Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/marcosd4h/DeepExtractRuntimenpx agentmods add commands/marcosd4h/deepextractruntime/winrtWrote 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/marcosd4h/deepextractruntime/winrt)<a href="https://agentmods.dev/commands/marcosd4h/deepextractruntime/winrt"><img src="https://agentmods.dev/badge/commands/marcosd4h/deepextractruntime/winrt/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/marcosd4h/deepextractruntime/winrt"><img src="https://agentmods.dev/badge/commands/marcosd4h/deepextractruntime/winrt.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.01193 |
| Opus 5.5 | $0.00000 | $0.00477 |
| Sonnet 5.5 | $0.00000 | $0.00239 |
| Haiku 4.5 | $0.00000 | $0.00119 |
Grade A, and why
winrt 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 1mo 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.
This is a copy
86% identical to com — 68 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WinRT Analysis
Overview
Analyze WinRT server interfaces in Windows binaries using ground-truth extraction data. Enumerates server classes, maps the privilege-boundary attack surface, audits security properties, classifies entry points, and identifies privilege escalation targets.
The text after /winrt is the module name and optional subcommand (e.g., /winrt TaskFlowDataEngine.dll, /winrt surface, /winrt privesc).
Subcommands
| Subcommand | Usage | Purpose |
|---|---|---|
| (default) | /winrt <module> |
Enumerate WinRT server classes, interfaces, methods |
workspace |
/winrt workspace |
Discover which workspace modules implement WinRT servers |
surface |
/winrt surface [module] |
Risk-ranked WinRT attack surface (module or system-wide) |
methods |
/winrt methods <module_or_class> |
List methods with optional pseudo-IDL |
classify |
/winrt classify <module> |
Semantic classification of WinRT entry points |
audit |
/winrt audit <module> |
WinRT-specific security audit |
privesc |
/winrt privesc [--top N] |
Find privilege escalation targets |
IMPORTANT: Execution Model
Execute immediately. Do NOT ask for confirmation before running scripts. Read the winrt-interface-analysis SKILL.md to understand available scripts and their options, then run the appropriate script(s) for the subcommand.
Execution Context
- Working directory: Workspace root (scripts handle their own path setup)
- Output: Scripts support
--jsonfor machine-readable output - Data: Read-only access to WinRT extraction data via
helpers.winrt_index
Steps
Step 0: Preflight Validation
Parse the user's input to determine the subcommand and target. If a module name is given, verify it exists in the WinRT index by running:
python .claude/skills/winrt-interface-analysis/scripts/resolve_winrt_server.py <module> --json
If no servers are found, report this and suggest checking module name spelling or using surface --system-wide to see all available modules.
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.
- 1mo ago First seen · 121 lines · 0 tokens per session scan A 3146f9e4746a
winrt is a command published in the GitHub repository marcosd4h/DeepExtractRuntime (21 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,193 tokens. A static security scan graded it A with 0 findings. It is 86% identical to com, differing in 68 lines, and is treated as a copy.
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