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 Friz-zy/ai-capability-registry --skill egnytegit clone --depth 1 https://github.com/Friz-zy/ai-capability-registryWrote 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/friz-zy/ai-capability-registry/egnyte)<a href="https://agentmods.dev/skills/friz-zy/ai-capability-registry/egnyte"><img src="https://agentmods.dev/badge/skills/friz-zy/ai-capability-registry/egnyte/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/friz-zy/ai-capability-registry/egnyte"><img src="https://agentmods.dev/badge/skills/friz-zy/ai-capability-registry/egnyte.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 | $0.00015 | $0.00198 |
| Opus 5 | $0.00008 | $0.00099 |
| Sonnet 5 | $0.00003 | $0.00040 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
egnyte-mcp 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 6d 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
Egnyte
Document Management MCP server by Egnyte.
When to use
- Use Egnyte only when the task directly involves Document Management.
Connection
Hosted endpoint
{
"type": "sse",
"url": "https://mcp-server.egnyte.com/sse"
}
MCP instructions
References
Security policy
- Trust:
trusted - Default mode:
ask_before_write - Permission default:
ask_before_write - Authentication:
OAuth2.1 - Posture: Follow the declared defaults; this generated record does not replace server-specific review.
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.
- 6d ago First seen · 48 lines · 15 tokens per session scan A 818d0ce299c0
egnyte-mcp is a skill published in the GitHub repository Friz-zy/ai-capability-registry (9 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 198 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
pdf-extractor
Extract text, tables, and images from PDFs. Use when: extracting data from reports; converting PDF tables to CSV; pulling images from presentations; processing research papers; batch converting PDFs to text.
nextcloud-ingest
Natively ingest Nextcloud files into the epistemic-graph knowledge graph via the nextcloud-agent MCP server — fetch a file over WebDAV and store its raw bytes as a content-addressed :Blob/:AssetOccurrence plus its extracted text (pdf/office/txt or image OCR) as a linked :Document. Use when the agent must make a…
report-generator
Generate PDF/HTML reports from templates and data. Use when: creating client reports; generating weekly summaries; producing marketing performance reports; automating recurring reports.
n8n-binary-and-data
Handle files and binary data in n8n correctly. Use when working with files, images, PDFs, attachments, uploads or downloads, base64, vision/multimodal input, or when an AI agent needs a file as tool input or output — and whenever the user mentions $binary, binaryPropertyName, "read the PDF", "attach the file", "send…
docx-comment-reply
Reply to comments (批注) in Word .docx/.doc files: extract comment context, draft replies, write threaded replies back, and validate OOXML.
authoring
How to write Stencila Markdown (.smd) and work with executable documents in related flavours (.myst, .qmd). Use when creating or editing .smd files, adding executable code chunks, inline expressions, parameters, figures, tables, math, or document metadata, or when unsure how Stencila Markdown differs from plain…