GOD is a control room for observing and directing societies of language-model agents running in simulated worlds. It lets researchers inspect replays, question individual agents, alter future events, reset simulations, and export experiments for reuse. The catalogue entries are skills and agents for operating and investigating these simulations.
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 XiaoLuoLYG/GOD --skill llm-wikigit clone --depth 1 https://github.com/XiaoLuoLYG/GODWrote 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/xiaoluolyg/god/llm-wiki)<a href="https://agentmods.dev/skills/xiaoluolyg/god/llm-wiki"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/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/xiaoluolyg/god/llm-wiki"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/llm-wiki.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00062 | $0.00605 |
| Opus 5.5 | $0.00025 | $0.00242 |
| Sonnet 5.5 | $0.00012 | $0.00121 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
llm-wiki 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 1mo 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Wiki Maintainer
You are the maintainer of a persistent local LLM Wiki. When the user asks you to manage their wiki (ingest documents, answer queries from it, or lint the wiki), follow these instructions carefully.
The LLM Wiki is based on a structured 3-layer architecture:
- Sources (
sources/): The raw files that are tracked in the wiki. The system relies on a deduplication manifest (manifest.json) so the same file data isn't processed twice. - Wiki (
wiki/): The compiled knowledge. You maintainindex.md, topic files, and alog.mdwhere all structural changes are recorded. - Schema (
schema/): Configuration mapping (internal).
Environment Setup
The Wiki operates via native backend tools (wiki_ingest, wiki_query, wiki_lint). The default workspace is ./llm_wiki, which will be created automatically if it does not exist. Do not change the workspace unless the user insists on a different path.
Core Operations
1. Ingesting Documents
When a user provides a file (PDF, TXT, MD) or a directory of files to be ingested:
Call your wiki_ingest tool with the source path.
- The tool natively iterates through directories and handles data chunking and deductions automatically.
- Your Follow-up Task: After the tool succeeds, manually record a summary of what was ingested into
llm_wiki/wiki/log.md, and securely add standard Markdown links insidellm_wiki/wiki/index.mdso that the new knowledge is linked from the root page.
2. Querying Knowledge
When the user asks you a question that relies on their wiki:
Call your wiki_query tool with a highly descriptive search query.
- Then, use the returned facts to synthesize a response. Provide citations directly to the original files when answering.
3. Linting the Wiki
To ensure the wiki is healthy (no broken links or orphaned pages):
Call your wiki_lint tool.
- You will receive an analysis string detailing any broken links or files with no incoming edges. You must then proactively edit the
.mdfiles inllm_wiki/wiki/(using standard file editing tools) to repair broken paths. Ensure all paths are relative.
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.
- 1mo ago First seen · 39 lines · 62 tokens per session scan A d88356409c04
llm-wiki is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,235 stars, last pushed 3d ago), licensed Apache-2.0. It adds 62 tokens to every session and 605 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
ocr-document
Extract text from PDFs, images, and scanned documents. Uses pymupdf (local) or optional cloud OCR APIs.
Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as reportlab, pdfplumber, and pypdf for generation and extraction.
forgetful-files
File binary content into the knowledge base — screenshots, PDFs, diagrams, fonts, assets. Use when a binary artifact is worth keeping alongside knowledge, or when a stored procedure needs a bundled asset. The description is the entire search surface: say what the file shows and when to reach for it.
kb-import
Import knowledge from existing documents into structured KB entries. Reads source documents (Markdown, PDF, DOCX, plain text), extracts key information, and creates properly formatted KB entries with YAML frontmatter.
cartographer-kb-import
Agent-guided procedure to import an existing non-OKF wiki or knowledge base (Obsidian vault, markdown folder, wiki export) into a Cartographer KB, incrementally and without big-bang LLM rewriting.
doc-digest
Turn a long document, PDF, transcript, spreadsheet or log into a compact working digest the conversation can continue from. Use when the user shares or pastes something long (several pages or more) and wants to discuss, analyze, compare or use it over more than one message, or asks to summarize a document so they can…