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/xiaoluolyg/god/memorynpx skills add XiaoLuoLYG/GOD --skill memorygit clone --depth 1 https://github.com/XiaoLuoLYG/GODWhat 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 | $0.00017 | $0.01999 |
| Opus 5 | $0.00009 | $0.01000 |
| Sonnet 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 2d 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.
- 2d ago First seen · 215 lines · 17 tokens per session scan A d27c4d982727
memory is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,091 stars, last pushed 5d 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. 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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