diagnose
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.
Best for AI workflow auditsWorks with GitHubLow risk
⌘source
- author
@github- language
- Python
★32kstars
4kforks
May 6, 2026
✦overview.md
Key Features
- ·Scores 5 quality dimensions 1-5
- ·Identifies prompt anti-patterns
- ·Evaluates context budget usage
- ·Checks tool count and descriptions
- ·Assesses architecture and safety
- ·Produces prioritized remediation
Use Cases
- →Audit a new AI workflow before deployment
- →Debug a production workflow with poor output quality
- →Optimize context usage for cost savings
- →Review tool integrations for reliability
Best for
- ✓AI workflow audits
- ✓quality assurance
- ✓systematic debugging
Not ideal for
- !simple single-prompt tasks
- !non-AI debugging
FAQs
name
diagnose
description
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.
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)
$install
1-click copynpx skills add github/awesome-copilot --skill diagnoseSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
3/ 5
goodMostly clear, but there are still a few confusing or poorly structured parts.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
3/ 5
mediumPartially actionable with several concrete steps, but still missing important details.