How do I install the Local First LLM skill?

Run npx skillmds@latest add johnalbertini14-glitch/local-first-llm in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Local First LLM safe to use?

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Which AI agents work with Local First LLM?

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Is Local First LLM free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Local First LLM?

johnalbertini14-glitch (@johnalbertini14-glitch) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Local First LLM by johnalbertini14-glitch

Name: Local First LLM
Rating: 4.2 (5 reviews)
Author: johnalbertini14-glitch

Local First LLM

Routes LLM requests to a local model (Ollama, LM Studio, llamafile) before falling back to cloud APIs. Tracks token savings and cost avoidance in a persistent dashboard. Use when: (1) user asks to run a task with a local model first, (2) user wants to reduce cloud API costs or keep requests private, (3) user asks to see their token savings or LLM routing dashboard, (4) any request where local-vs-cloud routing should be decided automatically. Supports Ollama, LM Studio, and llamafile as local providers.

File contents

Local-First LLM

Route requests to a local LLM first; fall back to cloud only when necessary. Track every decision to show real token and cost savings.

Quick Start

1. Check if a local LLM is running

python3 skills/local-first-llm/scripts/check_local.py

Returns JSON: { "any_available": true, "best": { "provider": "ollama", "models": [...] } }

2. Route a request

python3 skills/local-first-llm/scripts/route_request.py \
  --prompt "Summarize this meeting transcript" \
  --tokens 800 \
  --local-available \
  --local-provider ollama

Returns: { "decision": "local", "reason": "...", "complexity_score": -1 }

3. Log the outcome

After executing the request, record it:

python3 skills/local-first-llm/scripts/track_savings.py log \
  --tokens 800 \
  --model gpt-4o \
  --routed-to local

4. Show the dashboard

python3 skills/local-first-llm/scripts/dashboard.py

Full Routing Workflow

┌─────────────────────────────────────────────────────┐
│  1. check_local.py  →  is a local provider running? │
│                                                      │
│  2. route_request.py  →  local or cloud?             │
│     - sensitivity check  (private data → local)      │
│     - complexity score   (high score → cloud)        │
│     - availability gate  (no local → cloud)          │
│                                                      │
│  3. Execute with the chosen provider                 │
│                                                      │
│  4. track_savings.py log  →  record the outcome      │
│                                                      │
│  5. dashboard.py  →  show cumulative savings         │
└─────────────────────────────────────────────────────┘

Routing Rules (Summary)

Condition	Route
No local provider available	☁️ Cloud
Prompt contains sensitive data (`password`, `secret`, `api key`, `ssn`, etc.)	🏠 Local
Complexity score ≥ 3	☁️ Cloud
Complexity score < 3	🏠 Local

For full scoring details, see references/routing-logic.md.

Executing with a Local Provider

Once route_request.py returns "decision": "local", send the request:

Ollama

curl http://localhost:11434/api/generate \
  -d '{"model": "llama3.2", "prompt": "YOUR_PROMPT", "stream": false}'

LM Studio / llamafile (OpenAI-compatible)

curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "local-model", "messages": [{"role": "user", "content": "YOUR_PROMPT"}]}'

Dashboard

The dashboard reads from ~/.openclaw/local-first-llm/savings.json (auto-created).

┌─────────────────────────────────────────┐
│    🧠  Local-First LLM — Dashboard      │
├─────────────────────────────────────────┤
│  Local LLM:  ✅  ollama (llama3.2...)   │
├─────────────────────────────────────────┤
│  Total requests:         42             │
│  Routed locally:         31  (73.8%)    │
│  Routed to cloud:        11             │
├─────────────────────────────────────────┤
│  Tokens saved:       84,200             │
│  Cost saved:           $0.4210          │
└─────────────────────────────────────────┘

Reset savings data:

python3 skills/local-first-llm/scripts/track_savings.py reset

Additional References

Routing scoring details: references/routing-logic.md
Local provider setup (Ollama, LM Studio, llamafile): references/local-providers.md
Token estimation & cloud cost table: references/token-estimation.md

