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AD auto-drive

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.

modbender/skill-library-mcp Agent Skills author: modbender MIT 12 files · 8 scripts body ≈ 1 974 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Upload and download files to Autonomys Network permanent decentralized storage via Auto-Drive.

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
77
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token references/memory-chain.md:71
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      The contract is at `0x51…A3c` on Autonomys Mainnet (Chain ID 870). It provides:
      quoted

    Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 48/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 40Consistency. Frontmatter name (auto-drive) differs from the folder (auto-drive-jim)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Failures and branches. 4 branches
    • 100Steps. 32 steps
    • 100Execution cost. Instruction body is 1974 tokens
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 8 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 196: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.