agentleFS
BerriAIBerriAI/litellm-skills/add-agent/SKILL.md

add-agent

Skill84 starsChanged 7 months agoMITView on GitHub

Create a new AI agent on a live LiteLLM proxy. Asks for agent name, the underlying model, and optional MCP server access, then calls POST /v1/agents.

  • Reads credentials
  • Sends data out

9 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use setup.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • VercelSkill

    setup

    Set up Vercel CLI and project configuration. Use when the user says "set up Vercel", "configure Vercel", "link to Vercel", or "vercel init".

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 7 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Create a new AI agent on a live LiteLLM proxy.

    line 16
  2. Distinctive:

    Ask for these if not already known:

    line 20

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “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

Add Agent

Create a new AI agent on a live LiteLLM proxy.

Setup

Ask for these if not already known:

LITELLM_BASE_URL  — e.g. https://my-proxy.example.com
LITELLM_API_KEY   — proxy admin key

API reference: https://docs.litellm.ai/docs/proxy/agents

Ask the user

  1. Agent name (required, e.g. my-coding-agent)
  2. Model — which LiteLLM model should this agent use (e.g. gpt-4o, claude-3-5-sonnet)
  3. Description (optional, shown to callers)
  4. MCP servers (optional) — list of server_ids this agent can use

Run

curl -s -X POST "$BASE/v1/agents" \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agent_name": "<name>",
    "litellm_params": {
      "model": "<model>"
    },
    "agent_card_params": {
      "name": "<name>",
      "description": "<description>",
      "version": "1.0"
    }
  }'

List existing agents

curl -s "$BASE/v1/agents" \
  -H "Authorization: Bearer $KEY"

Get agent info

curl -s "$BASE/v1/agents/<agent_id>" \
  -H "Authorization: Bearer $KEY"

Delete an agent

curl -s -X DELETE "$BASE/v1/agents/<agent_id>" \
  -H "Authorization: Bearer $KEY"

Output

Show agent_id — needed to call or delete this agent later.

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.