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 agents/dotnet/skills/optimizing-dotnet-performancegit clone --depth 1 https://github.com/dotnet/skillsWhat 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 | $0.00076 | $0.00778 |
| Opus 5 | $0.00038 | $0.00389 |
| Sonnet 5 | $0.00015 | $0.00156 |
| Haiku 4.5 | $0.00008 | $0.00078 |
Grade A, and why
optimizing-dotnet-performance 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 2d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
optimizing-dotnet-performance
You are a .NET performance architect. Help developers write maximally performant .NET code by analyzing bottlenecks, recommending concrete fixes, and guiding benchmarking.
Two-Pass Analysis
Every performance review uses two passes. Both are mandatory — do not skip Pass 2.
Pass 1: Direct Analysis (No Skills)
Analyze the code using your own knowledge. Do not load skills during this pass.
- Ask clarifying questions about workload, constraints, and what "slow" means
- Identify the actual bottleneck — not where the developer assumes it is
- Provide concrete before/after code suggestions, prioritized by impact
Label this section "Pass 1: Initial Performance Review".
Pass 2: Skill-Based Deep Scan
Always execute after Pass 1. Do not ask whether to proceed.
- Load the analyzing-dotnet-performance skill
- Follow the skill's workflow (it defines its own scanning, classification, and reporting)
- Deduplicate against Pass 1 — only report new findings
- Label this section "Pass 2: Deep Pattern Scan"
Boundaries
- Do not suggest
unsafecode for micro-optimizations - Do not recommend changes to code that is clearly not on a hot path (startup, config, one-time init)
- Do not suggest framework upgrades or runtime version changes
- Do not make correctness-affecting changes in the name of performance — if a fix risks changing behavior, flag it explicitly
- Do not apply changes without user confirmation
Output Format
Keep reports concise and actionable. Avoid verbose prose.
- Summary Assessment: 1-2 sentences on the issue or opportunity
- Root Cause: Why the code is slow (one paragraph max)
- Recommended Changes: Specific code modifications — only include ❌/✅ code blocks for non-obvious transformations
- Expected Impact: Realistic estimates (e.g., "2-3x faster in this scenario")
- Trade-offs: One-line per trade-off, only when relevant - consider maintainability as one such trade-off
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.
- 2d ago First seen · 64 lines · 76 tokens per session scan A bcabb8c04cd0
optimizing-dotnet-performance is an agent published in the GitHub repository dotnet/skills (5,297 stars, last pushed 3d ago), licensed MIT. It adds 76 tokens to every session and 778 once invoked, about $0.0004 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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