How should DataSleuth be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

DataSleuth answer-first brief

AI-powered data analysis application that uses specialized CrewAI agents to analyze CSV datasets, identify data-quality issues, detect anomalies, generate visualizations, and produce automated reports. DataSleuth Multi-Agent AI Data Analyst using CrewAI Overview DataSleuth is an AI-powered data analysis application that uses specialized CrewAI agents to analyze CSV datasets, identify data-quality issues, detect anomalies, generate visualizations, and produce automated reports. Users upload a CSV file and receive a structured analysis of their data through a simple Streamlit interface. Tech Stack * **Python**: Core Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

DataSleuth is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Agent DossierGITHUB REPOSSafety: 66/100

DataSleuth

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Oculus54

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

View Source

Setup snapshot

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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 Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Oculus54

profilemedium

Observed Oct 9, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

text

CSV Upload
    |
    v
Data Profiling
    |
    v
Data Quality Inspection
    |
    v
Data Cleaning
    |
    v
Statistical Analysis
    |
    v
Anomaly Detection
    |
    v
Visualization
    |
    v
Automated Report

text

DataSleuth/
├── app.py
├── agents.py
├── tasks.py
├── tools.py
├── requirements.txt
├── README.md
├── sample_data/
│   └── dataset.csv
└── outputs/
    ├── charts/
    ├── cleaned_data.csv
    └── report.html

bash

git clone https://github.com/YOUR_USERNAME/DataSleuth.git
cd DataSleuth

bash

python -m venv .venv

bash

.venv\Scripts\activate

bash

source .venv/bin/activate

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

DataSleuth

Multi-Agent AI Data Analyst using CrewAI

Overview

DataSleuth is an AI-powered data analysis application that uses specialized CrewAI agents to analyze CSV datasets, identify data-quality issues, detect anomalies, generate visualizations, and produce automated reports.

Users upload a CSV file and receive a structured analysis of their data through a simple Streamlit interface.

Tech Stack

Python: Core programming language
CrewAI: Multi-agent orchestration
Pandas & NumPy: Data processing and cleaning
Scikit-learn: Anomaly detection
Matplotlib & Seaborn: Data visualization
Streamlit: Web interface
Ollama: Optional local LLM inference

Agent Architecture

Data Profiler: Examines dataset structure, column types, missing values, and duplicates.
Data Cleaner: Recommends and applies approved cleaning operations.
Statistical Analyst: Calculates descriptive statistics and identifies relationships between variables.
Anomaly Detector: Identifies unusual observations using statistical methods and machine learning.
Report Generator: Summarizes findings and generates a report with charts and evidence.

Workflow

CSV Upload
    |
    v
Data Profiling
    |
    v
Data Quality Inspection
    |
    v
Data Cleaning
    |
    v
Statistical Analysis
    |
    v
Anomaly Detection
    |
    v
Visualization
    |
    v
Automated Report

CrewAI coordinates the agents, while Python libraries perform the actual calculations to ensure numerical results are reproducible.

Core Features

CSV dataset upload and preview
Automatic dataset profiling
Missing-value and duplicate detection
Data cleaning with an operation log
Descriptive statistics and correlation analysis
Statistical and ML-based anomaly detection
Automatic chart generation
AI-generated analysis summaries
Downloadable cleaned CSV and HTML report

Project Structure

DataSleuth/
├── app.py
├── agents.py
├── tasks.py
├── tools.py
├── requirements.txt
├── README.md
├── sample_data/
│   └── dataset.csv
└── outputs/
    ├── charts/
    ├── cleaned_data.csv
    └── report.html

Getting Started

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/DataSleuth.git
cd DataSleuth

2. Create a virtual environment

python -m venv .venv

Activate it:

Windows

.venv\Scripts\activate

Linux / macOS

source .venv/bin/activate

3. Install dependencies

pip install crewai streamlit pandas numpy scikit-learn matplotlib seaborn

Add any additional dependencies required by the selected LLM integration.

4. Run the application

streamlit run app.py

Future Improvements

Support Excel and JSON datasets
Add interactive visualizations
Introduce natural-language questions about datasets
Add downloadable PDF reports
Support larger datasets with background processing

Project Goal

Build a practical multi-agent AI system that combines LLM-based reasoning with reliable data-processing tools to automate exploratory data analysis.

Note: This repository describes the intended architecture. Features should only be marked as implemented after their corresponding code and tests are complete.

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

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.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

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://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T23:49:35.311Z"
    }
  },
  "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": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Oculus54",
    "href": "https://github.com/oculus54/DataSleuth",
    "sourceUrl": "https://github.com/oculus54/DataSleuth",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T11:50:37.692Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T11:50:37.692Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-oculus54-datasleuth/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]