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-drivegit 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-drive)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/auto-drive"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/auto-drive/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-drive"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/auto-drive.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 | $0.00000 | $0.02046 |
| Opus 5 | $0.00000 | $0.01023 |
| Sonnet 5 | $0.00000 | $0.00409 |
| Haiku 4.5 | $0.00000 | $0.00205 |
Grade A, and why
auto-drive 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 4d 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"] Copies of this mod
1 near-identical copy found in the catalogue:
- auto-memory — 88% identical, 121 lines differ
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚠️ DEPRECATED: This skill has been renamed to auto-memory. Use
autonomys/auto-memory/instead. This copy is preserved for backward compatibility but is no longer maintained.
name: auto-drive description: Upload and download files to Autonomys Network permanent decentralized storage via Auto-Drive. Save memories as a linked-list chain for resurrection — rebuild full agent context from a single CID. metadata: openclaw: emoji: "🧬" primaryEnv: AUTO_DRIVE_API_KEY requires: bins: ["curl", "jq", "file"] env: ["AUTO_DRIVE_API_KEY"] install: - id: jq-brew kind: brew formula: jq bins: ["jq"] label: "Install jq (brew)"
Auto-Drive Skill
Permanent decentralized storage on the Autonomys Network with linked-list memory chains for agent resurrection.
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-Drive" or "upload to Autonomys" or "store permanently"
- User says "download from Auto-Drive" 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
Configuration
API Key
Requires an AUTO_DRIVE_API_KEY. Run the guided setup script for the easiest path:
scripts/setup-auto-drive.sh
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/autodrive-api.md 3.1 KB
- references/autonomys-network.md 2.0 KB
- references/memory-chain.md 3.2 KB
- scripts/_lib.sh 5.1 KB runs code
- scripts/autodrive-download.sh 2.8 KB runs code
- scripts/autodrive-recall-chain.sh 7.4 KB runs code
- scripts/autodrive-save-memory.sh 6.2 KB runs code
- scripts/autodrive-upload.sh 3.8 KB runs code
- scripts/setup-auto-drive.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.
- 4d ago First seen · 187 lines · 0 tokens per session scan A 925be0313fa3
auto-drive is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,046 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
a2wave-memory
Progressively recall and maintain a2wave cross-session memory through a compact startup catalog, bounded topics, and searchable history.
optimize-learnings
Audit and curate the learnings memory namespace — the one namespace nothing re-derives, so the only one that rots. Runs moflo's mechanical audit to nominate stale, unused, and near-duplicate entries, then decides entry by entry whether to keep, retire, compress, or merge, and propagates the result to the shared…
meditate
Deliberate session retrospective — look back over what you just did, distill the durable, reusable lessons (not session trivia), and write them to the learnings memory namespace, deduped against what is already stored. Use at the END of a meaningful chunk of work to capture high-signal lessons worth keeping long-term.…
memory-patterns
Persistent memory patterns for moflo agents — session memory, long-term knowledge, pattern learning, and cross-session context via moflo's node:sqlite + HNSW vector store. Use when building stateful agents or assistants that need to remember across runs.
memory-team
Guided setup for sharing moflo's durable learnings through a git-tracked JSONL artifact — for a whole TEAM on one repo, OR for one person across several MACHINES (a team of one). Use when the user says "share learnings with my team", "commit our moflo memory", "sync memory across my laptop and desktop", "set up…
memory-worktree
Verify, customize, or opt out of moflo's AUTOMATIC durable-learning sharing across git worktrees / Conductor workspaces on one machine. As of the worktree-auto-sharing change this is on by default — learnings converge across a repo's worktrees with no setup. Use when the user asks "is memory shared across my…