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Health Check

Organization
codewithmukesh
health-check

Multi-dimensional health assessment for .NET projects with letter grades (A-F) using Roslyn MCP tools. Evaluates 8 dimensions: build health, code quality, architecture, test coverage, dead code, API surface, security posture, and documentation. Produces a structured report card with actionable recommendations. Load this skill when: "health check", "how healthy is this", "project health", "code quality report", "grade this project", "assess codebase", "quality audit", "technical assessment", "codebase review", "report card".

Overview

Publishercodewithmukesh
Repositorydotnet-claude-kit
Skill namehealth-check
Stars
721
Forks
170
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Health Check 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.
  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/codewithmukesh/dotnet-claude-kit.git /tmp/dotnet-claude-kit
mkdir -p .claude/skills
cp -r /tmp/dotnet-claude-kit/skills/health-check .claude/skills/health-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Health Check 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 Health Check 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 Health Check 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.

/health-check — 8-Dimension Project Assessment

What

Runs a data-driven health assessment across 8 dimensions, each graded A-F with the specific data points that produced the grade, and rolls them into a GPA. Gut feeling is not a grade: every dimension uses MCP tools or CLI commands, and every grade below A comes with specific, prioritized, effort-estimated fixes — "add test classes for OrderService, PaymentProcessor, ShippingCalculator" is actionable; "improve test coverage" is not.

This skill owns the canonical grading system for the kit. The full rubrics, GPA scale, and report template live in references/grading-rubric.md — load that file when running an assessment.

Tone is diagnostic, not punitive: a C grade is an improvement path, not a failure.

When

  • Onboarding to an unfamiliar or new project — set the baseline
  • "How healthy is this?", "grade this project", "codebase review", "report card"
  • Pre-release quality gate, or monthly/quarterly maintenance review
  • After a cleanup sprint (/de-sloppify) — re-grade to show progress
  • Tech-debt prioritization — lowest grades get the next sprint's attention

How

Step 1: Choose Scope

ScenarioDimensions
Full assessment (onboarding, pre-release, monthly review)All 8
Quick health (mid-sprint checkpoint, before a demo, after a merge)1-4 only
After major refactor1 (Build), 3 (Architecture), 4 (Tests)
Post-dependency update1 (Build), 7 (Security)
After cleanup sprintRe-grade only the cleaned dimensions

Step 2: Run the Dimensions

Read references/grading-rubric.md for the grade thresholds, then collect data per dimension. For deep code-quality dimensions, delegate to the code-reviewer agent with the code-review skill.

#DimensionData source
1Build Healthdotnet build --no-restore — errors + warnings
2Code QualityMCP detect_antipatterns — read summary, grade high-confidence only
3ArchitectureMCP get_project_graph + detect_circular_dependencies (projects AND types)
4Test CoverageMCP get_test_coverage_mapcheck applicable first (structural, not line coverage)
5Dead CodeMCP find_dead_code(scope: "solution") — grade high-confidence; ignore conventionFiltered
6API SurfaceMCP get_public_api + find_references — overexposure, return-type consistency
7Security Posturedotnet list package --vulnerable --include-transitive + secrets/auth spot check (deep dive: /security-scan)
8DocumentationXML doc coverage on public APIs + README currency

Step 2.5: Triage Gate (before any grade is assigned)

Detector output is evidence, not a grade. Pass every finding through this gate first — it is what stops a noisy count becoming a wrong letter.

  1. Read summary, not the violation list. summary.byId is complete even when the list is truncated. Never sample a truncated list and extrapolate.
  2. Drop suppressed. Record the count and summary.suppressionConfig in the report so suppression stays visible.
  3. Set aside medium. These are review items, not grade inputs. Summarise them by category; do not fix or count them.
  4. Grade high only. These are wrong regardless of context.
  5. Check invariants. If a signal contradicts something the target repo's CLAUDE.md documents as deliberate, the invariant wins — verify before grading.
  6. Read before asserting. Never describe a finding you have not opened. If you cannot open all of them, report the ones you did and say so.

Fill in the triage table from references/grading-rubric.md as you go. If the table cannot be filled, the dimension is not ready to grade.

Step 3: Grade and Aggregate

Apply the rubric thresholds exactly — never grade on a curve ("pretty good for a project this size" is how standards erode; 15 warnings is a C regardless of project size). Convert to GPA (A=4.0 … F=0.0), averaging only the dimensions actually graded — a dimension marked "Not assessed" is excluded from the GPA, never scored as an F.

Step 4: Report

Produce the report card from the template in references/grading-rubric.md: grades table with key findings, overall GPA, and priority recommendations — each with named types/files, priority order, and effort estimates. If a previous report exists, append the trend comparison table.

Example

User: /health-check

Claude: Running full 8-dimension assessment...

| Dimension | Grade | Key Finding |
|-----------|-------|-------------|
| Build Health | A | 0 errors, 2 warnings |
| Code Quality | B | 3 high-confidence findings in 4.2K lines; 31 medium untriaged |
| Architecture | A | Clean direction, 0 cycles |
| Test Coverage | Not assessed | Integration-driven suite — structural metric invalid |
| Dead Code | B | 5 unused methods (79 convention-discovered, not counted) |
| API Surface | B | 2 overexposed service types |
| Security | A | 0 vulnerable packages |
| Documentation | D | 12/30 public APIs documented |

Overall GPA: 3.1 (B) — averaged over 7 graded dimensions.

Triage: 44 AP005 raw → all log-and-rethrow wrappers (medium); 2 AP004 real.

Priority: (1) `SystemSeeder` → `TimeProvider`, ~15 min; (2) XML docs on the 8
endpoint classes, ~1 day; (3) review the 44 catch blocks or suppress by path.

Related

  • references/grading-rubric.md — canonical rubrics, GPA scale, report template
  • /de-sloppify — cleanup pipeline for the issues a health check surfaces
  • /security-scan — deep 6-layer scan behind Dimension 7
  • /code-review — per-change review (this skill grades the whole project)
  • /verify — pass/fail pipeline for a change set, not a graded assessment

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Health Check AI skill do?

Multi-dimensional health assessment for .NET projects with letter grades (A-F) using Roslyn MCP tools. Evaluates 8 dimensions: build health, code quality, architecture, test coverage, dead code, API surface, security posture, and documentation. Produces a structured report card with actionable recommendations. Load this skill when: "health check", "how healthy is this", "project health", "code quality report", "grade this project", "assess codebase", "quality audit", "technical assessment", "codebase review", "report card".

Why use Health Check on TypingMind?

Because you install it once and use it with any model. Health Check 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 Health Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codewithmukesh/dotnet-claude-kit/tree/main/skills/health-check. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Health Check?

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 Health Check?

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

Is the Health Check AI skill free?

Yes. It is published on GitHub by codewithmukesh 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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