agentleFS
Sign inStart free
OctagonAIOctagonAI/octagon-mcp-server/skills/octagon-api-smoke-test/SKILL.md

octagon-api-smoke-test

Skill147 starsChanged 6 months agoMITView on GitHub

Validate Octagon plugin configuration and run a lightweight smoke test across the main Octagon MCP workflows. Use when checking whether the plugin is configured correctly, debugging auth, or verifying tool availability.

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to confirm that the Claude plugin and Octagon MCP are configured correctly.

    line 8
  2. Very specific:

    Run a simple octagon-agent request

    line 12
  3. Very specific:

    If a Kalshi URL is available, run octagon-prediction-markets-agent

    line 14
  4. Very specific:

    If an event ticker is available, run prediction_markets_history

    line 15

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Octagon API Smoke Test

Use this skill to confirm that the Claude plugin and Octagon MCP are configured correctly.

Smoke test sequence

  1. Run a simple octagon-agent request
  2. Run a simple octagon-deep-research-agent request
  3. If a Kalshi URL is available, run octagon-prediction-markets-agent
  4. If an event ticker is available, run prediction_markets_history

MCP calls

General agent:

{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Give me a one-sentence summary of Apple's latest quarter."
  }
}

Deep research:

{
  "server": "octagon-mcp",
  "toolName": "octagon-deep-research-agent",
  "arguments": {
    "prompt": "Research the current AI infrastructure spending cycle in one short paragraph."
  }
}

Failure triage

  • Missing auth or invalid key: check plugin api_key
  • Unexpected environment or staging issue: check api_base_url
  • Prediction market failures mentioning Kalshi URL: supply a valid Kalshi market URL
  • Credit or entitlement failures: report the exact error and stop

Success criteria

  • At least one Octagon tool returns usable text
  • Tool failures, if any, are classified clearly as config, entitlement, input, or service issues

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free