| name | local-llm |
| description | Use when the user explicitly asks about local LLMs, LM Studio, local model inventory, local model chat, local model benchmarking, model role curation, or configuring private/local model endpoints. Provides public-safe mechanics and model index guidance for locally hosted or trusted-network OpenAI-compatible models without deciding when other domain skills should use a local model. |
| metadata | {"short-description":"Use local LLM endpoints"} |
| resources | [{"path":"references/model-index.yaml","kind":"reference","description":"Public curated model roles, strengths, weaknesses, and tested notes."},{"path":"references/local-llm.local.example.yaml","kind":"reference","description":"Public-safe template for private `.local/local-llm.yaml` endpoint and role overrides."},{"path":"scripts/lm_studio_inventory.py","kind":"script","description":"List API-visible models and LM Studio native runtime state when available."},{"path":"scripts/lm_studio_api.py","kind":"script","description":"Shared endpoint, model-role, lifecycle, and redaction helpers for local LLM scripts."},{"path":"scripts/lm_studio_chat.py","kind":"script","description":"Send a bounded one-shot chat prompt with optional LM Studio API lifecycle management."},{"path":"scripts/lm_studio_benchmark.py","kind":"script","description":"Benchmark configured or explicit local models with short cold/fast response probes."},{"path":"scripts/local_code_agent.py","kind":"script","description":"Run Every Code `code exec` through an isolated LM Studio-backed CODE_HOME."},{"path":"scripts/validate_lm_studio_api.py","kind":"script","description":"Offline validation for LM Studio API URL, redaction, and unload guard semantics."}] |
| commands | [{"name":"local-llm-inventory","source":"skill","resource_path":"scripts/lm_studio_inventory.py","example_argv":["uv","run","local-llm/scripts/lm_studio_inventory.py","--json"],"purpose":"Lists configured endpoint trust/locality and available model IDs."},{"name":"local-llm-chat","source":"skill","resource_path":"scripts/lm_studio_chat.py","example_argv":["uv","run","local-llm/scripts/lm_studio_chat.py","--role","rollout_scout","--load-policy","jit_chat","--ttl","300","--warmup","--prompt","Reply with OK","--json"],"purpose":"Sends one bounded prompt through a role or explicit model."},{"name":"local-llm-benchmark","source":"skill","resource_path":"scripts/lm_studio_benchmark.py","example_argv":["uv","run","local-llm/scripts/lm_studio_benchmark.py","--role","rollout_scout","--json"],"purpose":"Benchmarks one or more role/model candidates before changing model defaults."},{"name":"local-code-agent","source":"skill","resource_path":"scripts/local_code_agent.py","example_argv":["uv","run","local-llm/scripts/local_code_agent.py","--role","rollout_scout","--model","openai/gpt-oss-20b","--json","Reply with exactly: OK"],"purpose":"Runs Every Code itself through a local LM Studio chat provider for agent-route smoke tests and comparisons."}] |
| workflow_defaults | [{"name":"default_endpoint","value":"http://127.0.0.1:1234/v1","description":"LM Studio default endpoint when `.local/local-llm.yaml` is absent."},{"name":"private_config","value":".local/local-llm.yaml","description":"Gitignored local endpoint, trust, and model override configuration."}] |
Local LLM
This skill documents how to inventory, call, benchmark, and curate local or trusted-network LLMs. It does not decide when every other skill should use a local model; domain skills keep their own evidence and prompting rules.
Configuration
Public model curation lives in references/model-index.yaml. Machine-specific endpoint choices, trusted LAN settings, installed model IDs, and user overrides live in .local/local-llm.yaml.
Use repo-root .local/ for private config. Keeping private files out of skill folders preserves skill portability while still giving each skill a clear private filename.
Copy the shape from references/local-llm.local.example.yaml when creating private config. Do not commit real hostnames, LAN addresses, tokens, installed-model inventories tied to a private machine, or private benchmark notes.
When endpoint URLs, ports, trust scope, or preferred model IDs change, update the
private config before data-heavy runs and run a lightweight inventory check. Bump
the config version only for schema changes, not routine endpoint or model
rotation.
Locality
localhost: same machine as the agent, typically http://127.0.0.1:1234/v1.trusted_lan: a private LAN or trusted mesh/VPN endpoint. Treat as acceptable for private work when local config says the network is trusted.remote_private: a private endpoint reached over an authenticated tunnel or managed private network.cloud: an internet-hosted provider, even when the API is OpenAI-compatible.
The endpoint determines data handling. A model name alone does not prove locality.
Sensitive Material
Localhost and trusted-LAN models are preferred over cloud APIs for private or
sensitive local material when the runtime is trusted. The restriction is on
durable artifacts, not on local inference itself. When local config marks an
endpoint as locality: localhost or locality: trusted_lan with private/local
trust, it may receive original private local inputs for the current task,
including unredacted rollout snippets, memory drafts, local config extracts, and
repo-specific context. Do not redact away useful local signal just because the
input would be unsafe for a public issue or cloud model.
Keep these boundaries:
- Do not commit raw private prompts, outputs, traces, memory drafts, hostnames, tokens, or account details.
