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Xpersona Agent

research-cog

#1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations. Skill: research-cog Owner: nitishgargiitd Summary: #1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations. Tags: latest:1.0.3 Version history: v1.0.3 | 2026-02-11T01:46:52.322Z | user - Added author information and explicit dependency listing for cellcog in the skill metadata. - Minor imp

3.2K downloadsTrust evidence available
clawhub skill install kn7a96cj9q65e0bhmzahv790en80ffqm:research-cog

Overall rank

#62

Adoption

3.2K downloads

Trust

Unknown

Freshness

Feb 28, 2026

Freshness

Last checked Feb 28, 2026

Best For

research-cog is best for general automation workflows where documented compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, CLAWHUB, runtime-metrics, public facts pack

Overview

Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.

Verifiededitorial-content

Overview

Executive Summary

#1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations. Skill: research-cog Owner: nitishgargiitd Summary: #1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations. Tags: latest:1.0.3 Version history: v1.0.3 | 2026-02-11T01:46:52.322Z | user - Added author information and explicit dependency listing for cellcog in the skill metadata. - Minor imp Capability contract not published. No trust telemetry is available yet. 3.2K downloads reported by the source. Last updated 4/15/2026.

No verified compatibility signals3.2K downloads

Trust score

Unknown

Compatibility

Profile only

Freshness

Feb 28, 2026

Vendor

Clawhub

Artifacts

0

Benchmarks

0

Last release

1.0.3

Install & run

Setup Snapshot

clawhub skill install kn7a96cj9q65e0bhmzahv790en80ffqm:research-cog
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    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.

Evidence & Timeline

Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.

Verifiededitorial-content

Public facts

Evidence Ledger

Vendor (1)

Vendor

Clawhub

profilemedium
Observed Apr 15, 2026Source linkProvenance
Release (1)

Latest release

1.0.3

releasemedium
Observed Feb 11, 2026Source linkProvenance
Adoption (1)

Adoption signal

3.2K downloads

profilemedium
Observed Apr 15, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance

Artifacts & Docs

Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.

Self-declaredCLAWHUB

Captured outputs

Artifacts Archive

Extracted files

2

Examples

4

Snippets

0

Languages

Unknown

Executable Examples

bash

clawhub install cellcog

python

# Fire-and-forget - returns immediately
result = client.create_chat(
    prompt="[your research query]",
    notify_session_key="agent:main:main",
    task_label="research-task",
    chat_mode="agent team"  # Deep research
)
# Daemon notifies you when complete - do NOT poll

bash

clawhub install cellcog

python

# Fire-and-forget - returns immediately
result = client.create_chat(
    prompt="[your research query]",
    notify_session_key="agent:main:main",
    task_label="research-task",
    chat_mode="agent team"  # Deep research
)
# Daemon notifies you when complete - do NOT poll
Extracted Files

SKILL.md

---
name: research-cog
description: "#1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations."
author: CellCog
metadata:
  openclaw:
    emoji: "🔬"
dependencies: [cellcog]
---

# Research Cog - Deep Research Powered by CellCog

**#1 on DeepResearch Bench (Feb 2026).** Your AI research analyst for comprehensive, citation-backed research on any topic.

Leaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard

---

## Prerequisites

This skill requires the `cellcog` skill for SDK setup and API calls.

```bash
clawhub install cellcog
```

**Read the cellcog skill first** for SDK setup. This skill shows you what's possible.

**Quick pattern (v1.0+):**
```python
# Fire-and-forget - returns immediately
result = client.create_chat(
    prompt="[your research query]",
    notify_session_key="agent:main:main",
    task_label="research-task",
    chat_mode="agent team"  # Deep research
)
# Daemon notifies you when complete - do NOT poll
```

---

## What You Can Research

### Competitive Analysis

Analyze companies against their competitors with structured insights:

- **Company vs. Competitors**: "Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses"
- **SWOT Analysis**: "Create a SWOT analysis for Shopify in the e-commerce platform market"
- **Market Positioning**: "How does Notion position itself against Confluence, Coda, and Obsidian?"
- **Feature Comparison**: "Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM"

