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/batteryshark/rekit/dotnet-analyzenpx skills add batteryshark/rekit --skill dotnet-analyzegit clone --depth 1 https://github.com/batteryshark/rekitWrote 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/skills/batteryshark/rekit/dotnet-analyze)<a href="https://agentmods.dev/skills/batteryshark/rekit/dotnet-analyze"><img src="https://agentmods.dev/badge/skills/batteryshark/rekit/dotnet-analyze.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 | $0.00086 | $0.00628 |
| Opus 5 | $0.00043 | $0.00314 |
| Sonnet 5 | $0.00017 | $0.00126 |
| Haiku 4.5 | $0.00009 | $0.00063 |
Grade A, and why
dotnet-analyze 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 4d 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.
What it actually says
.NET Assembly Analyzer
Static triage of .NET / CLR managed assemblies (.dll / .exe) with
dnfile. No .NET runtime required — it
parses CLR metadata directly.
When to use
You have a Windows binary that turns out to be managed .NET (a PE with a CLR
header) rather than native code. pe-analyze sees only the tiny native stub; the
real behaviour lives in IL + metadata. This reads that metadata. If you're not sure
which it is, point this at it — a native PE is reported as such with a pointer to
pe-analyze.
What it reports
- Identity — runtime version (e.g.
v4.0.30319), assembly name, IL-only vs mixed-mode (native code →DOTNET.MIXED_MODE), strong-name signing (DOTNET.NO_STRONGNAME). - References — referenced assemblies (name + version), and unmanaged module
refs (
DOTNET.UNMANAGED_MODULE). - P/Invoke surface — every
DllImport(managed method → native API in a native DLL), classified by capability (inject / exec / network / anti-debug / crypto) →DOTNET.PINVOKE. This is the key signal: pure-IL malware still has to reach the OS through P/Invoke. - Suspicious refs — dynamic-code / process / WMI namespaces (
DOTNET.SUSPICIOUS_REF). - Counts of types and methods.
Strictly static — parses metadata tables via dnfile; never loads the assembly into a CLR, JITs, or executes it. Safe on hostile files.
Usage
rekit run dotnet-analyze ./payload.exe
rekit run dotnet-analyze ./managed.dll --format json
Native PE → {"ok": true, "isDotNet": false, "note": "… use pe-analyze"}.
Non-PE → honest failure.
Prerequisites
- python3 ≥ 3.8 — dnfile is installed under
scripts/site(pure-python). No .NET runtime, no network/install at analysis time.
Rebuilding
scripts/site is populated from the pinned scripts/requirements.txt by
scripts/build.sh (uv pip install --target, build time only).
What ships with it
3 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.
- 4d ago First seen · 56 lines · 86 tokens per session scan A c0f0e8714adc
dotnet-analyze is a skill published in the GitHub repository batteryshark/rekit (11 stars, last pushed 26d ago), licensed Apache-2.0. It adds 86 tokens to every session and 628 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…