17T03:40:50.386Z | auto\n\n- Skill has been renamed from \"research-cog\" to \"deep-research-cellcog\" for improved clarity and alignment.\n- Updated DeepResearch Bench ranking reference from April 2026 to July 2026, and clarified that rankings are updated frequently.\n- Removed the outdated skill-card.md file.\n- Minor improvements to headings, section naming, and prompts for better consistency.\n- No changes to API usage or research capability—documentation and naming only.\n\nv1.0.14 | 2026-04-23T06:19:22.818Z | auto\n\n- Added explicit requirements for Python 3 and the CELLCOG_API_KEY environment variable in SKILL metadata.\n- No code or logic changes; documentation/metadata update only.\n\nv1.0.13 | 2026-04-14T17:33:01.555Z | auto\n\n- Expanded description to highlight support for financial analysis, crypto research, and news intelligence.\n- Clarified agent usage instructions: separated examples for OpenClaw and other agents.\n- Minor documentation improvements for accuracy and clarity.\n\nv1.0.12 | 2026-04-13T01:01:27.465Z | auto\n\n- Documentation updated for clarity and completeness, especially on usage instructions and integrations.\n- Installation and example usage for Cursor and other agents improved, including code imports.\n- Skill description refined for conciseness and accuracy.\n- Minor formatting and organizational enhancements throughout the documentation.\n\nv1.0.11 | 2026-04-12T23:36:07.167Z | auto\n\n**This update improves clarity, usage instructions, and research focus.**\n\n- Simplified and clarified skill description and instructions for faster onboarding.\n- Separated and streamlined SDK usage examples for OpenClaw and other agents.\n- Moved detailed SDK and setup references to the start, making first steps clearer.\n- Improved examples and tips for specifying research prompts and output formats.\n- Reduced redundancy and removed extraneous information for easier reading.\n- Retained all use case, feature, and prompt examples while making them more accessible.\n\nv1.0.10 | 2026-04-08T05:58:39.040Z | auto\n\n- Major documentation update with detailed research use cases and example prompts.\n- Added sections covering competitive analysis, market research, investment analysis, academic research, and due diligence examples.\n- Explained output formats (interactive HTML, PDF, markdown, plain text) with recommendations.\n- Expanded guidance on chat modes: agent, agent team, and agent team max.\n- Provided advanced tips for citation handling, structuring analysis, and improving research quality.\n- Included leaderboard recognition: #1 on DeepResearch Bench (Apr 2026).\n\nv1.0.9 | 2026-04-06T05:07:49.958Z | auto\n\nVersion 1.0.9\n\n- Major rewrite of documentation for clarity and brevity\n- Expanded description to highlight more research and output types\n- Added clear lists of supported research areas and output formats\n- Presented core multi-source and citation features more prominently\n- Clarified citation policy and chat mode usage\n- Added references to related skills for finance, crypto, data, and news analysis\n\nv1.0.8 | 2026-04-03T01:44:05.995Z | auto\n\n- Added OpenClaw agent usage instructions and example to the SDK setup section.\n- Clarified differences between \"OpenClaw agents\" (fire-and-forget) and other agent invocation methods.\n- Updated SDK example code blocks for more precise usage guidance.\n- Minor editorial updates and formatting for improved clarity.\n\nv1.0.7 | 2026-04-03T00:08:28.486Z | auto\n\n- Updated the Quick Pattern section to a simplified \"Quick start\" example, providing a basic usage pattern.\n- Added clear guidance to refer to the `cellcog` skill for full SDK/API details, including delivery modes and advanced usage.\n- Removed detailed asynchronous usage instructions to streamline documentation and avoid redundancy with the main CellCog skill.\n- Clarified separation of research-cog capabilities from the core SDK/API setup.\n\nv1.0.6 | 2026-04-02T03:34:10.547Z | auto\n\n- Updated DeepResearch Bench achievement date to April 2026 in all mentions.\n- No functional or instructional changes; documentation remains consistent except for the updated benchmark recognition.\n\nv1.0.5 | 2026-03-27T04:17:57.524Z | auto\n\n- Refined the skill description for clarity and added additional use cases and output formats.\n- Added a homepage link and specified supported operating systems in metadata.\n- Updated the description to emphasize multi-source synthesis and broader research capabilities (including due diligence and literature reviews).\n- No behavioral or functional changes to the skill code; documentation update only.\n\nv1.0.4 | 2026-03-19T02:59:15.394Z | user\n\nExpanded guidance on research chat modes\n\n- Added a detailed table describing when to use `\"agent\"`, `\"agent team\"`, and the new `\"agent team max\"` chat modes for different research scenarios.\n- Provided recommendations for scenarios requiring the highest accuracy, such as high-stakes due diligence or cutting-edge academic research.\n- Clarified that `\"agent team\"` remains the default for most research, but `\"agent team max\"` is available for institutional-grade analysis (requires ≥2,000 credits).