Tencent SkillHub · Developer Tools

Offload Tasks to LM Studio Models

Reduces token usage from paid providers by offloading work to local LM Studio models. Use when: (1) Cutting costs—use local models for summarization, extraction, classification, rewriting, first-pass review, brainstorming when quality suffices, (2) Avoiding paid API calls for high-volume or repetitive tasks, (3) No extra model configuration—JIT loading and REST API work with existing LM Studio setup, (4) Local-only or privacy-sensitive work. Requires LM Studio 0.4+ with server (default :1234). No CLI required.

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Download the package from Yavira.
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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: README.md, scripts/test.mjs, scripts/unload.mjs, scripts/load.mjs, scripts/lmstudio-api.mjs, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.3

Provenance

Publisher: t-sinclair2500
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 17 sections Open source page

LM Studio Models

Offload tasks to local models when quality suffices. Base URL: http://127.0.0.1:1234. Auth: Authorization: Bearer lmstudio. instance_id = loaded_instances[].id (same model can have multiple, e.g. key and key:2).

Key Terms

model: From GET models key; use in chat and optional load. lm_studio_api_url: Default http://127.0.0.1:1234 (paths /api/v1/...). response_id / previous_response_id: Chat returns response_id; pass as previous_response_id for stateful. instance_id: For unload, use only the value from GET /api/v1/models for that model: each loaded_instances[].id. Do not assume it equals the model key; with multiple instances ids can be like key:2. LM Studio docs: List (loaded_instances[].id), Unload (instance_id). Trigger in frontmatter; below = implementation.

Prerequisites

LM Studio 0.4+, server :1234, models on disk; load/unload via API (JIT optional); Node for script (curl ok).

Quick start

Minimal path: list models, then one chat. Replace <model> with a key from GET /api/v1/models and <task> with the task text. curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models node scripts/lmstudio-api.mjs <model> '<task>' --temperature=0.5 --max-output-tokens=200 Stateful multi-turn: pass --previous-response-id=<id> from the prior script output. Or use --stateful to persist response_id automatically. Optional --log <path> for request/response. node scripts/lmstudio-api.mjs <model> 'First turn...' --previous-response-id=$ID1 node scripts/lmstudio-api.mjs <model> 'Second turn...' --previous-response-id=$ID2

Step 0: Preflight

GET <base>/api/v1/models; non-200 or connection error = server not ready. exec command:"curl -s -o /dev/null -w '%{http_code}' -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models"

Step 1: List Models and Select

GET /api/v1/models to list models. Parse each entry: key, type, loaded_instances, max_context_length, capabilities. If a model already has loaded_instances.length > 0 and fits the task, skip to Step 5; otherwise pick a key for chat (and optional load in Step 3). Choose by task: vision -> capabilities.vision; embedding -> type=embedding; context -> max_context_length. Prefer already-loaded; prefer smaller for speed, larger for reasoning. Note loaded_instances[].id for optional unload later. Example — list models: exec command:"curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models" Parse models[] (key, type, loaded_instances, max_context_length, capabilities, params_string). If a model has loaded_instances.length > 0 and fits task, skip to Step 5; else pick key for chat (and optional load). Note loaded_instances[].id for optional unload.

Step 2: Model Selection

Pick key from GET response; use as model in chat (optional load). Constraints: vision -> capabilities.vision; embedding -> type=embedding; context -> max_context_length. Prefer loaded (loaded_instances non-empty), smaller for speed/larger for reasoning; fallback primary. If unsure, use the first loaded instance for that key or the smallest loaded model that fits the task. Optional POST load; else JIT on first chat.

Step 3: Load Model (optional)

Optional: POST /api/v1/models/load { model, context_length?, ... }. Or run scripts/load.mjs <model>. JIT: first chat loads; explicit load only for specific options.

Step 4: Verify Loaded (optional)

If explicit load: GET models, confirm loaded_instances. If JIT: no verify; first chat returns model_instance_id, stats.model_load_time_seconds.

Step 5: Call API

From the skill folder: node scripts/lmstudio-api.mjs <model> '<task>' [options]. exec command:"node scripts/lmstudio-api.mjs <model> '<task>' --temperature=0.7 --max-output-tokens=2000" Stateful: add --previous-response-id=<response_id>. Curl: POST <base>/api/v1/chat, body model, input, store, temperature, max_output_tokens; optional previous_response_id. Parse: output (type message) -> content; response_id, model_instance_id, stats. Script outputs content, model_instance_id, response_id, usage.

Step 6: Unload (optional)

For the model key you used: GET /api/v1/models, then for each loaded_instances[].id for that model, POST /api/v1/models/unload with body {"instance_id": "<that id>"}. Use the id from the response only (do not send the model key unless it exactly equals that id). Or run scripts/unload.mjs <model_key> (script does GET then unloads each instance id). Optional --unload-after (default off); use --keep to leave loaded. Unload only that model's instances. JIT+TTL auto-unload; explicit when needed. # One unload per instance_id; repeat for each id in that model's loaded_instances exec command:"curl -s -X POST http://127.0.0.1:1234/api/v1/models/unload -H 'Content-Type: application/json' -H 'Authorization: Bearer lmstudio' -d '{\"instance_id\": \"<instance_id>\"}'"

Step 7: Verify unload (optional)

After unloading, confirm no instances remain for that model key. Run the jq check below; result must be 0. If non-zero, unload the remaining instance_id(s) from that model and re-run the check. Do not infer from "model object exists"; the object still exists with an empty loaded_instances array. exec command:"curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models | jq '.models[]|select(.key==\"<model_key>\")|.loaded_instances|length'" Expect output 0. If not, unload remaining instance_ids and re-run.

Error Handling

Script retries on transient failure (2-3 attempts with backoff). Model not found -> pick another model from GET response. API/server errors -> GET models, check URL. Invalid output -> retry. Memory -> unload or smaller model. Unload fails -> instance_id must be exactly from GET /api/v1/models for that model's loaded_instances[].id (not the model key unless it matches).

Copy-paste

Replace <model> with a key from GET /api/v1/models and <task> with the task text. Optional unload per Step 6 (instance_id from GET models for that key). exec command:"curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models" exec command:"node scripts/lmstudio-api.mjs <model> '<task>' --temperature=0.7 --max-output-tokens=2000"

LM Studio API Details

Helper/API: see Step 5. Output: content, model_instance_id, response_id, usage. Auth: Bearer lmstudio. List GET /api/v1/models. Load POST /api/v1/models/load (optional). Unload POST /api/v1/models/unload { instance_id }.

Scripts

lmstudio-api.mjs: chat; options --stateful, --unload-after, --keep, --log <path>, --previous-response-id, --temperature, --max-output-tokens. load.mjs: load model by key. unload.mjs: unload by model key (all instances). test.mjs: smoke test (load, chat, unload one model).

Notes

LM Studio 0.4+. JIT (first chat loads; model_load_time_seconds in stats); stateful (response_id / previous_response_id).

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

4 Scripts2 Docs

SKILL.md Primary doc
README.md Docs
scripts/lmstudio-api.mjs Scripts
scripts/load.mjs Scripts
scripts/test.mjs Scripts
scripts/unload.mjs Scripts