agentleFS
zedarvateszedarvates/frugal-skills/skills/hermes-bridge/SKILL.md

hermes-bridge

Skill1 starsChanged 32 days agoMITView on GitHub

Instructions for the upstream workflow: Expose auto_route/local_chat/fusion/find_skills/infra_tips to Hermes-Agent (or any framework that expects OpenAI-function-calling tool specs instead of MCP) — plus a one-call MCP config generator for the zero-code path if the framework already speaks MCP. Use when connecting the toolkit's routing belt to another agent framework.

2 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 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.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every task [[hermes-second-brain]] would otherwise send straight to its own model gets a chance to resolve locally or via the cheapest …

    line 56

≈ “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

hermes-bridge — connect the belt to another agent framework

Package scope and prerequisites

This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.

the toolkit already runs a standard MCP server (skills.llm_mcp.server, stdio JSON-RPC). If the target framework speaks MCP, that's the whole integration — no code in this module runs:

python -m skills.hermes_bridge.cli config --cwd /path/to/project
# → paste the printed {"mcpServers": {...}} block into the framework's MCP config

This module exists for frameworks — [[hermes-second-brain]] among them — that instead expect a flat list of OpenAI-function-calling tool specs + a dispatcher, a common shape for agents built before/without MCP support.

python -m skills.hermes_bridge.cli schemas          # the 5 tool specs, OpenAI-shaped
python -m skills.hermes_bridge.cli call botte_auto_route --prompt "rename x to y"
from skills.hermes_bridge import TOOL_SCHEMAS, dispatch

# register TOOL_SCHEMAS with the framework's tool-calling mechanism, then:
dispatch("botte_auto_route", {"prompt": "..."})   # → JSON string, same shape as the MCP tool

The 5 tools

Deliberately a subset of the ~35-tool MCP surface — the ones that matter for a second-brain / routing integration:

toolwhat it does
botte_auto_routeplan local-vs-cloud routing; execute=true to actually run it
botte_local_chatrun a prompt on a configured local model; inspect usage and forwarding
botte_fusioncascade / draft_refine / vote — models collaborating
botte_find_skills0-token local search over installed skill catalogs
botte_infra_tipshardware/software setup advice for running local models

Why this matters for a second-brain agent

Every task [[hermes-second-brain]] would otherwise send straight to its own model gets a chance to resolve locally or via the cheapest capable tier first — see [[bench]] for how to measure it with a compatible runtime; no result is established by this catalog. botte_find_skills also gives Hermes 0-token access to the local skill catalog instead of re-deriving capabilities from scratch each session.

Related: [[llm-mcp]] (the native MCP server this bridges from), [[auto-router]], [[bench]] (the proof), docs/integrations/hermes.md (the full integration write-up).

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.