aoa-session-self-diagnose

Name: aoa-session-self-diagnose
Availability: InStock
Author: Dionysus

Classify drift, friction, and proof gaps into evidence-linked diagnosis packets.

4|Updated Mar 18, 2026

Search Tags:#receipts #diagnosis #drift-analysis #session-review #evidence-tracing #automation-readiness

Ready to run

npx skills add https://github.com/8Dionysus/aoa-skills --skill aoa-session-self-diagnose

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aoa-session-self-diagnose
Source: https://github.com/8Dionysus/aoa-skills/tree/main/.agents/skills/aoa-session-self-diagnose
Command: npx skills add https://github.com/8Dionysus/aoa-skills --skill aoa-session-self-diagnose

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Reviews and harvested session artifacts often contain repeated friction, contradictions, or blurred ownership that block honest repair and automation; this Skill converts reviewed session evidence into a bounded, evidence-linked diagnosis packet that separates symptoms from probable causes and preserves unknowns without mutating the system.

Core Features & Use Cases

Classify drift types, symptoms, probable causes, and owner-layer hints from reviewed (non-live) sessions.
Emit bounded receipts that remain descriptive and evidence-linked, including a DIAGNOSIS_PACKET and a CORE_SKILL_APPLICATION_RECEIPT, while preserving unknowns and avoiding silent repair.
Use case: postmortem or session-harvest analysis where the next step must be diagnosis before any repair or automation attempt.

Quick Start

Analyze the attached reviewed session transcript, classify drift and probable causes, and produce a DIAGNOSIS_PACKET with owner hints plus a bounded diagnosis receipt.

Frequently Asked Questions about aoa-session-self-diagnose

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

FAQPage Schema

What is an evidence-linked session diagnosis packet?▼

It is a bounded summary that classifies drift, friction, and ownership confusion from reviewed session artifacts into symptom and probable cause categories while preserving unknowns. The packet relies on explicit citation of repository evidence refs to maintain read-only integrity.

How do I analyze session drift and repeated failure patterns from a postmortem?▼

Analyze session drift by classifying drift types, symptoms, and probable causes from the reviewed transcript into a bounded diagnosis packet with owner-layer hints. Emit bounded receipts that follow stats-event-envelope rules to preserve unknowns without silent repair.

Can I use session diagnosis for live automation and repair attempts?▼

No, this diagnosis process applies strictly to reviewed non-live session artifacts, harvest packets, and postmortems. It requires read-only access to repository evidence and references, ensuring diagnosis is completed before any repair or automation attempt.

Do I need repository evidence to trace session proof gaps?▼

Yes, tracing proof gaps requires read-only access to repository evidence and references. The diagnosis process demands explicit citation of evidence refs to validate owner-layer hints and generate bounded receipts without mutating the system.

What is the best way to separate symptoms from probable causes in a session review?▼

The best way to separate symptoms from probable causes is to convert reviewed session evidence into a bounded diagnosis packet. This approach classifies drift and friction while explicitly preserving unknowns before attempting any automation.

Why does my session review fail to produce clear ownership and automation readiness?▼

Session reviews fail to produce automation readiness when blurred ownership and repeated friction block honest repair. Converting reviewed evidence into a bounded diagnosis packet with explicit evidence refs separates symptoms from causes and clarifies owner-layer hints.