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Diagnose

GitHubOfficialFirst committed by alfersugoDistributed by GitHub

  • Skill
  • Review and audit
  • Agent tooling
  • MIT
  • No issues found
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From 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.
— the description by alfersugo

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The diagnose skill provides a structured framework for evaluating the health and reliability of AI agent workflows. It performs a comprehensive audit across five critical dimensions: prompt quality, context efficiency, tool health, architecture fitness, and safety. By scoring each dimension on a scale of 1 to 5, the skill generates a visual diagnostic report and a prioritized list of remediation actions, helping developers identify bottlenecks, risks, and areas for optimization before deployment.

What it does

  • Evaluates prompt structure, clarity, and output schema definitions.
  • Assesses context window utilization and memory management strategies.
  • Audits tool health, including schema completeness and error handling.
  • Analyzes architectural fitness, such as agent boundaries and observability.
  • Reviews safety protocols, including input validation and cost controls.
  • Produces a scored summary report with actionable remediation steps.

When to use it

  • Before moving an AI workflow into a production environment.
  • When troubleshooting performance issues or unexpected agent behavior.
  • To perform a periodic health check after modifying agent configurations.
  • When you need a prioritized list of improvements for an existing agent.

Advantages

  • Provides a standardized scoring system for objective workflow assessment.
  • Identifies specific anti-patterns in prompt and tool design.
  • Offers clear, prioritized remediation paths for identified issues.
  • Helps balance complexity and cost in multi-agent architectures.

Usage examples

“Diagnose my customer support agent workflow. Here is the system prompt and the list of tools it uses.”
The agent provides a 5-dimension score report and identifies that the tool definitions are too vague, recommending specific schema updates.
“Run a diagnostic scan on this research agent. It keeps hitting context limits.”
The agent audits the context efficiency and identifies that the memory strategy is inefficient, suggesting a more targeted summarization approach.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/diagnose/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip diagnose.zip -d .agents/skills

Documentation by alfersugo

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.

Files

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of alfersugoalfersugo
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/diagnose · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
alfersugo

AutoGineer is not affiliated with or endorsed by alfersugo or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download diagnose.zip