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 amazon-qbusiness-anonymousgit 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/amazon-qbusiness-anonymous)<a href="https://agentmods.dev/skills/friz-zy/ai-capability-registry/amazon-qbusiness-anonymous"><img src="https://agentmods.dev/badge/skills/friz-zy/ai-capability-registry/amazon-qbusiness-anonymous/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/amazon-qbusiness-anonymous"><img src="https://agentmods.dev/badge/skills/friz-zy/ai-capability-registry/amazon-qbusiness-anonymous.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 | $0.00018 | $0.00434 |
| Opus 5 | $0.00009 | $0.00217 |
| Sonnet 5 | $0.00004 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
amazon-qbusiness-anonymous-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 8d 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
Amazon Q Business
AI assistant for ingested content with anonymous access.
When to use
- Use Amazon Q Business only when the task directly involves the relevant service, SaaS product, platform, or technology.
Connection
Docker stdio
{
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-e",
"AWS_SECRET_ACCESS_KEY",
"-e",
"AWS_SESSION_TOKEN",
"mcp/amazon-qbusiness-anonymous-mcp-server"
]
}
MCP instructions
- Use the MCP tools only for the user-requested service workflow and prefer read-only operations by default.
- Confirm the target account, workspace, project, repository, or dataset before actions that can read private data or mutate remote state.
- This server requires authorization or environment-provided credentials; ask the user before connecting or requesting access.
Docker launch notes
- Launch through Docker stdio with
docker run --rm -i -e AWS_SECRET_ACCESS_KEY -e AWS_SESSION_TOKEN mcp/amazon-qbusiness-anonymous-mcp-server. - Confirm required environment variables before launch: AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN.
References
- https://github.com/awslabs/mcp
- https://github.com/docker/mcp-registry/tree/main/servers/amazon-qbusiness-anonymous
Security policy
- Trust:
reviewed - Default mode:
manual_review - Permission default:
manual_review - Authentication:
Unspecified in source metadata - Warning: Default mode is
manual_review. - Warning: Permission default is
manual_review. - Required posture: Complete manual review before connecting or invoking tools; this generated record does not grant approval.
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.
- 8d ago First seen · 68 lines · 18 tokens per session scan A 4e289a4b7ad9
amazon-qbusiness-anonymous-mcp is a skill published in the GitHub repository Friz-zy/ai-capability-registry (9 stars, last pushed 3d ago), licensed MIT. It adds 18 tokens to every session and 434 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
voice-chatterbox-tts
Use when free local TTS with voice cloning using Chatterbox. Zero API costs, word-level timing, whisper integration. Clone any voice with 10-60s reference audio. Use when generating narration, voiceovers, or custom AI voices.
voice-ai
Use when voice AI — text-to-speech (ElevenLabs, OpenAI TTS), speech-to-text (Whisper), voice cloning, real-time voice agents. Use when working with voice ai.
prowler-mcp
Creates MCP tools for Prowler MCP Server. Covers BaseTool pattern, model design, and API client usage. Trigger: When working in mcpserver/ on tools (BaseTool), models (MinimalSerializerMixin/fromapiresponse), or API client patterns.
LQF_Machine_Learning_Expert_Guide
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting…
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.