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/marcosd4h/deepextractruntime/winrt-interface-analysisnpx skills add marcosd4h/DeepExtractRuntime --skill winrt-interface-analysisgit clone --depth 1 https://github.com/marcosd4h/DeepExtractRuntimeWrote 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/marcosd4h/deepextractruntime/winrt-interface-analysis)<a href="https://agentmods.dev/skills/marcosd4h/deepextractruntime/winrt-interface-analysis"><img src="https://agentmods.dev/badge/skills/marcosd4h/deepextractruntime/winrt-interface-analysis.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 | $0.00074 | $0.01815 |
| Opus 5 | $0.00037 | $0.00907 |
| Sonnet 5 | $0.00015 | $0.00363 |
| Haiku 4.5 | $0.00007 | $0.00181 |
Grade A, and why
winrt-interface-analysis 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.
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WinRT Interface Analysis
Purpose
Query and analyze WinRT (Windows Runtime) server registrations extracted from Windows system binaries. Uses pre-built extraction data that maps every binary to its WinRT activation classes, interface methods, pseudo-IDL definitions, trust levels, SDDL permissions, server identities, and activation types. The unique value is privilege-boundary risk scoring across four access contexts defined by caller integrity level and server process privilege.
When to Use
- Enumerate WinRT classes and interfaces in a module
- Map the WinRT attack surface ranked by privilege-boundary risk
- Find privilege escalation targets (medium-IL caller to SYSTEM server)
- Classify WinRT entry points by semantic category
- Audit WinRT server security (permissive SDDL, SYSTEM identity, BaseTrust)
- Confirm decompiled functions are WinRT entry points
- View pseudo-IDL interface definitions
When NOT to Use
- General function explanation -- use re-analyst
- COM interface reconstruction from vtable patterns -- use com-interface-reconstruction
- RPC interface analysis -- use rpc-interface-analysis
- Non-WinRT attack surface mapping -- use map-attack-surface
Data Sources
- WinRT index (
helpers/winrt_index.py): Singleton loaded fromconfig/assets/winrt_data/across four access contexts. - Access contexts (caller IL x server privilege):
extracted_high_il/all_servers-- high-IL caller, elevated + regular processesextracted_high_il/privileged_servers-- high-IL caller, privileged processes (SYSTEM/high)extracted_medium_il/medium_il/all_servers-- medium-IL caller, elevated + regular processesextracted_medium_il/medium_il/privileged_servers-- medium-IL caller, privileged processes (SYSTEM/high)
- Per-context file:
winrt_servers.json(binary-keyed; contains server metadata, interfaces, methods, pseudo-IDL, and procedure lists per binary). - Per-module analysis DB: Decompiled code for WinRT handler functions.
What ships with it
8 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.
- reference.md 5.5 KB
- scripts/_common.py 1.6 KB runs code
- scripts/audit_winrt_security.py 4.3 KB runs code
- scripts/classify_winrt_entrypoints.py 4.4 KB runs code
- scripts/enumerate_winrt_methods.py 3.1 KB runs code
- scripts/find_winrt_privesc.py 3.3 KB runs code
- scripts/map_winrt_surface.py 3.8 KB runs code
- scripts/resolve_winrt_server.py 4.2 KB runs code
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 · 183 lines · 74 tokens per session scan A 513500b9aa1a
winrt-interface-analysis is a skill published in the GitHub repository marcosd4h/DeepExtractRuntime (20 stars, last pushed 4mo ago), licensed MIT. It adds 74 tokens to every session and 1,815 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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