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AC openclaw-memory-transfer

Zero-friction memory migration from other AI assistants (ChatGPT, Claude.ai, Gemini, Copilot, Perplexity, Cursor, Windsurf, etc.) into OpenClaw. Triggers: "migrate from ChatGPT", "I used to use Gemini", "import my memory", "memory transfer", "bring my data from X", "switch from ChatGPT", "迁移记忆", "从ChatGPT搬过来", "记忆导入", "I'm coming from Claude", "transfer my preferences", "import from Cursor". Supports both cloud-based AI (prompt-guided export) and local agents (automatic file scanning). Parses ChatGPT data export ZIP files for zero-effort migration.

ClawHub Agent Skills author: MyClaw.ai v1.0.1 MIT-0 12 files body ≈ 2 626 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 63/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceAI 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
63/100
Has gaps
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 · 0

    ✓ No critical or high findings

    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 63/100

    • 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. 7 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 36 steps
    • 100Failures and branches. 6 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2626 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 10 example trigger phrases
    • +3Description length 554: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 36 items
    • +4Has examples (8 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    This memory-migration skill is coherent, but it can automatically read broad local assistant files and process very sensitive history, so it needs user review before installation.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026