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aidd-observability

Centralize observability and reporting for diagnostics, inventory, and logs across the AIDD runtime.

5|Updated Oct 9, 2025
Search Tags:#logs#runtime#observability#diagnostics#inventory#export#dag
One-click install
npx skills add https://github.com/GrinRus/ai_driven_dev --skill aidd-observability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aidd-observability
Source: https://github.com/GrinRus/ai_driven_dev/tree/main/skills/aidd-observability
Command: npx skills add https://github.com/GrinRus/ai_driven_dev --skill aidd-observability

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides centralized observability and reporting for diagnostics, inventory, and logs across the AIDD runtime.

Core Features & Use Cases

  • Deterministic diagnostics and health reporting from runtime components.
  • Inventory generation and export of identifiers, logs, and DAG graphs.
  • Graph and report exports to share status with stakeholders.

Quick Start

Run doctor for environment checks and then use dag_export to generate a diagnostic/export report.

Frequently Asked Questions about aidd-observability

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

FAQPage Schema
How do I export runtime diagnostics and logs for audit reporting?▼

To export runtime diagnostics and logs for audit reporting, this Skill applies health checks and inventory generation across stage workflows, ensuring consistent outputs using canonical Python entrypoints like doctor.py and tests_log.py.

What is the best way to generate a DAG graph export from stage workflows?▼

The best way to generate a DAG graph export from stage workflows is using the dag_export.py entrypoint, which centralizes observability and shares status reports with stakeholders.

Can I use this to generate an inventory of identifiers across the AIDD runtime?▼

Yes, you can generate an inventory of identifiers across the AIDD runtime by utilizing the tools_inventory.py and identifiers.py command contracts to export deterministic reports.

Does unified observability reporting cover deterministic diagnostics for the idea to QA stages?▼

Unified observability reporting covers deterministic diagnostics across all stage workflows from idea to research, plan, review, and QA, ensuring consistent tooling outputs and audits.

How do I run environment checks before exporting diagnostic reports?▼

To run environment checks before exporting diagnostic reports, execute the doctor.py entrypoint to verify runtime components, then proceed with DAG and log exports for stakeholders.

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