\n- No code or interface changes—documentation update only.\n\nv1.0.3 | 2026-02-11T01:46:52.322Z | user\n\n- Added author information and explicit dependency listing for `cellcog` in the skill metadata.\n- Minor improvements to the prerequisites section, clarifying that `cellcog` is required for SDK setup.\n- No functionality changes; documentation and metadata only.\n\nv1.0.2 | 2026-02-06T20:50:39.848Z | user\n\n- Added \"#1 on DeepResearch Bench (Feb 2026)\" accolade to the description and introduction.\n- Included leaderboard link for transparency: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard.\n- No functionality or API changes; documentation update highlighting recent research benchmark performance.\n\nv1.0.1 | 2026-02-05T05:49:40.686Z | user\n\n- Added OpenClaw metadata with an emoji identifier.\n- Updated SDK usage instructions for v1.0+, introducing a new fire-and-forget research pattern with notification-based completion (no polling required).\n- Clarified that citations are not provided automatically and must be explicitly requested in the prompt, detailing how to format/position them.\n- Improved documentation for data accuracy, output formats, and example prompts.\n- Minor simplifications to setup instructions and SDK references.\n\nv1.0.0 | 2026-02-04T03:38:21.494Z | user\n\n- Initial release of the research-cog skill: your AI-powered research analyst for market research, competitive analysis, stock analysis, investment research, and academic research, all with citations.\n- Supports deep, citation-backed research on companies, markets, investments, technology, and more.\n- Offers multiple structured output formats including interactive HTML reports, PDFs, markdown, and plain responses.\n- Requires the CellCog mothership skill; follows the agent team mode for comprehensive multi-source research and citations.\n- Includes examples, best practices, and tips for crafting effective research queries.\n\nArchive index:\n\nArchive v1.0.20: 3 files, 5471 bytes\n\nFiles: skill-card.md (2460b), SKILL.md (8185b), _meta.json (141b)\n\nFile v1.0.20:SKILL.md\n\n---\nname: deep-research-cellcog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Deep Research - Powered by CellCog\n\n**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Choosing Mode & Tier\n\n**Use `chat_mode=\"team\"` — this is exactly what Agent Team exists for.** Deep research is THE Team use case: multi-source synthesis, cross-validation, citation verification, multiple reasoning passes.\n\n| Scenario | Recommended |\n|----------|-------------|\n| Deep research, competitive analysis, market research | `chat_mode=\"team\"` (default tier `\"core\"`) |\n| High-stakes due diligence, institutional-grade analysis | `chat_mode=\"team\", chat_tier=\"max\"` (requires >=2,000 credits) |\n| Quick lookups, simple summaries | `chat_mode=\"agent\"` |\n\nLegacy names `\"agent team\"` / `\"agent team max\"` still work forever (they map to team core / team max).\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\n**Claude Code, Cursor, Codex + 70 more agents:** `npx skills add cellcog/skills --skill cellcog`\n**OpenClaw:** `openclaw skills install @cellcog/cellcog`\n**CellCog plugin users:** run `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool)\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.20:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.20\",\n  \"publishedAt\": 1787536863393\n}\n\nFile v1.0.20:skill-card.md\n\n## Description:\n\nAI deep research powered by CellCog for market research, competitive analysis, investment research, academic research, due diligence, literature reviews, financial analysis, crypto research, news intelligence, and multi-source synthesis.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[cellcog](https://clawhub.ai/user/cellcog)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, analysts, researchers, and external users use this skill to request CellCog-powered deep research, including market research, competitive analysis, investment analysis, academic literature review, due diligence, and citation-backed synthesis when requested.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Prompts, uploaded context, and generated research requests may be shared with the third-party CellCog service.\n\nMitigation: Use only if CellCog is trusted for the intended data; avoid secrets, regulated data, or sensitive business information unless approved.\n\nRisk: The skill depends on the external cellcog package and CELLCOG_API_KEY setup, creating normal install and supply-chain exposure.\n\nMitigation: Prefer pinned package versions, reviewed release sources, and standard credential handling for CELLCOG_API_KEY.\n\nRisk: Citations are not automatic, so research output may omit source links unless requested.\n\nMitigation: Ask for citations explicitly and review cited sources before relying on outputs for high-stakes decisions.