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/managedcode/prompterone/dotnet-managedcode-markitdownnpx skills add managedcode/PrompterOne --skill dotnet-managedcode-markitdowngit clone --depth 1 https://github.com/managedcode/PrompterOneWhat 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 | $0.00039 | $0.00356 |
| Opus 5 | $0.00019 | $0.00178 |
| Sonnet 5 | $0.00008 | $0.00071 |
| Haiku 4.5 | $0.00004 | $0.00036 |
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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- dotnet-managedcode-markitdown — 100% identical, 2 lines differ
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 ships with it
1 file 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.
- yesterday First seen · 45 lines · 39 tokens per session scan A c666d4fb1094
dotnet-managedcode-markitdown is a skill published in the GitHub repository managedcode/PrompterOne (42 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 356 once invoked, about $0.0002 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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