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AA alibabacloud-tablestore-openclaw-memory

This skill installs and configures the **Tablestore Mem0** plugin for OpenClaw. Tablestore Mem0 uses Alibaba Cloud Tablestore as the vector store backend for mem0, providing persistent long-term memory for AI agents. Use this skill when the user wants OpenClaw to persist or manage long-term memory using Alibaba Cloud Tablestore as the backend. Triggers: "set up tablestore memory", "install tablestore mem0 plugin", "configure long-term memory with tablestore", "remember this".

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 3 files body ≈ 2 444 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process A 83/100 · Runs to the end — weak spots: result and completion, running it twice, progress reporting

ProcedureSoftware developmentInfrastructureAI 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%
93
Run on models
none yet
Process rating
A
83/100
Runs to the end
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
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: 3. 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 83/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 9 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 21 steps
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2444 tokens

    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 4 example trigger phrases
    • +3Description length 480: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 21 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    The skill has a legitimate memory setup purpose, but it asks for broad Alibaba Cloud control and can automatically expose Tablestore to the public internet, so it needs review before install.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026