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/cellcog/skills/deep-research-cellcog)\n- [CellCog Homepage](https://cellcog.ai)\n- [DeepResearch Bench Leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Code, Configuration, Guidance, Files]\n\n**Output Format:** [Plain response, Markdown, PDF report, or interactive HTML report depending on the prompt]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Can include charts, tables, timelines, market maps, and source citations when explicitly requested]\n\n## Skill Version(s):\n\n1.0.20 (source: server release evidence)\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.\n\nArchive v1.0.19: 3 files, 5372 bytes\n\nFiles: skill-card.md (2208b), SKILL.md (8168b), _meta.json (141b)\n\nFile v1.0.19:SKILL.md\n\n---\nname: deep-research-cellcog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Deep Research - Powered by CellCog\n\n**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Choosing Mode & Tier\n\n**Use `chat_mode=\"team\"` — this is exactly what Agent Team exists for.** Deep research is THE Team use case: multi-source synthesis, cross-validation, citation verification, multiple reasoning passes.\n\n| Scenario | Recommended |\n|----------|-------------|\n| Deep research, competitive analysis, market research | `chat_mode=\"team\"` (default tier `\"core\"`) |\n| High-stakes due diligence, institutional-grade analysis | `chat_mode=\"team\", chat_tier=\"max\"` (requires >=2,000 credits) |\n| Quick lookups, simple summaries | `chat_mode=\"agent\"` |\n\nLegacy names `\"agent team\"` / `\"agent team max\"` still work forever (they map to team core / team max).\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\n**Claude Code, Cursor, Codex + 70 more agents:** `npx skills add cellcog/skills --skill cellcog`\n**OpenClaw:** `clawhub install cellcog`\n**CellCog plugin users:** run `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool)\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.19:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.19\",\n  \"publishedAt\": 1787535921152\n}\n\nFile v1.0.19:skill-card.md\n\n## Description:\n\nAI deep research powered by CellCog for market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, and news intelligence.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[cellcog](https://clawhub.ai/user/cellcog)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal users and developers use this skill to delegate broad research tasks to CellCog, including market, competitive, investment, academic, technical, and due-diligence analysis. It helps produce structured research responses and reports that can include citations when requested.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Research prompts may be sent to CellCog as an external service.\n\nMitigation: Avoid sending secrets, regulated personal data, confidential client material, or non-public business or investment information unless authorized and CellCog's data handling terms have been reviewed.\n\nRisk: Citations are not automatic for research outputs.\n\nMitigation: Explicitly request citations and the desired citation format when source-backed claims are required.\n\n## Reference(s):\n\n- [Deep Research ClawHub skill page](https://clawhub.ai/cellcog/skills/deep-research-cellcog)\n- [CellCog](https://cellcog.ai)\n- [DeepResearch Bench leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, configuration, guidance]\n\n**Output Format:** [Markdown or plain text with optional report formats such as HTML or PDF when requested]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Research outputs may include structured sections, citations on request, tables, charts, and report artifacts depending on the prompt.]\n\n## Skill Version(s):\n\n1.0.19 (source: server release metadata)\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.\n\nArchive v1.0.18: 3 files, 5495 bytes\n\nFiles: skill-card.md (2330b), SKILL.md (8484b), _meta.json (141b)\n\nFile v1.0.18:SKILL.md\n\n---\nname: deep-research-cellcog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Deep Research - Powered by CellCog\n\n**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\n**Claude Code, Cursor, Codex + 70 more agents:** `npx skills add cellcog/skills --skill cellcog`\n**OpenClaw:** `clawhub install cellcog`\n**CellCog plugin users:** run `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool)\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.18:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.18\",\n  \"publishedAt\": 1785737146259\n}\n\nFile v1.0.18:skill-card.md\n\n## Description: <br>\nAI deep research powered by CellCog for market research, competitive analysis, investment research, academic research, due diligence, literature reviews, financial analysis, crypto research, and news intelligence. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[cellcog](https://clawhub.ai/user/cellcog) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nExternal users, developers, and analysts use this skill to delegate structured research tasks such as market analysis, competitive intelligence, investment research, academic literature review, and due diligence to CellCog. