Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/fvanlookeren-bit/llm-intern/internnpx skills add fvanlookeren-bit/llm-Intern --skill interngit clone --depth 1 https://github.com/fvanlookeren-bit/llm-InternWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fvanlookeren-bit/llm-intern/intern)<a href="https://agentmods.dev/skills/fvanlookeren-bit/llm-intern/intern"><img src="https://agentmods.dev/badge/skills/fvanlookeren-bit/llm-intern/intern.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00149 | $0.01588 |
| Opus 5 | $0.00075 | $0.00794 |
| Sonnet 5 | $0.00030 | $0.00318 |
| Haiku 4.5 | $0.00015 | $0.00159 |
Grade A, and why
intern scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intern (LM Studio local)
Delegar la tarea al modelo local de LM Studio vía el servidor MCP lm-studio, en vez
de hacerla directo. LM Studio corre 100% local y no consume cuota del modelo grande
("ilimitado y gratis"), pero rinde peor en razonamiento complejo — este skill existe
para decidir bien CUÁNDO delegar y CON QUÉ modelo, no solo cómo llamar la tool.
"El intern" no es sinónimo de subagentes (el tool Agent/Explore/
general-purpose, etc.): esos corren en el modelo grande y consumen su cuota igual
que hacerlo directo. El intern es el MCP lm-studio — un modelo distinto, gratis,
local.
1. Dos tiers, y las tools de cada uno
lm_studio_list_models muestra el tier= de cada modelo:
subagent(junior) — contexto largo y tool-calling confiable. Se le delega con autonomía: que lea los archivos él mismo, en vez de masticarle el contexto.intern— el resto. Delegación mecánica, todo el contexto en el prompt.
El roster se configura con LM_STUDIO_SUBAGENT_MODELS="id-a,id-b". Es curado a
propósito: el host reporta tool_use para todos los modelos no-embedding, hasta
los de 1-2B, así que esa capability no sirve para decidir a quién confiarle un loop.
Tools:
lm_studio_generate— solo texto/código, POST directo al modelo, sin tools. Tier intern. Dale todo el contexto necesario en el prompt (datos, texto fuente, resultados de tus propias tool calls) — no puede ir a buscar nada él mismo.lm_studio_agent— con tools MCP reales. Tier subagent. Le pasásmcp_servers(nombres de~/.lmstudio/mcp.json, listalos conlm_studio_list_mcp_serversantes) y corre un loop de agente real: llama tools, lee resultados, repite hasta terminar (topemax_iterations, default 8). Usá el mínimo de MCPs necesario — cada uno agrega tools al contexto del modelo. Verificá cada dato contra eltool_traceque devuelve, no contra su prosa.lm_studio_capacity— techo de memoria, modelos residentes y margen libre. Llamala antes de cargar un segundo modelo.lm_studio_list_models— qué modelos hay, cuál está cargado y en qué tier.lm_studio_list_mcp_servers— qué MCPs puede usarlm_studio_agent.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 100 lines · 149 tokens per session scan A b9884747784d
intern is a skill published in the GitHub repository fvanlookeren-bit/llm-Intern (5 stars, last pushed 19d ago), licensed MIT. It adds 149 tokens to every session and 1,588 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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