---name: local-first-llmdescription: Routes LLM requests to a local model (Ollama, LM Studio, llamafile) before falling back to cloud APIs. Tracks token savings and cost avoidance in a persistent dashboard. Use when: (1) user asks to run a task with a local model first, (2) user wants to reduce cloud API costs or keep requests private, (3) user asks to see their token savings or LLM routing dashboard, (4) any request where local-vs-cloud routing should be decided automatically. Supports Ollama, LM Studio, and llamafile as local providers.--- # Local-First LLM Route requests to a local LLM first; fall back to cloud only when necessary. Track every decision to show real token and cost savings. ## Quick Start ### 1. Check if a local LLM is running ```bash python3 skills/local-first-llm/scripts/check_local.py ``` Returns JSON: `{ "any_available": true, "best": { "provider": "ollama", "models": [...] } }` ### 2. Route a request ```bash python3 skills/local-first-llm/scripts/route_request.py \ --prompt "Summarize this meeting transcript" \ --tokens 800 \ --local-available \ --local-provider ollama ``` Returns: `{ "decision": "local", "reason": "...", "complexity_score": -1 }` ### 3. Log the outcome After executing the request, record it: ```bash python3 skills/local-first-llm/scripts/track_savings.py log \ --tokens 800 \ --model gpt-4o \ --routed-to local ``` ### 4. Show the dashboard ```bash python3 skills/local-first-llm/scripts/dashboard.py ``` --- ## Full Routing Workflow ``` ┌─────────────────────────────────────────────────────┐ │ 1. check_local.py → is a local provider running? │ │ │ │ 2. route_request.py → local or cloud? │ │ - sensitivity check (private data → local) │ │ - complexity score (high score → cloud) │ │ - availability gate (no local → cloud) │ │ │ │ 3. Execute with the chosen provider │ │ │ │ 4. track_savings.py log → record the outcome │ │ │ │ 5. dashboard.py → show cumulative savings │ └─────────────────────────────────────────────────────┘ ``` --- ## Routing Rules (Summary) | Condition | Route | | ----------------------------------------------------------------------------- | -------- | | No local provider available | ☁️ Cloud | | Prompt contains sensitive data (`password`, `secret`, `api key`, `ssn`, etc.) | 🏠 Local | | Complexity score ≥ 3 | ☁️ Cloud | | Complexity score < 3 | 🏠 Local | For full scoring details, see [references/routing-logic.md](references/routing-logic.md). --- ## Executing with a Local Provider Once `route_request.py` returns `"decision": "local"`, send the request: ### Ollama ```bash curl http://localhost:11434/api/generate \ -d '{"model": "llama3.2", "prompt": "YOUR_PROMPT", "stream": false}' ``` ### LM Studio / llamafile (OpenAI-compatible) ```bash curl http://localhost:1234/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "local-model", "messages": [{"role": "user", "content": "YOUR_PROMPT"}]}' ``` --- ## Dashboard The dashboard reads from `~/.openclaw/local-first-llm/savings.json` (auto-created). ``` ┌─────────────────────────────────────────┐ │ 🧠 Local-First LLM — Dashboard │ ├─────────────────────────────────────────┤ │ Local LLM: ✅ ollama (llama3.2...) │ ├─────────────────────────────────────────┤ │ Total requests: 42 │ │ Routed locally: 31 (73.8%) │ │ Routed to cloud: 11 │ ├─────────────────────────────────────────┤ │ Tokens saved: 84,200 │ │ Cost saved: $0.4210 │ └─────────────────────────────────────────┘ ``` Reset savings data: ```bash python3 skills/local-first-llm/scripts/track_savings.py reset ``` --- ## Additional References - **Routing scoring details**: [references/routing-logic.md](references/routing-logic.md) - **Local provider setup** (Ollama, LM Studio, llamafile): [references/local-providers.md](references/local-providers.md) - **Token estimation & cloud cost table**: [references/token-estimation.md](references/token-estimation.md)

Frequently asked questions