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 skills add Postpartum-genushyacinthus29/dotnet-skills --skill dotnet-managedcode-markitdowngit clone --depth 1 https://github.com/Postpartum-genushyacinthus29/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/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-managedcode-markitdown)<a href="https://agentmods.dev/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-managedcode-markitdown"><img src="https://agentmods.dev/badge/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-managedcode-markitdown/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/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-managedcode-markitdown"><img src="https://agentmods.dev/badge/skills/postpartum-genushyacinthus29/dotnet-skills/dotnet-managedcode-markitdown.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 | $0.00039 | $0.00370 |
| Opus 5 | $0.00019 | $0.00185 |
| Sonnet 5 | $0.00008 | $0.00074 |
| Haiku 4.5 | $0.00004 | $0.00037 |
Grade A, and why
dotnet-managedcode-markitdown 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 9d 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
91% identical to managedcode-markitdown — 6 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.
What it actually says
ManagedCode.MarkItDown
Trigger On
- integrating
ManagedCode.MarkItDowninto document ingestion flows - converting office or rich-text content into Markdown for downstream processing
- reviewing indexing, chunking, or AI-preparation pipelines that depend on Markdown output
- documenting file-conversion steps for a .NET application
Workflow
- Identify the document sources the app actually handles.
- Decide where Markdown conversion belongs in the pipeline:
- before indexing
- before chunking
- before AI summarization or enrichment
- Keep conversion isolated behind one ingestion or processing service instead of scattering format handling everywhere.
- Validate real converted output for structure, links, headings, and attachment handling.
- Document which downstream stage depends on the produced Markdown.
flowchart LR
A["Input document"] --> B["ManagedCode.MarkItDown conversion"]
B --> C["Markdown output"]
C --> D["Indexing, chunking, or AI workflow"]
Deliver
- guidance on where ManagedCode.MarkItDown fits in a real processing pipeline
- conversion-boundary recommendations for application design
- output-validation expectations for document ingestion
Validate
- the converted Markdown is good enough for the actual downstream consumer
- conversion is isolated in one clear pipeline step
- tests or review samples cover the real input formats the application claims to support
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.
- 9d ago First seen · 47 lines · 39 tokens per session scan A c092267e6a6f
dotnet-managedcode-markitdown is a skill published in the GitHub repository Postpartum-genushyacinthus29/dotnet-skills (10 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 370 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to managedcode-markitdown, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
data-report
Turns CSV, Excel, or JSON data into a polished visual report page.
docetl
Build and run LLM-powered data processing pipelines with DocETL. Use when users say "docetl", want to analyze unstructured data, process documents, extract information, or run ETL tasks on text. Helps with data collection, pipeline creation, execution, and optimization.
Workspace Data Analyst
Analyze CSV files in the workspace and summarize insights.
data-report
A template for turning CSV, Excel, or JSON data into a visual report page. It includes summary metrics, charts, a data table, and written observations based on the supplied data.
malloy-document
Add documentation with #(doc) tags to Malloy models so fields and sources are described in plain language. Use when user asks to "add documentation", "add doc tags", "document the model", or wants fields and sources described for natural-language search and discovery. For declaring parameterizable filters with…
backend-bulk-import
Enforce bulk data import patterns including CSV/Excel parsing, file upload handling, validation pipeline, progress tracking, batch processing, error reporting, rollback on failure, deduplication, and background processing. NOT for real-time data ingestion or streaming pipelines.