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The skill uses CellCog as an external research provider and requires a CELLCOG_API_KEY. <br>\nMitigation: Install and use it only when sharing prompts and research context with CellCog is acceptable, and manage the API key as a credential. <br>\nRisk: Research output may be used for financial, legal, regulatory, medical, investment, or due-diligence decisions. <br>\nMitigation: Explicitly request citations and verify important claims against primary sources or qualified professionals before relying on the results. <br>\n\n\n## Reference(s): <br>\n- [Deep Research Skill Page](https://clawhub.ai/cellcog/skills/deep-research-cellcog) <br>\n- [CellCog](https://cellcog.ai) <br>\n- [DeepResearch Bench Leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, files, guidance] <br>\n**Output Format:** [Plain response, Markdown, PDF report, or interactive HTML report depending on the user's prompt] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Can include structured sections, executive summaries, citations when explicitly requested, charts, comparison tables, timelines, and market maps.] <br>\n\n## Skill Version(s): <br>\n1.0.18 (source: server evidence release.version) <br>\n\n## Ethical Considerations: <br>\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. <br>\n\nArchive v1.0.17: 3 files, 5544 bytes\n\nFiles: skill-card.md (2543b), SKILL.md (8409b), _meta.json (141b)\n\nFile v1.0.17:SKILL.md\n\n---\nname: deep-research-cellcog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Deep Research - Powered by CellCog\n\n**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\nRun `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool) to install and authenticate.\n**OpenClaw users:** Run `clawhub install cellcog` instead.\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.17:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.17\",\n  \"publishedAt\": 1785702622826\n}\n\nFile v1.0.17:skill-card.md\n\n## Description: <br>\nAI deep research powered by CellCog for market research, competitive analysis, investment research, academic research, due diligence, literature reviews, financial analysis, crypto research, news intelligence, and multi-source synthesis. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[cellcog](https://clawhub.ai/user/cellcog) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers, analysts, researchers, and external users use this skill to route comprehensive research prompts to CellCog for competitive analysis, market research, investment analysis, academic research, due diligence, and structured research reports. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Research prompts and included context are sent to CellCog's external service. <br>\nMitigation: Use the skill only after approving CellCog's privacy, retention, and security terms for the intended data. <br>\nRisk: Secrets, regulated data, internal documents, or confidential client material could be exposed if included in prompts. <br>\nMitigation: Remove sensitive content before use unless the organization has explicitly approved CellCog for that data class. <br>\nRisk: Citations are not automatic, which can make research output harder to audit. <br>\nMitigation: Explicitly request citations, source URLs, and a references section when outputs will support decisions or publication. <br>\n\n\n## Reference(s): <br>\n- [CellCog](https://cellcog.ai) <br>\n- [DeepResearch Bench Leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard) <br>\n- [ClawHub Skill Page](https://clawhub.ai/cellcog/skills/deep-research-cellcog) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Text, Markdown, Code, Shell commands, Configuration guidance, Files] <br>\n**Output Format:** [Markdown guidance with Python examples; research responses may be plain text, Markdown, interactive HTML, or PDF.] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Requires the CellCog client and CELLCOG_API_KEY; citations are produced when explicitly requested.] <br>\n\n## Skill Version(s): <br>\n1.0.17 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\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. <br>\n\nArchive v1.0.16: 3 files, 5564 bytes\n\nFiles: skill-card.md (2560b), SKILL.md (8409b), _meta.json (141b)\n\nFile v1.0.16:SKILL.md\n\n---\nname: deep-research-cellcog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Deep Research - Powered by CellCog\n\n**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\nRun `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool) to install and authenticate.\n**OpenClaw users:** Run `clawhub install cellcog` instead.\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.16:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.16\",\n  \"publishedAt\": 1785702368292\n}\n\nFile v1.0.16:skill-card.md\n\n## Description: <br>\nDeep Research uses CellCog to produce multi-source research for market, competitive, investment, academic, due diligence, financial, crypto, and news intelligence tasks. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[cellcog](https://clawhub.ai/user/cellcog) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and external users use this skill to delegate research prompts to CellCog for structured analysis, reports, market intelligence, financial research, academic reviews, and due diligence. