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Diagnose

Community
sharpdeveye
diagnose

Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.

Overview

Publishersharpdeveye
Repositorymaestro
Skill namediagnose
Stars
591
Forks
73
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sharpdeveye on GitHub. Read the source before you install it.

Installation

Install the Diagnose AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sharpdeveye/maestro/tree/main/source/skills/diagnose
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sharpdeveye/maestro.git /tmp/maestro
mkdir -p .claude/skills
cp -r /tmp/maestro/source/skills/diagnose .claude/skills/diagnose
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Diagnose in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Diagnose on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Diagnose is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.


Perform a systematic diagnostic scan across 5 dimensions. For each dimension, score 1-5 and provide specific findings.

Dimension 1: Prompt Quality (1-5)

Evaluate:

  • Structure (4-zone pattern: role, context, instructions, output)
  • Output schema definition (explicit vs. implicit)
  • Instruction clarity (specific vs. vague)
  • Edge case handling (addressed vs. ignored)
  • Anti-patterns present (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 between project-configured tools (e.g., custom scripts, project MCP servers) and agent-level tools (e.g., 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 flowing between a user's own frontend and backend (e.g., authenticated sessions, internal APIs) is lower risk than data exposed to external services or third-party APIs
  • Cost controls (ceilings set vs. unbounded)
  • Error recovery (fallbacks vs. crash)
  • Evaluation strategy (golden tests vs. "it seems to work")

Diagnostic Report Format

text
╔══════════════════════════════════════╗
║          MAESTRO 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. Run /fortify to add error handling (addresses Tool Health + Safety)
2. Run /streamline to reduce tool count (addresses Tool Health)
3. Run /refine for prompt structure improvements (addresses Prompt Quality)

Maestro Command Mapping

Every recommended action MUST reference the specific Maestro command that addresses it. Use this mapping:

Dimension GapMaestro CommandWhen to Recommend
Prompt structure, clarity, output schema/refineScore ≤ 4 on Prompt Quality
Context budget, attention gradient, memory/streamlineScore ≤ 3 on Context Efficiency
Tool errors, missing tools, redundant tools/fortifyScore ≤ 3 on Tool Health
Tool count reduction, unused tools/streamlineTool count > 7 or unused tools found
Safety gaps, error recovery, validation/fortifyScore ≤ 3 on Safety & Reliability
Test coverage, golden tests, evaluation/guardNo automated tests or evaluation strategy
Architecture boundaries, observability/calibrateScore ≤ 3 on Architecture Fitness

Do NOT give generic manual actions (e.g., "Add Vitest", "Create a rollback script") without also specifying which Maestro command the user should run to implement it. The recommended action format is:

Run /<command> to [specific action] (addresses [Dimension] #[gap number])

Scoring Guide

ScoreMeaningMaestro Action
5Production-excellentNo action needed
4Good with minor gaps/refine for polish
3Functional but risky/fortify or /streamline for targeted fix
2Significant issues/fortify + /guard — immediate attention
1Broken or missing/onboard-agent — rebuild required

Diagnostic Checklist

  • All 5 dimensions scored with specific evidence
  • Critical findings listed in priority order
  • Each finding includes specific file/component location
  • Recommended actions reference specific Maestro commands (see Command Mapping above)
  • Overall score calculated and report generated

Recommended Next Step

After diagnosis, run the command mapped to your lowest-scoring dimension. For a general improvement sequence: /fortify → /streamline → /refine.

NEVER:

  • Give all 5s unless the workflow is genuinely production-excellent
  • Skip dimensions — score all 5 even if some seem fine
  • Diagnose without reading the actual workflow code/config
  • Recommend changes without specific findings to support them
  • Give generic manual actions without mapping them to a Maestro command

Frequently asked questions

What does the Diagnose AI skill do?

Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.

Why use Diagnose on TypingMind?

Because you install it once and use it with any model. Diagnose is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Diagnose in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sharpdeveye/maestro/tree/main/source/skills/diagnose. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Diagnose?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Diagnose?

As many as you like. As long as a model supports skills, you can use Diagnose with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Diagnose AI skill free?

Yes. It is published on GitHub by sharpdeveye under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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