### Market Research

Understand markets, industries, and trends:

- **Industry Analysis**: "Analyze the electric vehicle market in Europe - size, growth, key players, trends"
- **Market Sizing**: "What's the TAM/SAM/SOM for AI-powered customer service tools in North America?"
- **Trend Analysis**: "What are the emerging trends in sustainable packaging for 2026?"
- **Customer Segments**: "Identify and profile the key customer segments for premium pet food"
- **Regulatory Landscape**: "Research FDA regulations for AI-powered medical devices"

### Stock & Investment Analysis

Financial research with data and analysis:

- **Company Fundamentals**: "Analyze NVIDIA's financials - revenue growth, margins, competitive moat"
- **Investment Thesis**: "Build an investment thesis for Microsoft's AI strategy"
- **Sector Analysis**: "Compare semiconductor stocks - NVDA, AMD, INTC, TSM"
- **Risk Assessment**: "What are the key risks for Tesla investors in 2026?"
- **Earnings Analysis**: "Summarize Apple's Q4 2025 earnings and forward guidance"

### Academic & Technical Research

Deep dives with proper citations:

- **Literature Review**: "Research the current state of quantum error correction techniques"
- **Technology Deep Dive**: "Explain transformer architectures and their evolution from attention mechanisms"
- **Scientific Topics**: "What's the

_meta.json

{
  "ownerId": "kn7a96cj9q65e0bhmzahv790en80ffqm",
  "slug": "research-cog",
  "version": "1.0.3",
  "publishedAt": 1770774412322
}

Editorial read

Docs & README

Docs source

CLAWHUB

Editorial quality

ready

#1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations. Skill: research-cog Owner: nitishgargiitd Summary: #1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations. Tags: latest:1.0.3 Version history: v1.0.3 | 2026-02-11T01:46:52.322Z | user - Added author information and explicit dependency listing for cellcog in the skill metadata. - Minor imp

Full README

Skill: research-cog

Owner: nitishgargiitd

Summary: #1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations.

Tags: latest:1.0.3

Version history:

v1.0.3 | 2026-02-11T01:46:52.322Z | user

  • Added author information and explicit dependency listing for cellcog in the skill metadata.
  • Minor improvements to the prerequisites section, clarifying that cellcog is required for SDK setup.
  • No functionality changes; documentation and metadata only.

v1.0.2 | 2026-02-06T20:50:39.848Z | user

  • Added "#1 on DeepResearch Bench (Feb 2026)" accolade to the description and introduction.
  • Included leaderboard link for transparency: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard.
  • No functionality or API changes; documentation update highlighting recent research benchmark performance.

v1.0.1 | 2026-02-05T05:49:40.686Z | user

  • Added OpenClaw metadata with an emoji identifier.
  • Updated SDK usage instructions for v1.0+, introducing a new fire-and-forget research pattern with notification-based completion (no polling required).
  • Clarified that citations are not provided automatically and must be explicitly requested in the prompt, detailing how to format/position them.
  • Improved documentation for data accuracy, output formats, and example prompts.
  • Minor simplifications to setup instructions and SDK references.

v1.0.0 | 2026-02-04T03:38:21.494Z | user

  • 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.
  • Supports deep, citation-backed research on companies, markets, investments, technology, and more.
  • Offers multiple structured output formats including interactive HTML reports, PDFs, markdown, and plain responses.
  • Requires the CellCog mothership skill; follows the agent team mode for comprehensive multi-source research and citations.
  • Includes examples, best practices, and tips for crafting effective research queries.

Archive index:

Archive v1.0.3: 2 files, 3623 bytes

Files: SKILL.md (7025b), _meta.json (131b)

File v1.0.3:SKILL.md


name: research-cog description: "#1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations." author: CellCog metadata: openclaw: emoji: "🔬" dependencies: [cellcog]

Research Cog - Deep Research Powered by CellCog

#1 on DeepResearch Bench (Feb 2026). Your AI research analyst for comprehensive, citation-backed research on any topic.

Leaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard


Prerequisites

This skill requires the cellcog skill for SDK setup and API calls.

clawhub install cellcog

Read the cellcog skill first for SDK setup. This skill shows you what's possible.

Quick pattern (v1.0+):

# Fire-and-forget - returns immediately
result = client.create_chat(
    prompt="[your research query]",
    notify_session_key="agent:main:main",
    task_label="research-task",
    chat_mode="agent team"  # Deep research
)
# Daemon notifies you when complete - do NOT poll

What You Can Research

Competitive Analysis

Analyze companies against their competitors with structured insights:

  • Company vs. Competitors: "Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses"
  • SWOT Analysis: "Create a SWOT analysis for Shopify in the e-commerce platform market"
  • Market Positioning: "How does Notion position itself against Confluence, Coda, and Obsidian?"
  • Feature Comparison: "Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM"

Market Research

Understand markets, industries, and trends:

  • Industry Analysis: "Analyze the electric vehicle market in Europe - size, growth, key players, trends"
  • Market Sizing: "What's the TAM/SAM/SOM for AI-powered customer service tools in North America?"
  • Trend Analysis: "What are the emerging trends in sustainable packaging for 2026?"
  • Customer Segments: "Identify and profile the key customer segments for premium pet food"
  • Regulatory Landscape: "Research FDA regulations for AI-powered medical devices"

Stock & Investment Analysis

Financial research with data and analysis:

  • Company Fundamentals: "Analyze NVIDIA's financials - revenue growth, margins, competitive moat"
  • Investment Thesis: "Build an investment thesis for Microsoft's AI strategy"
  • Sector Analysis: "Compare semiconductor stocks - NVDA, AMD, INTC, TSM"
  • Risk Assessment: "What are the key risks for Tesla investors in 2026?"
  • Earnings Analysis: "Summarize Apple's Q4 2025 earnings and forward guidance"

Academic & Technical Research

Deep dives with proper citations:

  • Literature Review: "Research the current state of quantum error correction techniques"
  • Technology Deep Dive: "Explain transformer architectures and their evolution from attention mechanisms"
  • Scientific Topics: "What's the latest research on CRISPR gene editing for cancer treatment?"
  • Historical Analysis: "Research the history and impact of the Bretton Woods system"

Due Diligence

Comprehensive research for decision-making:

  • Startup Due Diligence: "Research [Company Name] - founding team, funding, product, market, competitors"
  • Vendor Evaluation: "Compare AWS, GCP, and Azure for enterprise AI/ML workloads"
  • Partnership Analysis: "Research potential risks and benefits of partnering with [Company]"

Research Output Formats

CellCog can deliver research in multiple formats:

| Format | Best For | |--------|----------| | Interactive HTML Report | Explorable dashboards with charts, expandable sections | | PDF Report | Shareable, printable professional documents | | Markdown | Integration into your docs/wikis | | Plain Response | Quick answers in chat |

Specify your preferred format in the prompt:

  • "Create an interactive HTML report on..."
  • "Generate a PDF research report analyzing..."
  • "Give me a markdown summary of..."

When to Use Agent Team Mode

For research, always use chat_mode="agent team" (the default).

Agent team mode enables:

  • Multi-source research and cross-referencing
  • Citation verification
  • Deeper analysis with multiple reasoning passes
  • Higher quality, more comprehensive outputs

Use chat_mode="agent" only for trivial lookups like "What's Apple's stock ticker?"


Research Quality Features

Citations (On Request)

Citations are NOT automatic. CellCog focuses on delivering accurate, well-researched content by default.

If you need citations:

  • Explicitly request them: "Include citations for all factual claims with source URLs"
  • Specify format: "Provide citations as footnotes" or "Include a references section at the end"
  • Indicate placement: "Citations inline" vs "Citations in appendix"

Without explicit citation requests, CellCog prioritizes delivering accurate information efficiently.

Data Accuracy

CellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.

Structured Analysis

Complex research is organized with clear sections, executive summaries, and actionable insights.

