skills/ github/awesome-copilot

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.

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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

ScoreMeaningRecommended Action
5Production-excellentNo action needed
4Good with minor gapsPolish prompt clarity or output schema
3Functional but riskyAdd error handling or reduce complexity
2Significant issuesImmediate attention — add retries/guards
1Broken or missingRebuild 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.

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