questio-investigate

Name: questio-investigate
Availability: InStock
Author: arashshahidi1997

Guide end-to-end pipeline anomaly investigations using questio and MCP tooling.

1|Updated Mar 9, 2026

Search Tags:#debugging #research #pipeline #investigate #anomaly #pipeline-quality #questio

Ready to run

npx skills add https://github.com/arashshahidi1997/projio --skill questio-investigate

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: questio-investigate
Source: https://github.com/arashshahidi1997/projio/tree/main/docs/prompts/skills/questio-investigate
Command: npx skills add https://github.com/arashshahidi1997/projio --skill questio-investigate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Agent-driven deep dive into anomalies, failures, or unexpected outputs in pipelines, providing a structured, repeatable approach to identify scope, gather context, inspect outputs, compare against expectations, narrow the cause, and report findings.

Core Features & Use Cases

Structured investigation workflow: guides scoping, data context gathering, and evidence collection to diagnose issues.
Health checks and context: leverages MCP tooling to assess flow status, run health, and relevant logs for rapid root-cause localization.
Evidence-driven reporting: creates observation notes and consolidates findings for escalation or remediation across research pipelines.

Quick Start

Describe the issue clearly and trigger questio to begin a guided investigation.

Frequently Asked Questions about questio-investigate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I investigate pipeline anomalies and data quality issues?▼

Investigating pipeline anomalies involves guiding an end-to-end workflow that scopes the issue, gathers context, inspects outputs, and tests hypotheses to provide evidence-based root-cause analysis.

What is the best way to debug failed pipeline runs and unexpected outputs?▼

Debugging failed pipeline runs is best handled by leveraging MCP tooling to assess flow status, check run health, and review relevant logs for rapid root-cause localization.

How do I conduct an end-to-end investigation for a data pipeline failure?▼

You can conduct an end-to-end investigation by following a defined workflow with checks for health, logs, prior decisions, and outputs, enabling structured evidence collection and observation-note generation.

Can I use MCP tooling to scope and diagnose research pipeline failures?▼

Yes, you can use MCP tooling to assess flow status and run health, enabling rapid root-cause localization and evidence-driven reporting for research pipeline failures.

How do I generate observation notes for pipeline anomaly escalation?▼

You can generate observation notes by consolidating findings from your structured investigation workflow, creating evidence-driven reports that support escalation or remediation across research pipelines.