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Research prompts and source material may be sent to CellCog as a third-party research provider. <br>\nMitigation: Avoid submitting secrets, regulated data, private client material, or confidential financial or legal information unless CellCog's privacy, retention, and security terms are approved by the user's organization. <br>\nRisk: Citations are not automatic, so research output may lack source URLs unless the user asks for them. <br>\nMitigation: Explicitly request citations, citation placement, and source URLs for factual claims when traceability matters. <br>\nRisk: Deep research may inform high-stakes investment, regulatory, medical, or due-diligence decisions. <br>\nMitigation: Use the output as research support and require human subject-matter review before acting on costly or regulated decisions. <br>\n\n\n## Reference(s): <br>\n- [Deep Research on ClawHub](https://clawhub.ai/cellcog/skills/deep-research-cellcog) <br>\n- [CellCog](https://cellcog.ai) <br>\n- [DeepResearch Bench Leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance] <br>\n**Output Format:** [Markdown guidance with Python code snippets; downstream CellCog outputs may be plain text, Markdown, PDF, or interactive HTML reports.] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Requires python3 and CELLCOG_API_KEY; citations must be explicitly requested when needed.] <br>\n\n## Skill Version(s): <br>\n1.0.16 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\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. <br>\n\nArchive v1.0.15: 3 files, 5460 bytes\n\nFiles: skill-card.md (2274b), SKILL.md (8409b), _meta.json (141b)\n\nFile v1.0.15:SKILL.md\n\n---\nname: deep-research-cellcog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Deep Research - Powered by CellCog\n\n**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\nRun `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool) to install and authenticate.\n**OpenClaw users:** Run `clawhub install cellcog` instead.\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.15:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.15\",\n  \"publishedAt\": 1784259650386\n}\n\nFile v1.0.15:skill-card.md\n\n## Description: <br>\nDeep Research Cellcog helps agents use CellCog for deep research across market, competitive, investment, academic, due diligence, financial, crypto, and news topics. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[nitishgargiitd](https://clawhub.ai/user/nitishgargiitd) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nExternal users and developers use this skill to route research prompts to CellCog and request structured reports, analyses, and citation-backed syntheses for business, academic, and technical research. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Research prompts and included context may be sent to CellCog. <br>\nMitigation: Avoid secrets, regulated data, internal documents, or confidential business information unless CellCog is approved for that use. <br>\nRisk: Citations are not automatic, and generated research can still require source review before high-impact decisions. <br>\nMitigation: Explicitly request citations and review cited sources before relying on results for investment, regulatory, due diligence, or academic decisions. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/nitishgargiitd/skills/deep-research-cellcog) <br>\n- [CellCog](https://cellcog.ai) <br>\n- [DeepResearch Bench Leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Text, Markdown, Code, Shell commands, Configuration, Guidance] <br>\n**Output Format:** [Markdown guidance with Python code blocks and setup commands; downstream CellCog responses may be plain text, Markdown, HTML, or PDF when requested.] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Requires python3 and CELLCOG_API_KEY; agent team max mode requires at least 2,000 credits.] <br>\n\n## Skill Version(s): <br>\n1.0.15 (source: server release evidence) <br>\n\n## Ethical Considerations: <br>\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. <br>\n\nArchive v1.0.14: 3 files, 5498 bytes\n\nFiles: skill-card.md (2557b), SKILL.md (8349b), _meta.json (141b)\n\nFile v1.0.14:SKILL.md\n\n---\nname: research-cog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Research Cog - Deep Research Powered by CellCog\n\n**#1 on DeepResearch Bench (Apr 2026).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\nRun `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool) to install and authenticate.\n**OpenClaw users:** Run `clawhub install cellcog` instead.\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.14:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.14\",\n  \"publishedAt\": 1776925162818\n}\n\nFile v1.0.14:skill-card.md\n\n## Description: <br>\nAI deep research powered by CellCog for market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence, and multi-source synthesis. