Moltis is a persistent personal agent server written in Rust that runs on hardware controlled by its user. It provides an AI agent with sandboxed command execution, model-provider connections, memory, voice, scheduling, messaging integrations, browser automation, and MCP tools. Its catalogue add-ons extend the agent’s workflows and available tools.
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 skills add moltis-org/moltis --skill llm-wikigit clone --depth 1 https://github.com/moltis-org/moltisWrote 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/moltis-org/moltis/llm-wiki)<a href="https://agentmods.dev/skills/moltis-org/moltis/llm-wiki"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/llm-wiki/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/moltis-org/moltis/llm-wiki"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/llm-wiki.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00037 | $0.04075 |
| Opus 5 | $0.00018 | $0.02037 |
| Sonnet 5 | $0.00007 | $0.00815 |
| Haiku 4.5 | $0.00004 | $0.00407 |
Grade B, and why
llm-wiki scanned grade B with 1 finding 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 7d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo loginctl enable-linger $USER This is a copy
83% identical to llm-wiki — 91 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Karpathy's LLM Wiki
Build and maintain a persistent, compounding knowledge base as interlinked markdown files. Based on Andrej Karpathy's LLM Wiki pattern.
Unlike traditional RAG (which rediscovers knowledge from scratch per query), the wiki compiles knowledge once and keeps it current. Cross-references are already there. Contradictions have already been flagged. Synthesis reflects everything ingested.
Division of labor: The human curates sources and directs analysis. The agent summarizes, cross-references, files, and maintains consistency.
When This Skill Activates
Use this skill when the user:
- Asks to create, build, or start a wiki or knowledge base
- Asks to ingest, add, or process a source into their wiki
- Asks a question and an existing wiki is present at the configured path
- Asks to lint, audit, or health-check their wiki
- References their wiki, knowledge base, or "notes" in a research context
Wiki Location
Location: Set via WIKI_PATH environment variable (e.g. in your environment).
If unset, defaults to ~/wiki.
WIKI="${WIKI_PATH:-$HOME/wiki}"
The wiki is just a directory of markdown files — open it in Obsidian, VS Code, or any editor. No database, no special tooling required.
Architecture: Three Layers
wiki/
├── SCHEMA.md # Conventions, structure rules, domain config
├── index.md # Sectioned content catalog with one-line summaries
├── log.md # Chronological action log (append-only, rotated yearly)
├── raw/ # Layer 1: Immutable source material
│ ├── articles/ # Web articles, clippings
│ ├── papers/ # PDFs, arxiv papers
│ ├── transcripts/ # Meeting notes, interviews
│ └── assets/ # Images, diagrams referenced by sources
├── entities/ # Layer 2: Entity pages (people, orgs, products, models)
├── concepts/ # Layer 2: Concept/topic pages
├── comparisons/ # Layer 2: Side-by-side analyses
└── queries/ # Layer 2: Filed query results worth keeping
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.
- 7d ago First seen · 449 lines · 37 tokens per session scan B 8bad1aaf8f22
llm-wiki is a skill published in the GitHub repository moltis-org/moltis (2,847 stars, last pushed 7d ago), licensed MIT. It adds 37 tokens to every session and 4,075 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). It is 83% identical to llm-wiki, differing in 91 lines, and is treated as a copy.
Other skills, from other repositories
pinecone-research
Agent RAG and long-term memory with Pinecone.
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
vector-db
Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies.
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
local-embedding
Run embedding on-device with ONNX Runtime. Build from source, model selection, offline mode. Use when setting up local embedding without an API key.
dify
Use when building LLM applications with visual workflow — RAG knowledge bases, AI agents, chatbots with drag-and-drop orchestration. Dify: open-source LLM app platform supporting 30+ models (OpenAI, Claude, DeepSeek, Ollama, Qwen, GLM) with Docker deployment.