Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/stefanthecode/dotnet-ai-toolkit/codebase-health-dashboardnpx skills add StefanTheCode/dotnet-ai-toolkit --skill codebase-health-dashboardgit clone --depth 1 https://github.com/StefanTheCode/dotnet-ai-toolkitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00146 | $0.01151 |
| Opus 5 | $0.00073 | $0.00575 |
| Sonnet 5 | $0.00029 | $0.00230 |
| Haiku 4.5 | $0.00015 | $0.00115 |
Grade A, and why
codebase-health-dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Health Dashboard
Point this at a .NET repo and get back a single scored report card: one overall health score, eight category scores, the findings behind each score, and the highest-leverage fixes. It is the "run everything and tell me where I stand" tool.
Input - point it at your project
Works on a target: a project/solution or a GitHub URL (git clone --depth 1 <url> then scan). Find the surface first:
find <target> -name "*.sln" -o -name "*.csproj" | grep -vi "/obj/\|/bin/"
Workflow
- Run the relevant auditors on the repo and collect their findings. Use the installed tools if present, otherwise apply their checklists directly:
- Security -
aspnetcore-security-auditor,secrets-config-auditor - Architecture -
dotnet-architecture-reviewer - EF / Database -
db-performance-auditor,ef-core-query-optimizer - Performance -
async-await-auditor,memory-allocation-analyzer - Observability -
observability-gap-finder - Testing -
test-coverage-gap-finder - Dependencies -
dependency-vuln-scanner,nuget-dependency-analyzer - Code Quality -
dotnet-code-reviewer
- Security -
- Score each category with the fixed rubric below (deterministic, so re-runs are comparable).
- Fill the template
references/health-dashboard-template.html- replace/*__HEALTH_DATA__*/with the JSON object (shape below) and write it tohealth-dashboard/index.html. - Save a history record to
health/health-<YYYY-MM-DD>.jsonwith the same data, so the score can be tracked over time. - Tell the user the overall score and the top 3 actions to raise it the fastest.
Scoring rubric (deterministic)
Each category starts at 100. Subtract per finding by severity, floor at 0:
- critical: -25
- high: -12
- medium: -5
- low: -2
If a category genuinely has no applicable surface (e.g. no messaging, so some observability checks do not apply), score what exists and note it - do not invent findings to fill it.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 85 lines · 146 tokens per session scan A f17d4b89b174
codebase-health-dashboard is a skill published in the GitHub repository StefanTheCode/dotnet-ai-toolkit (19 stars, last pushed 24d ago), licensed MIT. It adds 146 tokens to every session and 1,151 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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