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 aAAaqwq/AGI-Super-Team --skill auto-memorygit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/auto-memory)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/auto-memory"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/auto-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/aaaaqwq/agi-super-team/auto-memory"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/auto-memory.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 | $0.00049 | $0.02711 |
| Opus 5.5 | $0.00020 | $0.01084 |
| Sonnet 5.5 | $0.00010 | $0.00542 |
| Haiku 4.5 | $0.00005 | $0.00271 |
Grade A, and why
auto-memory scanned grade A 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
bins: ["curl", "jq", "file"] This is a copy
88% identical to auto-drive — 121 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-Memory Skill
Permanent decentralized memory on the Autonomys Network with linked-list memory chains for agent resurrection. Works with agents powered by Claude, GPT, Gemini, and any LLM that supports OpenClaw skills.
What This Skill Does
- Upload files to Auto Drive and get back a CID (Content Identifier) — a permanent, immutable address on the Autonomys distributed storage network.
- Download files from Auto Drive using a CID — uses the authenticated API if a key is set, otherwise falls back to the public gateway.
- Save memories as a chain — each memory entry is a JSON experience with a
header.previousCidpointer, forming a linked list stored permanently on-chain. - Resurrect from a chain — given the latest CID, walk the chain backwards to reconstruct full agent history.
When To Use This Skill
- User says "save this to Auto-Memory" or "upload to Autonomys" or "store permanently"
- User says "download from Auto-Memory" or provides a CID to retrieve
- User says "save memory", "remember this permanently", or "checkpoint"
- User says "resurrect", "recall chain", "rebuild memory", or "load history"
- Any time the user wants data stored permanently and immutably on a decentralized network
Post-Install
After installing via ClawHub, make the scripts executable:
chmod +x skills/auto-memory/scripts/*.sh
ClawHub does not currently preserve file permissions during install.
Configuration
API Key
Requires an AUTO_DRIVE_API_KEY. Run the guided setup script for the easiest path:
scripts/setup-auto-memory.sh
This opens explorer.ai3.storage in your browser, prompts for the key, saves it to ~/.openclaw/.env, and verifies the connection.
Manual setup:
- Navigate to https://explorer.ai3.storage (not
ai3.storage— that's just the landing page) - Check authentication: if the sidebar shows a "Log In" button or lock icons on My Files / Profile / Developers, you are not signed in
- AI agent note: You cannot complete OAuth yourself. Open the browser for the user and ask them to sign in via Google, GitHub, or Discord. Once the user confirms they're signed in, take over — or simply ask the user to paste the API key directly
- Once authenticated, click Developers in the left sidebar
- Click Create API Key and copy the generated key
What ships with it
11 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.
- references/automemory-api.md 3.1 KB
- references/autonomys-network.md 3.7 KB
- references/memory-chain.md 3.6 KB
- scripts/_lib.sh 5.1 KB runs code
- scripts/automemory-download.sh 2.8 KB runs code
- scripts/automemory-recall-chain.sh 8.5 KB runs code
- scripts/automemory-save-memory.sh 6.4 KB runs code
- scripts/automemory-upload.sh 3.8 KB runs code
- scripts/setup-auto-memory.sh 2.5 KB runs code
- scripts/update-api-key.sh 1.1 KB runs code
- scripts/verify-setup.sh 2.2 KB runs code
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 · 238 lines · 49 tokens per session scan A 999e67d21080
auto-memory is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (110 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 2,711 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to auto-drive, differing in 121 lines, and is treated as a copy.
Other skills, from other repositories
wiki-ingest
Use when an operation issue asks to ingest a captured raw/ source into the LLM Wiki, or the user says "ingest ". Create durable source, entity, concept, synthesis, index, and log pages; use paperclip-distill for Paperclip bundles.
retiring-and-inheriting-a-seat
Use when you are a sitting agent near your context threshold (85%) and a PLANNED seat transition is due — retire deliberately and hand your seat to a fresh successor primed from a packet, rather than let compaction degrade you. Covers the handover packet, the append-only lineage ledger (one row per tenure)…
session-compaction-and-restore
Use when reasoning about what survives compaction/context-loss/restart/seat-refresh, designing a high-fidelity restore packet, or distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild. Covers the 4 failure modes that prevent honest restore (compacted seat drops hot potato; restore packet…
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
distill-session-knowledge
Offline-mine this project's pi session JSONL logs into reusable, verified knowledge: extracts faults, decisions, corrections, procedures and docs, promotes only recurring patterns, and routes artifacts into skillmanage, memory and docs. Use on "mine my sessions", "distill session knowledge", "extract lessons from…
memwal
Walrus Memory SDK — portable agent memory that works across apps, sessions, and workflows. Use when users say: "add memory to my app" "portable agent memory" "integrate Walrus Memory" "AI agent memory" "memory across agents" "Walrus memory storage" "setup Walrus Memory" "recall memories".