Visual Elements

Research reports can include:

  • Charts and graphs
  • Comparison tables
  • Timeline visualizations
  • Market maps

Example Research Prompts

Quick competitive intel:

"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."

Deep market research:

"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."

Investment analysis:

"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."

Academic deep dive:

"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."


Tips for Better Research

  1. Be specific: "AI market" is vague. "Enterprise AI automation market in healthcare" is better.

  2. Specify timeframe: "Recent" is ambiguous. "2025-2026" or "last 6 months" is clearer.

  3. Define scope: "Compare everything about X and Y" leads to bloat. "Compare X and Y on pricing, features, and market positioning" is focused.

  4. Request structure: "Include executive summary, key findings, and recommendations" helps organize output.

  5. Mention output format: "Deliver as PDF" or "Create interactive HTML dashboard" gets you the right format.

File v1.0.3:_meta.json

{ "ownerId": "kn7a96cj9q65e0bhmzahv790en80ffqm", "slug": "research-cog", "version": "1.0.3", "publishedAt": 1770774412322 }

Archive v1.0.2: 2 files, 3607 bytes

Files: SKILL.md (6994b), _meta.json (131b)

File v1.0.2:SKILL.md


name: research-cog description: "#1 on DeepResearch Bench (Feb 2026). Deep research agent powered by CellCog. Market research, competitive analysis, stock analysis, investment research, academic research with citations." metadata: openclaw: emoji: "🔬"

Research Cog - Deep Research Powered by CellCog

#1 on DeepResearch Bench (Feb 2026). Your AI research analyst for comprehensive, citation-backed research on any topic.

Leaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard


Prerequisites

This skill requires the CellCog mothership skill for SDK setup and API calls.

clawhub install cellcog

Read the cellcog skill first for SDK setup. This skill shows you what's possible.

Quick pattern (v1.0+):

# Fire-and-forget - returns immediately
result = client.create_chat(
    prompt="[your research query]",
    notify_session_key="agent:main:main",
    task_label="research-task",
    chat_mode="agent team"  # Deep research
)
# Daemon notifies you when complete - do NOT poll

What You Can Research

Competitive Analysis

Analyze companies against their competitors with structured insights:

  • Company vs. Competitors: "Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses"
  • SWOT Analysis: "Create a SWOT analysis for Shopify in the e-commerce platform market"
  • Market Positioning: "How does Notion position itself against Confluence, Coda, and Obsidian?"
  • Feature Comparison: "Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM"

Market Research

Understand markets, industries, and trends:

  • Industry Analysis: "Analyze the electric vehicle market in Europe - size, growth, key players, trends"
  • Market Sizing: "What's the TAM/SAM/SOM for AI-powered customer service tools in North America?"
  • Trend Analysis: "What are the emerging trends in sustainable packaging for 2026?"
  • Customer Segments: "Identify and profile the key customer segments for premium pet food"
  • Regulatory Landscape: "Research FDA regulations for AI-powered medical devices"

Stock & Investment Analysis

Financial research with data and analysis:

  • Company Fundamentals: "Analyze NVIDIA's financials - revenue growth, margins, competitive moat"
  • Investment Thesis: "Build an investment thesis for Microsoft's AI strategy"
  • Sector Analysis: "Compare semiconductor stocks - NVDA, AMD, INTC, TSM"
  • Risk Assessment: "What are the key risks for Tesla investors in 2026?"
  • Earnings Analysis: "Summarize Apple's Q4 2025 earnings and forward guidance"

Academic & Technical Research

Deep dives with proper citations:

  • Literature Review: "Research the current state of quantum error correction techniques"
  • Technology Deep Dive: "Explain transformer architectures and their evolution from attention mechanisms"
  • Scientific Topics: "What's the latest research on CRISPR gene editing for cancer treatment?"
  • Historical Analysis: "Research the history and impact of the Bretton Woods system"

Due Diligence

Comprehensive research for decision-making:

