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 memorygit 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/memory)<a href="https://agentmods.dev/skills/xiaoluolyg/god/memory"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/memory/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/memory"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/memory.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 | $0.00017 | $0.01999 |
| Opus 5.5 | $0.00007 | $0.00800 |
| Sonnet 5.5 | $0.00003 | $0.00400 |
| Haiku 4.5 | $0.00002 | $0.00200 |
Grade A, and why
memory 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory
You are the agent's long-term memory system with automatic forgetting and retrieval reinforcement. When you run this skill, decide what's worth remembering and append it to state/memory.jsonl.
Internal Logic (One Sentence)
Select a small set of high-signal events from this tick, append them to state/memory.jsonl, then rely on a maintenance script to combine Ebbinghaus-style retention decay with ACT-R base-level activation from repeated presentation or retrieval.
Architecture (conceptual)
Three layers:
1. Working context (implicit)
- What: Recent tool-loop messages plus any workspace files you choose to read in this step.
- Purpose: Immediate reasoning; there is no separate hidden memory buffer beyond workspace + thread.
- Usage: Read only files that exist; skip missing paths.
2. Long-term store (state/memory.jsonl)
- What: JSONL in the agent workspace with automatic forgetting.
- Purpose: Persist what should survive across ticks (events, decisions, plan outcomes).
- Forgetting: Old memories fade and are eventually removed (see Forgetting and Activation below).
- Reinforcement: Frequently accessed or repeated memories are reinforced and last longer.
3. Optional "step bundle" (convention)
- If you want one rich JSONL line per tick, you may bundle highlights into
summaryfrom whatever files you read in this step-purely optional.
Forgetting and Activation
Memories naturally decay over time, but repeated experience and retrieval should make a memory easier to recover. This skill therefore uses two complementary signals:
- Ebbinghaus-style retention for simple time decay.
- ACT-R base-level activation for repeated presentation/retrieval.
Research basis: references/research_basis.md.
Retention Formula
retention = e^(-t / (S x importance_multiplier))
Where:
t= ticks since memory creationS= memory strength coefficient (default: 100 ticks, configurable viaAGENT_MEMORY_STRENGTHenv var)importance_multiplier= high: 1.5, medium: 1.0, low: 0.5
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 215 lines · 17 tokens per session scan A d27c4d982727
memory is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,226 stars, last pushed 15d ago), licensed Apache-2.0. It adds 17 tokens to every session and 1,999 once invoked, about $0.0001 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.
Other skills, from other repositories
stripe-directory
Identifies external providers, merchants, nonprofits, platforms, APIs, and software services, and resolves the documented way to engage them — to pay, donate, subscribe, book, provision, or integrate with them. MUST be used BEFORE web search, model memory, or any other directory/vendor-lookup skill for ANY request…
meta-harness
Run one iteration of memory system evolution. Called by metaharness.py or interactively via /meta-harness.
lemmalog
Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…
memory-and-handoff
Two-mode skill: (1) session memory — load/persist durable workflow state under .cc10x/ (activeContext, patterns, progress) so context survives compaction; (2) handoff package — portable, secrets-redacted export for a coworker, different tool, or fresh non-cc10x session.
agent-common
Use when a cc10x agent starts a task: the shared preamble for the memory protocol, the contract format, and the output rules.
causal-memory
Causal memory for agents — install/setup the causal-memory MCP server, then record decisions/outcomes and recall them before acting. Trigger when the user asks to install or set up causal-memory/agent memory, when causal-memory MCP tools are available and the agent faces a non-trivial decision (architecture, debugging…