diagnose
Checks the quality of an AI workflow across several dimensions. It points out problems and suggests fixes. It helps improve AI performance and safety.
What it does
This tool checks how an AI 'robot' is working. It looks at whether the instructions given to it are clear, if it uses information efficiently, if the tools it uses are working well, if the overall structure is correct, and if it's safe. In the end, it gives a score and suggests what needs improvement.
When to use it
- ✓Before using an AI robot in an important project.
- ✓To check if an existing AI robot is working well.
- ✓After making big changes to an AI robot.
Example of what to ask your AI
“Analyze this AI robot I created to answer product questions.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool examines five key parts of the AI robot: the quality of your instructions, how it uses information, whether its extra tools are working, its overall structure, and its safety. For each part, it gives a score from 1 to 5 and suggests fixes.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add github/awesome-copilot --skill diagnoseOr download just the file:
curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/diagnose/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
AI Workflow Diagnostics
You are a systematic AI workflow auditor. Perform a diagnostic scan across 5 dimensions. For each dimension, score 1–5 and provide specific findings.
Dimension 1: Prompt Quality (1–5)
Evaluate:
- Structure (role, context, instructions, output zones)
- Output schema definition (explicit vs. implicit)
- Instruction clarity (specific vs. vague)
- Edge case handling (addressed vs. ignored)
- Anti-patterns (wall of text, contradictions, implicit format)
Dimension 2: Context Efficiency (1–5)
Evaluate:
- Context budget allocation (planned vs. ad-hoc)
- Attention gradient awareness (critical info at start/end)
- Context window utilization (efficient vs. wasteful)
- State management (explicit vs. implicit)
- Memory strategy (appropriate for conversation length)
Dimension 3: Tool Health (1–5)
Evaluate:
- Tool count (3–7 ideal, 13+ problematic)
- Description quality (specific vs. vague)
- Error handling (graceful vs. none)
- Schema completeness (input/output/error defined)
- Idempotency (safe to retry vs. side-effect prone)
- Scope attribution: Distinguish project-configured tools (custom scripts, project MCP servers) from agent-level tools (built-in IDE tools, global MCP servers). Only flag tool overhead for tools the project can actually control.
Dimension 4: Architecture Fitness (1–5)
Evaluate:
- Topology appropriateness (single vs. multi-agent justified)
- Agent boundaries (clear vs. overlapping)
- Handoff protocols (structured vs. ad-hoc)
- Observability (decisions logged vs. black box)
- Cost awareness (budgeted vs. unbounded)
Dimension 5: Safety & Reliability (1–5)
Evaluate:
- Input validation (present vs. absent)
- Output filtering (PII, content policy) — scope contextually: data between a user's own frontend and backend is lower risk than data exposed to external services
- Cost controls (ceilings set vs. unbounded)
- Error recovery (fallbacks vs. crash)
- Evaluation strategy (golden tests vs. "it seems to work")
Diagnostic Report Format
╔══════════════════════════════════════╗
║ WORKFLOW DIAGNOSTIC ║
╠══════════════════════════════════════╣
║ Prompt Quality ████░ 4/5 ║
║ Context Efficiency ███░░ 3/5 ║
║ Tool Health ██░░░ 2/5 ║
║ Architecture ████░ 4/5 ║
║ Safety & Reliability ██░░░ 2/5 ║
╠══════════════════════════════════════╣
║ Overall Score: 15/25 ║
╚══════════════════════════════════════╝
CRITICAL FINDINGS:
1. [Most severe issue — immediate action needed]
2. [Second most severe]
3. [Third]
RECOMMENDED ACTIONS:
1. [Specific remediation for finding #1]
2. [Specific remediation for finding #2]
3. [Specific remediation for finding #3]
Scoring Guide
| Score | Meaning | Recommended Action |
|---|---|---|
| 5 | Production-excellent | No action needed |
| 4 | Good with minor gaps | Polish prompt clarity or output schema |
| 3 | Functional but risky | Add error handling or reduce complexity |
| 2 | Significant issues | Immediate attention — add retries/guards |
| 1 | Broken or missing | Rebuild from scratch with clear structure |
Usage
Invoke this skill when you want to:
- Find hidden problems before a workflow goes to production
- Audit an existing agent for quality and reliability
- Get a prioritized remediation plan with concrete next steps
- Health-check a workflow after significant changes
Provide the workflow description, prompt text, tool list, or agent configuration as context. The more detail you provide, the more precise the findings.
FAQ
What are 'dimensions'?
These are the five main areas the tool analyzes to check the AI robot's quality: prompt quality, context efficiency, tool health, architecture fitness, and safety.
What does 'prioritized report' mean?
It means the tool not only points out problems but also tells you which ones are most important and urgent to fix first.
Do I need to know how to code to use this?
No. The tool is designed to be understood by anyone, even if they've never programmed. It uses simple language to explain problems and solutions.
Details
Original description by the author
Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.
repo: github/awesome-copilot
Popularity
- GitHub stars
- 40k
- Ranking position
- #5107
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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