  • Startup Due Diligence: "Research [Company Name] - founding team, funding, product, market, competitors"
  • Vendor Evaluation: "Compare AWS, GCP, and Azure for enterprise AI/ML workloads"
  • Partnership Analysis: "Research potential risks and benefits of partnering with [Company]"

Research Output Formats

CellCog can deliver research in multiple formats:

| Format | Best For | |--------|----------| | Interactive HTML Report | Explorable dashboards with charts, expandable sections | | PDF Report | Shareable, printable professional documents | | Markdown | Integration into your docs/wikis | | Plain Response | Quick answers in chat |

Specify your preferred format in the prompt:

  • "Create an interactive HTML report on..."
  • "Generate a PDF research report analyzing..."
  • "Give me a markdown summary of..."

When to Use Agent Team Mode

For research, always use chat_mode="agent team" (the default).

Agent team mode enables:

  • Multi-source research and cross-referencing
  • Citation verification
  • Deeper analysis with multiple reasoning passes
  • Higher quality, more comprehensive outputs

Use chat_mode="agent" only for trivial lookups like "What's Apple's stock ticker?"


Research Quality Features

Citations (On Request)

Citations are NOT automatic. CellCog focuses on delivering accurate, well-researched content by default.

If you need citations:

  • Explicitly request them: "Include citations for all factual claims with source URLs"
  • Specify format: "Provide citations as footnotes" or "Include a references section at the end"
  • Indicate placement: "Citations inline" vs "Citations in appendix"

Without explicit citation requests, CellCog prioritizes delivering accurate information efficiently.

Data Accuracy

CellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.

Structured Analysis

Complex research is organized with clear sections, executive summaries, and actionable insights.

Visual Elements

Research reports can include:

  • Charts and graphs
  • Comparison tables
  • Timeline visualizations
  • Market maps

Example Research Prompts

Quick competitive intel:

"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."

Deep market research:

"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."

Investment analysis:

"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."

Academic deep dive:

"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."


Tips for Better Research

  1. Be specific: "AI market" is vague. "Enterprise AI automation market in healthcare" is better.

  2. Specify timeframe: "Recent" is ambiguous. "2025-2026" or "last 6 months" is clearer.

  3. Define scope: "Compare everything about X and Y" leads to bloat. "Compare X and Y on pricing, features, and market positioning" is focused.

  4. Request structure: "Include executive summary, key findings, and recommendations" helps organize output.

  5. Mention output format: "Deliver as PDF" or "Create interactive HTML dashboard" gets you the right format.

File v1.0.2:_meta.json

{ "ownerId": "kn7a96cj9q65e0bhmzahv790en80ffqm", "slug": "research-cog", "version": "1.0.2", "publishedAt": 1770411039848 }

API & Reliability

Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.

MissingCLAWHUB

Machine interfaces

Contract & API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

No protocol metadata captured.

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/snapshot"
curl -s "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/contract"
curl -s "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/trust"

Operational fit

Reliability & Benchmarks

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Machine Appendix

Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.

MissingCLAWHUB

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": []
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "CLAWHUB",
      "generatedAt": "2026-04-17T04:58:01.536Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [],
  "flattenedTokens": ""
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Clawhub",
    "href": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceUrl": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "3.2K downloads",
    "href": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceUrl": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "latest_release",
    "category": "release",
    "label": "Latest release",
    "value": "1.0.3",
    "href": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceUrl": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceType": "release",
    "confidence": "medium",
    "observedAt": "2026-02-11T01:46:52.322Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/clawhub-nitishgargiitd-research-cog/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "release",
    "title": "Release 1.0.3",
    "description": "- Added author information and explicit dependency listing for `cellcog` in the skill metadata. - Minor improvements to the prerequisites section, clarifying that `cellcog` is required for SDK setup. - No functionality changes; documentation and metadata only.",
    "href": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceUrl": "https://clawhub.ai/nitishgargiitd/research-cog",
    "sourceType": "release",
    "confidence": "medium",
    "observedAt": "2026-02-11T01:46:52.322Z",
    "isPublic": true
  }
]

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