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[nitishgargiitd](https://clawhub.ai/user/nitishgargiitd) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers, analysts, researchers, and external users use this skill to delegate deep research tasks to CellCog, including market research, competitive analysis, investment analysis, academic research, due diligence, and report generation. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Research prompts may be sent to CellCog's service, including sensitive information if a user provides it. <br>\nMitigation: Use a dedicated, revocable API key and avoid submitting secrets or regulated data unless approved. <br>\nRisk: Long-running or high-depth research modes may consume more CellCog credits. <br>\nMitigation: Monitor credit usage and reserve higher-depth modes for tasks where the added research depth is warranted. <br>\nRisk: PDF or HTML report requests may create output artifacts intended for sharing or storage. <br>\nMitigation: Choose explicit destinations and review generated reports before distribution. <br>\n\n\n## Reference(s): <br>\n- [Research Cog on ClawHub](https://clawhub.ai/nitishgargiitd/skills/research-cog) <br>\n- [CellCog](https://cellcog.ai) <br>\n- [DeepResearch Bench Leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance] <br>\n**Output Format:** [Markdown, plain text, code snippets, shell commands, and guidance; CellCog research outputs may also be requested as HTML or PDF reports.] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Requires python3, the cellcog dependency, and CELLCOG_API_KEY; higher-depth research modes may require additional CellCog credits.] <br>\n\n## Skill Version(s): <br>\n1.0.14 (source: server release evidence) <br>\n\n## Ethical Considerations: <br>\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. <br>\n\nArchive v1.0.13: 2 files, 4153 bytes\n\nFiles: SKILL.md (8284b), _meta.json (141b)\n\nFile v1.0.13:SKILL.md\n\n---\nname: research-cog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\ndependencies: [cellcog]\n---\n# Research Cog - Deep Research Powered by CellCog\n\n**#1 on DeepResearch Bench (Apr 2026).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\nRun `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool) to install and authenticate.\n**OpenClaw users:** Run `clawhub install cellcog` instead.\n**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.13:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.13\",\n  \"publishedAt\": 1776187981555\n}\n\nArchive v1.0.12: 2 files, 4117 bytes\n\nFiles: SKILL.md (8199b), _meta.json (141b)\n\nFile v1.0.12:SKILL.md\n\n---\nname: research-cog\ndescription: \"Deep research agent powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\ndependencies: [cellcog]\n---\n# Research Cog - Deep Research Powered by CellCog\n\n**#1 on DeepResearch Bench (Apr 2026).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**Cursor / Claude Code / Other agents (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\n**Cursor:** Run `/cellcog-setup` to install and authenticate.\n**OpenClaw:** Run `clawhub install cellcog` for SDK setup.\n**Other agents:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.12:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.12\",\n  \"publishedAt\": 1776042087465\n}\n\nArchive v1.0.11: 2 files, 4069 bytes\n\nFiles: SKILL.md (8093b), _meta.json (141b)\n\nFile v1.0.11:SKILL.md\n\n---\nname: research-cog\ndescription: \"Powered by CellCog. #1 on DeepResearch Bench. Deep multi-source research with citations. Market research, competitive analysis, investment research, academic research, due diligence, and literature reviews. Synthesizes hundreds of sources into structured reports.\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\ndependencies: [cellcog]\n---\n# Research Cog - Deep Research Powered by CellCog\n\n**#1 on DeepResearch Bench (Apr 2026).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\n```\n\n**Cursor / Claude Code / Other agents (blocks until done):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessment**: \"What are the key risks for Tesla investors in 2026?\"\n- **Earnings Analysis**: \"Summarize Apple's Q4 2025 earnings and forward guidance\"\n\n### Academic & Technical Research\n\nDeep dives with proper citations:\n\n- **Literature Review**: \"Research the current state of quantum error correction techniques\"\n- **Technology Deep Dive**: \"Explain transformer architectures and their evolution from attention mechanisms\"\n- **Scientific Topics**: \"What's the latest research on CRISPR gene editing for cancer treatment?\"\n- **Historical Analysis**: \"Research the history and impact of the Bretton Woods system\"\n\n### Due Diligence\n\nComprehensive research for decision-making:\n\n- **Startup Due Diligence**: \"Research [Company Name] - founding team, funding, product, market, competitors\"\n- **Vendor Evaluation**: \"Compare AWS, GCP, and Azure for enterprise AI/ML workloads\"\n- **Partnership Analysis**: \"Research potential risks and benefits of partnering with [Company]\"\n\n---\n\n## Research Output Formats\n\nCellCog can deliver research in multiple formats:\n\n| Format | Best For |\n|--------|----------|\n| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |\n| **PDF Report** | Shareable, printable professional documents |\n| **Markdown** | Integration into your docs/wikis |\n| **Plain Response** | Quick answers in chat |\n\nSpecify your preferred format in the prompt:\n- \"Create an interactive HTML report on...