agentleFS
sherificansherifican/Agent-FleetOps/skills/local-model-onboarding/SKILL.md

local-model-onboarding

Skill34 starsChanged 37 days agoMITView on GitHub

Orient an agent to a local-first model fleet, its safety boundaries, and its operating disciplines.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    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:

    Use this at the start of work in a local-first fleet.

    line 9
  2. Distinctive:

    Prefer a local model when it can meet the required capability and reliability bar.

    line 12
  3. Distinctive:

    Keep durable rules and decisions separate from session observations and incidental traces.

    line 13
  4. Distinctive:

    Verify operational actions at their destination.

    line 15
  5. Distinctive:

    Document locally: inference endpoint(s), available worker roles, durable-memory location, episodic-memory location, project roots, handoff …

    line 20

≈ “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

Local Model Onboarding

Overview

Use this at the start of work in a local-first fleet. Read the deployment's own documentation for its authoritative model roster, storage locations, access controls, and escalation policy; do not rely on remembered infrastructure details.

Standing disciplines

  1. Prefer a local model when it can meet the required capability and reliability bar; escalate deliberately when it cannot.
  2. Keep durable rules and decisions separate from session observations and incidental traces.
  3. Honor project-level protected-code, approval, source-ownership, and secret-handling rules.
  4. Verify operational actions at their destination. Evidence is stronger than a status assertion.
  5. Name workers by their actual model and role in reports so results remain attributable.
  6. Bound parallel work to available compute and memory capacity.

Operating map

Document locally: inference endpoint(s), available worker roles, durable-memory location, episodic-memory location, project roots, handoff mechanism, and permitted external services. Treat the current project documentation as the source of truth when these conflict with an old note.

Pitfalls

  • Treating a historical transcript as a current rule source.
  • Sending sensitive work to a provider whose policy or capability makes the result unsuitable.
  • Announcing success before inspecting the resulting file, command output, or destination.

Verification checklist

  • The current deployment documentation was read.
  • Local-first and escalation policies are understood.
  • Protected-code and secret-handling rules are known.
  • The planned handoff and verification mechanism are identified.

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.