Commonly is a self-hosted collaborative workspace where people work with multiple AI agents, each retaining its own name, memory, skills, and workstation. Teams use it to assign and refine real work across different agent runtimes, and the catalogue entries supply agents, skills, and instructions for use in those rooms.
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 Team-Commonly/commonly --skill markdown-convertergit clone --depth 1 https://github.com/Team-Commonly/commonlyWrote 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/team-commonly/commonly/markdown-converter)<a href="https://agentmods.dev/skills/team-commonly/commonly/markdown-converter"><img src="https://agentmods.dev/badge/skills/team-commonly/commonly/markdown-converter/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/team-commonly/commonly/markdown-converter"><img src="https://agentmods.dev/badge/skills/team-commonly/commonly/markdown-converter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.00565 |
| Opus 5.5 | $0.00023 | $0.00226 |
| Sonnet 5.5 | $0.00011 | $0.00113 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
markdown-converter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
markdown-converter — binary doc → markdown for agent input
markitdown is installed via pip3 install markitdown and is on PATH. It
extracts text content from a wide range of binary formats and emits clean
markdown that's efficient for LLM context.
Supported input formats
PDF, DOCX, XLSX, PPTX, HTML, EPUB, images (with OCR), CSV, JSON, audio transcripts, ZIP archives.
Basic usage
# Convert a single file to markdown on stdout
markitdown /workspace/$(basename "$PWD")/input.pdf
# Save to a markdown file
markitdown /workspace/$(basename "$PWD")/input.docx > /workspace/$(basename "$PWD")/input.md
# Convert and pipe directly into another tool
markitdown /workspace/$(basename "$PWD")/spec.xlsx | head -200
Reading a user-attached file
Files attached by users are downloaded to the agent workspace by the gateway. Once you have a workspace path:
# 1. Convert the binary to markdown
markitdown /workspace/$(basename "$PWD")/uploads/report.pdf > /tmp/report.md
# 2. Read the markdown into your context
cat /tmp/report.md
Then summarize, answer questions about it, or feed sections back to the user.
Useful flags
| Flag | Effect |
|---|---|
--use-docintel <ENDPOINT> |
Use Azure Doc Intelligence (requires API key) for OCR-heavy PDFs |
-o <file> |
Write to file instead of stdout |
When NOT to use markdown-converter
- For producing documents (md → DOCX/PDF/XLSX) → use
officecliorpandic-officeinstead. - For PDF manipulation (extract specific pages, merge files) → use the
pdfskill.
Troubleshooting
- OCR-heavy scanned PDF returns garbage — markitdown uses basic text extraction. If the PDF is image-only, the result will be empty or unhelpful. Mention this limitation to the user.
- XLSX with charts — markitdown extracts cell data but ignores embedded
charts. For chart inspection, render the workbook to HTML first via
officecli view <file> html.
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 · 67 lines · 57 tokens per session scan A 8b080e2ca0a3
markdown-converter is a skill published in the GitHub repository Team-Commonly/commonly (1,363 stars, last pushed today), licensed Apache-2.0. It adds 57 tokens to every session and 565 once invoked, about $0.0002 per session on Opus 5.5. 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
convert-documents-to-markdown
Convert Word (.doc, .docx), PowerPoint (.ppt, .pptx), Excel (.xls, .xlsx), OpenDocument (.odt, .ods, .odp), RTF, EPUB, CSV, and PDF files to GitHub-Flavored Markdown. Use when a task needs the contents of an office document, spreadsheet, presentation, ebook, or PDF you cannot read directly.
doc-reader
Read any common document/data file — PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx), images (OCR), CSV/TSV, plain text, JSON/YAML/TOML, HTML/XML, and most source-code files. Use the readdocument tool.
smart-data-collection
A workflow for extracting structured information from images and documents such as PDFs, Word files, and spreadsheets, then storing it in a database.
meta-multi-format-export-pack
From one piece of source content, render four deliverables: .docx report, .pptx slides, .xlsx data, and an HTML/PDF public version.
translate-health-data
Turn raw health data into standardized data, and read a lab report without guessing. A lab report PDF, photo, spreadsheet or CSV becomes readings with LOINC codes and UCUM units; an Apple Health export becomes JSON lines; a symptom or diagnosis in any language becomes an ICPC-3 code; any reading becomes a FHIR…
superlinked-docs
Offload document, image, and structured-output work to the Superlinked inference cluster: convert PDF/DOCX/PPTX/XLSX/HTML/scans to clean markdown, describe an image (caption + tags), or produce schema/grammar-constrained JSON off the cluster — instead of ingesting the file directly, which can reduce the tokens billed…