.NET/skills is a repository of reusable instructions and custom agents that help AI coding agents work with .NET and C#. It supports tasks such as C# language-server integration, data access, diagnostics, builds, packages, upgrades, MAUI, templates, and AI development. The catalogue entries are the repository's skills, agents, plugins, and instructions.
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.
git clone --depth 1 https://github.com/dotnet/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/dotnet/skills/evaluate-skill-descriptions)<a href="https://agentmods.dev/commands/dotnet/skills/evaluate-skill-descriptions"><img src="https://agentmods.dev/badge/commands/dotnet/skills/evaluate-skill-descriptions.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00018 | $0.00912 |
| Opus 5 | $0.00009 | $0.00456 |
| Sonnet 5 | $0.00004 | $0.00182 |
| Haiku 4.5 | $0.00002 | $0.00091 |
Grade A, and why
evaluate-skill-descriptions 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 yesterday.
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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate Skill Description Quality
This repo contains several skills for helping AI coding agents like GitHub Copilot and Claude Code with .NET-related coding tasks. Evaluate how good the description is in the YAML front matter for each skill under plugins/**/skills/*/SKILL.md and rate them on a scale of 1–5 where 5 is the best.
What the Description Is For
The description field in each skill's frontmatter is the only information a coding agent sees (alongside the skill name) when deciding whether to load the skill. Agents use progressive loading — they read the full skill content only after they decide the skill is relevant. A poor description means a great skill never gets used.
A good description answers four questions:
- What it does — the concrete outcome or capability
- When to use it — trigger phrases, scenarios, or user intents that should activate the skill
- When NOT to use it — clear boundaries, non-goals, or nearby-but-wrong intents where the skill should be skipped
- Key capabilities — enough specifics to differentiate it from other skills
Rating Scale
| Rating | Meaning |
|---|---|
| 5 | Excellent — clearly states what, when to use, when NOT to use, and key capabilities; includes concrete trigger phrases, user-intent signals, and clear non-goals/boundaries |
| 4 | Good — covers what and when to use and mentions some boundaries or non-goals, but could be more specific on triggers, edge cases, or capabilities |
| 3 | Adequate — describes the skill but is missing clear when-to-use or when-NOT-to-use guidance, lacks strong trigger phrases, or is too generic to reliably match user intent |
| 2 | Weak — vague or overly technical; an agent would struggle to know when to activate it or might over-trigger because boundaries and non-goals are unclear |
| 1 | Poor — missing, trivially short, or provides almost no actionable information |
Examples of Good Descriptions (and Why)
# Good — specific, actionable, with clear boundaries
description: Analyzes Figma design files and generates developer handoff
documentation. Use when user uploads .fig files, asks for "design specs",
"component documentation", or "design-to-code handoff". Avoid when user is
asking about general UX research, copywriting, or non-Figma assets.
# Good — includes trigger phrases and non-goals
description: Manages Linear project workflows including sprint planning, task
creation, and status tracking. Use when user mentions "sprint", "Linear
tasks", "project planning", or asks to "create tickets". Avoid when the
user is managing GitHub issues or other ticketing tools instead of Linear.
# Good — clear value proposition and scope limits
description: End-to-end customer onboarding workflow for PayFlow. Handles
account creation, payment setup, and subscription management. Use when user
says "onboard new customer", "set up subscription", or "create PayFlow
account". Avoid when working with other payment providers or generic CRM
onboarding flows unrelated to PayFlow.
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.
- yesterday First seen · 79 lines · 18 tokens per session scan A 6a469b3813fd
evaluate-skill-descriptions is a command published in the GitHub repository dotnet/skills (5,383 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 912 once invoked, about $0.0001 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 2026-09-07.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.