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
- sunholo-data/ailangtheir own version, first seen Nov 6, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- sunholo-data/ailang★ 38same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository31 other files it gives its agents.
- .github/AGENTS.mdAGENTS.md
- tui/AGENTS.mdAGENTS.md
- actionability-passSkill
- agent-memory-opsSkill
- blocked-page-retrievalSkill
- brain-consistency-auditorSkill
- brainstorm-panelSkill
- concurrent-push-integrationSkill
- curation-auditSkill
- decomposed-local-researchSkill
- dual-model-reconciliationSkill
- eval-integritySkill
- file-organizationSkill
- fleet-model-routingSkill
- fleet-operational-runbookSkill
- generate-review-fix-loopSkill
- guard-target-correctnessSkill
- honesty-stop-gateSkill
- large-file-cartographerSkill
- local-lane-build-loopSkill
- model-routing-tableSkill
- multi-agent-code-workflowSkill
- peer-drop-watcherSkill
- protected-function-guardSkill
- research-dispatchSkill
- research-verificationSkill
- research-workflowSkill
- shared-brain-bookkeepingSkill
- should-weSkill
- task-dependency-sequencingSkill
- verify-running-buildSkill
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
- Distinctive: line 9
Use this at the start of work in a local-first fleet.
- Distinctive: line 12
Prefer a local model when it can meet the required capability and reliability bar.
- Distinctive: line 13
Keep durable rules and decisions separate from session observations and incidental traces.
- Distinctive: line 15
Verify operational actions at their destination.
- Distinctive: line 20
Document locally: inference endpoint(s), available worker roles, durable-memory location, episodic-memory location, project roots, handoff …
≈ “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.
Files
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
- Prefer a local model when it can meet the required capability and reliability bar; escalate deliberately when it cannot.
- Keep durable rules and decisions separate from session observations and incidental traces.
- Honor project-level protected-code, approval, source-ownership, and secret-handling rules.
- Verify operational actions at their destination. Evidence is stronger than a status assertion.
- Name workers by their actual model and role in reports so results remain attributable.
- 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.
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
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.