- Do not paste private model output into public issues, PR comments, docs, or skill examples.
- Check whether LM Studio logging/history is enabled before sending material that should not be retained.
- Strip or withhold obvious secrets such as tokens, passwords, API keys, and
private keys even for trusted local models unless the user explicitly asks for
secret analysis.
- Redact, summarize, or avoid sensitive material for
cloud, unknown, disabled,
or untrusted endpoints unless the user explicitly approves that provider and
context.
Model Index
The public model index can include curated model IDs, model roles, strengths, weaknesses, minimum token budgets, and last-tested notes. It is allowed to be opinionated, but it should stay public-safe and time-stamped.
Private config may add, remove, or override role choices. Prefer roles over hard-coded model names in task-specific scripts when the role has already been curated locally.
Benchmarking
Do not benchmark routinely. Benchmark when adding a model, choosing a model for a new role, investigating odd behavior, after model/runtime updates, or when latency/cold-load behavior matters.
Benchmark output is local machine evidence, not public truth. Keep private endpoint details and raw sensitive prompts out of committed results.
LM Studio Mechanics
LM Studio exposes local models through OpenAI-compatible endpoints such as
/v1/models and /v1/chat/completions, plus native lifecycle endpoints under
/api/v1. Use the bundled scripts as the normal path so endpoint selection,
trust/locality, tokens, TTL, context, warm-up, and redacted reporting stay tied
to .local/local-llm.yaml.
Inventory is catalog and runtime evidence, not a universal loaded-state proof.
lm_studio_inventory.py lists /v1/models API-visible models and, for
provider: lm_studio, attempts the native runtime model endpoint. Treat loaded
instance counts as authoritative only when the native runtime response provides
them; otherwise use a harmless warm-up chat and the response served_model
field before sending private prompts.
For ordinary local runs, prefer API JIT loading through
lm_studio_chat.py --load-policy jit_chat --ttl <seconds> --warmup. The warm-up
request uses non-sensitive text first, can absorb cold-load latency, and records
the served model before private content is sent. TTL on /v1/chat/completions
is an LM Studio JIT hint; already-loaded manual or explicit instances may keep
their load-time TTL instead of honoring per-request TTL.
For deterministic long-context or load-parameter-sensitive runs, use
--load-policy api_explicit with role or CLI context_length,
flash_attention, and --unload-after when cleanup should be immediate. This
uses /api/v1/models/load before chat and unloads only by the returned
instance_id; do not attempt model-name-only unloads. On the tested LM Studio
build, native load accepts context and flash-attention options but rejects TTL,
so TTL remains a JIT-chat hint rather than an explicit-load cleanup mechanism.
The lms CLI is an operator fallback for local diagnostics, downloads, or
manual recovery when the HTTP lifecycle path is unavailable. For non-local LM
Studio instances, CLI commands must target the serving host explicitly, so API
helpers are preferred for trusted LAN and remote-private endpoints.
Large reasoning models may return no assistant content when max_tokens is too low because the budget is consumed by reasoning. Increase max_tokens for deep models before declaring them unusable.
If a configured local endpoint is unavailable, say so plainly and keep private
inputs local. Do not silently fall back to cloud. For private workflows, either
retry after the local endpoint is healthy, switch to another configured trusted
local/private endpoint, or ask for explicit approval for the exact cloud
destination and input scope.
Local Code Agent
Direct local LLM calls and local Code-agent calls are different products:
- Use
lm_studio_chat.py when a domain skill needs prompt-pure local inference
with its exact system prompt and no Every Code/Code agent behavior layered in.
- Use
local_code_agent.py when you intentionally want the Every Code agent
harness itself to run through a local LM Studio chat model for smoke tests,
comparison runs, or command-backed local agent registration.
local_code_agent.py builds an isolated temporary CODE_HOME/config.toml from
.local/local-llm.yaml, forces the LM Studio provider to wire_api = "chat",
and then invokes code exec. This avoids depending on the operator's normal
Every Code config and avoids stale local settings such as wire_api = "responses" for LM Studio chat endpoints.
Example smoke test:
uv run local-llm/scripts/local_code_agent.py \
--role rollout_scout \
--model openai/gpt-oss-20b \
--json \
--output-last-message .local/local-code-agent-smoke.txt \
'Reply with exactly: LOCAL_AGENT_OK'
Command-backed Every Code agent example for private ~/.code/config.local.toml:
[[agents]]
name = "local-code"
command = "uv run /path/to/codex-skills/local-llm/scripts/local_code_agent.py --role rollout_scout --model openai/gpt-oss-20b --json"
enabled = true
description = "Every Code agent running through local LM Studio"
Test candidate models before relying on them as local agents. Some local models
work well for direct chat but return no final agent_message through the Code
harness because of reasoning/output-format or model-metadata compatibility. In
local smoke testing, openai/gpt-oss-20b returned a final agent message, while a
larger Qwen model reached the provider but returned no final message.
Related Skills
Other skills may reference this one for local model mechanics and model selection. They should keep their own domain-specific evidence rules and remain usable when local-llm is absent.
