raymon Skill
description: Use Raymon’s MCP server to search and inspect Ray-style logs. Use when you need to (1) set up Raymon as an MCP server (local or remote with auth), (2) search stored entries with filters/pagination, or (3) fetch full entry payloads by UUID for debugging/triage.
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Why use this skill
raymon is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around frontend skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Generic Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in bnomei/raymon, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
---
name: raymon
description: Use Raymon’s MCP server to search and inspect Ray-style logs. Use when you need to (1) set up Raymon as an MCP server (local or remote with auth), (2) search stored entries with filters/pagination, or (3) fetch full entry payloads by UUID for debugging/triage.
---
# Raymon MCP
Use Raymon as a lightweight, searchable log store that an agent can query via MCP (Streamable HTTP).
## Generating events (Ray integrations)
Raymon stores *Ray-style* log entries. Those entries are generated by language-specific Ray integrations: install the integration in your app, then call its `ray(...)` helper (or equivalent). The integration sends an HTTP request to the Ray ingest endpoint (default port `23517`).
If Raymon is running on `127.0.0.1:23517`, most integrations work out of the box. If Raymon is on a different host/port, configure the integration to point at Raymon instead of the Ray desktop app.
Supported integrations (install + first dump):
- **PHP** (`spatie/ray`)
- Install: `composer require spatie/ray`
- Emit: `ray('Hello from PHP');`
- **JavaScript & Node.js** (`permafrost-dev/node-ray`)
- Install: `npm install node-ray` (or `yarn add node-ray`, `bun add node-ray`)
- Emit (Node.js): `import Ray, { ray } from 'node-ray'; await Ray.initSettings(); ray('Hello from JS');`
- **Bash** (CLI wrappers)
- Pick a CLI sender and follow its README (the Ray docs list `node-ray-cli`, `myray-cli`, `ray-cli`, and the curl-based `raybash`).
- Aim for a “send a message” command and ensure it targets Raymon’s host/port.
- **Ruby** (`ruby-ray`)
-
...