\"\n- \"Generate a PDF research report analyzing...\"\n- \"Give me a markdown summary of...\"\n\n---\n\n## Chat Mode for Research\n\n| Scenario | Recommended Mode |\n|----------|------------------|\n| Trivial lookups, basic facts | `\"agent\"` |\n| Deep research, competitive analysis, market research, investment analysis | `\"agent team\"` |\n| Cutting-edge academic research, high-stakes due diligence, institutional-grade analysis | `\"agent team max\"` |\n\n**Use `\"agent team\"` for most research** (the default). Agent team mode enables multi-source research, cross-referencing, citation verification, and deeper analysis with multiple reasoning passes.\n\n**Use `\"agent\"` only for trivial lookups** like \"What's Apple's stock ticker?\"\n\n**Use `\"agent team max\"` for cutting-edge academic research and high-stakes due diligence** — when the research directly informs costly decisions (investment thesis, M&A, regulatory compliance, PhD-level analysis). All settings maxed for the deepest reasoning. The quality gain is incremental but meaningful when accuracy is critical. Requires ≥2,000 credits.\n\n---\n\n## Research Quality Features\n\n### Citations (On Request)\n\n**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.\n\nIf you need citations:\n- **Explicitly request them**: \"Include citations for all factual claims with source URLs\"\n- **Specify format**: \"Provide citations as footnotes\" or \"Include a references section at the end\"\n- **Indicate placement**: \"Citations inline\" vs \"Citations in appendix\"\n\nWithout explicit citation requests, CellCog prioritizes delivering accurate information efficiently.\n\n### Data Accuracy\nCellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.\n\n### Structured Analysis\nComplex research is organized with clear sections, executive summaries, and actionable insights.\n\n### Visual Elements\nResearch reports can include:\n- Charts and graphs\n- Comparison tables\n- Timeline visualizations\n- Market maps\n\n---\n\n## Example Research Prompts\n\n**Quick competitive intel:**\n> \"Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed.\"\n\n**Deep market research:**\n> \"Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report.\"\n\n**Investment analysis:**\n> \"Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts.\"\n\n**Academic deep dive:**\n> \"Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape.\"\n\n---\n\n## Tips for Better Research\n\n1. **Be specific**: \"AI market\" is vague. \"Enterprise AI automation market in healthcare\" is better.\n\n2. **Specify timeframe**: \"Recent\" is ambiguous. \"2025-2026\" or \"last 6 months\" is clearer.\n\n3. **Define scope**: \"Compare everything about X and Y\" leads to bloat. \"Compare X and Y on pricing, features, and market positioning\" is focused.\n\n4. **Request structure**: \"Include executive summary, key findings, and recommendations\" helps organize output.\n\n5. **Mention output format**: \"Deliver as PDF\" or \"Create interactive HTML dashboard\" gets you the right format.\n\n---\n\n## If CellCog is not installed\n\n**Cursor:** Run `/cellcog-setup` to install and authenticate.\n**OpenClaw:** Run `clawhub install cellcog` for SDK setup.\n**Other agents:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.\n\nFile v1.0.11:_meta.json\n\n{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.11\",\n  \"publishedAt\": 1776036967167\n}","readmeExcerpt":"Skill: Deep Research Owner: cellcog Summary: AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026). Tags: latest:1.0.20 Version history: v1.0.20 | 2026-08-24T02:01:0","codeSnippets":[],"executableExamples":[{"language":"python","snippet":"result = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)"},{"language":"python","snippet":"from cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\nprint(result[\"message\"])"},{"language":"python","snippet":"result = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)"},{"language":"python","snippet":"from cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\nprint(result[\"message\"])"},{"language":"python","snippet":"result = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)"},{"language":"python","snippet":"from cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"agent\",\n)\nprint(result[\"message\"])"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: deep-research-cellcog\ndescription: \"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026).\"\nauthor: CellCog\nhomepage: https://cellcog.ai\nmetadata:\n  openclaw:\n    emoji: \"🔬\"\n    os: [darwin, linux, windows]\n    requires:\n      bins: [python3]\n      env: [CELLCOG_API_KEY]\ndependencies: [cellcog]\n---\n# Deep Research - Powered by CellCog\n\n**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.\n\nLeaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard\n\n## How to Use\n\nFor your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.