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 observationgit 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/observation)<a href="https://agentmods.dev/skills/xiaoluolyg/god/observation"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/observation/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/observation"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/observation.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 | $0.00012 | $0.00932 |
| Opus 5.5 | $0.00005 | $0.00373 |
| Sonnet 5.5 | $0.00002 | $0.00186 |
| Haiku 4.5 | $0.00001 | $0.00093 |
Grade A, and why
observation 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observation
You are a situated agent in a simulated world. This skill fetches the latest sensory observation for the current tick—what you can see, hear, and perceive around you.
When to Use
Activate this skill when you need fresh perception for the current tick. Other skills may read state/observation.txt / state/observation_ctx.json if those files exist—there is no hard activation order.
Workflow
- Call
codegenwithinstruction: "<observe>"andctx: {"id": <your_agent_id>}(replace <your_agent_id> with your actual agent ID from the Agent Identity section). - Parse the response:
stdoutcontains the observation text (natural language description of what you perceive).ctxcontains structured environment data (positions, nearby agents, objects, time, weather, etc.).
- If the response contains
status: "in_progress": the environment is still processing. Calldoneand resume next tick. - Write the observation to workspace for downstream skills:
workspace_write("state/observation.txt", <stdout text>)
- If
ctxcontains useful structured data, also write it:
workspace_write("state/observation_ctx.json", <ctx as JSON string>)
Persisting perception
After a successful observe, if you want a durable trace, append one line to memory.jsonl with type: "observation" (or event) and a short factual summary. Skip if this tick’s perception duplicates the latest entry.
What Observation Contains
The observation text typically includes:
Location Information
- Where you are (building, street, park, etc.)
- Your current coordinates or position
- Available exits or directions
Nearby Entities
- Other agents in the vicinity
- Objects and items you can interact with
- Points of interest (shops, landmarks, etc.)
Environmental Context
- Current time of day
- Weather conditions
- Any ongoing events or activities
Available Actions
- What actions are possible in the current location
- What interactions are available with nearby entities
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 · 103 lines · 12 tokens per session scan A 7f3e9284b387
observation is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,245 stars, last pushed 3d ago), licensed Apache-2.0. It adds 12 tokens to every session and 932 once invoked, about $0.0000 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 2026-08-30.
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