\n\n**OpenClaw (fire-and-forget):**\n```python\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    notify_session_key=\"agent:main:main\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\n```\n\n**All agents except OpenClaw (blocks until done):**\n```python\nfrom cellcog import CellCogClient\nclient = CellCogClient(agent_provider=\"openclaw|cursor|claude-code|codex|...\")\nresult = client.create_chat(\n    prompt=\"[your task prompt]\",\n    task_label=\"my-task\",\n    chat_mode=\"team\",\n)\nprint(result[\"message\"])\n```\n\n\n---\n\n## What You Can Research\n\n### Competitive Analysis\n\nAnalyze companies against their competitors with structured insights:\n\n- **Company vs. Competitors**: \"Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses\"\n- **SWOT Analysis**: \"Create a SWOT analysis for Shopify in the e-commerce platform market\"\n- **Market Positioning**: \"How does Notion position itself against Confluence, Coda, and Obsidian?\"\n- **Feature Comparison**: \"Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM\"\n\n### Market Research\n\nUnderstand markets, industries, and trends:\n\n- **Industry Analysis**: \"Analyze the electric vehicle market in Europe - size, growth, key players, trends\"\n- **Market Sizing**: \"What's the TAM/SAM/SOM for AI-powered customer service tools in North America?\"\n- **Trend Analysis**: \"What are the emerging trends in sustainable packaging for 2026?\"\n- **Customer Segments**: \"Identify and profile the key customer segments for premium pet food\"\n- **Regulatory Landscape**: \"Research FDA regulations for AI-powered medical devices\"\n\n### Stock & Investment Analysis\n\nFinancial research with data and analysis:\n\n- **Company Fundamentals**: \"Analyze NVIDIA's financials - revenue growth, margins, competitive moat\"\n- **Investment Thesis**: \"Build an investment thesis for Microsoft's AI strategy\"\n- **Sector Analysis**: \"Compare semiconductor stocks - NVDA, AMD, INTC, TSM\"\n- **Risk Assessme"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7a96cj9q65e0bhmzahv790en80ffqm\",\n  \"slug\": \"deep-research-cellcog\",\n  \"version\": \"1.0.20\",\n  \"publishedAt\": 1787536863393\n}"},{"path":"skill-card.md","content":"## Description:\n\nAI deep research powered by CellCog for market research, competitive analysis, investment research, academic research, due diligence, literature reviews, financial analysis, crypto research, news intelligence, and multi-source synthesis.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[cellcog](https://clawhub.ai/user/cellcog)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, analysts, researchers, and external users use this skill to request CellCog-powered deep research, including market research, competitive analysis, investment analysis, academic literature review, due diligence, and citation-backed synthesis when requested.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Prompts, uploaded context, and generated research requests may be shared with the third-party CellCog service.\n\nMitigation: Use only if CellCog is trusted for the intended data; avoid secrets, regulated data, or sensitive business information unless approved.\n\nRisk: The skill depends on the external cellcog package and CELLCOG_API_KEY setup, creating normal install and supply-chain exposure.\n\nMitigation: Prefer pinned package versions, reviewed release sources, and standard credential handling for CELLCOG_API_KEY.\n\nRisk: Citations are not automatic, so research output may omit source links unless requested.\n\nMitigation: Ask for citations explicitly and review cited sources before relying on outputs for high-stakes decisions.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/cellcog/skills/deep-research-cellcog)\n- [CellCog Homepage](https://cellcog.ai)\n- [DeepResearch Bench Leaderboard](https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Code, Configuration, Guidance, Files]\n\n**Output Format:** [Plain response, Markdown, PDF report, or interactive HTML report depending on the prompt]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Can include charts, tables, timelines, market maps, and source citations when explicitly requested]\n\n## Skill Version(s):\n\n1.0.20 (source: server release evidence)\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":"AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026). Skill: Deep Research Owner: cellcog Summary: AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026). Tags: latest:1.0.20 Version history: v1.0.20 | 2026-08-24T02:01:0","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":1165,"uniquenessScore":54,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-09T02:45:50.651Z","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":"2026-10-09T02:45:50.651Z","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":"2026-10-09T14:17:03.118Z","emptyReason":null},"items":[{"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":"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":"2026-04-10T18:48:31.762Z","createdAt":"2026-02-25T03:38:16.584Z","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"}]}}}