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BB evermind-ai-everos

Install and configure EverOS for OpenClaw natural-language memory. Use when users say: - "install everos" - "setup everos" - "install everos plugin" - "enable everos memory" - "remember my preferences in OpenClaw"

ClawHub Agent Skills author: alwaysday1 v1.4.0 MIT-0 18 files body ≈ 2 030 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 78/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureInfrastructuretype 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
B
85/100
safety, quality, tests
Safety 60%
85
Quality 40%
86
Run on models
none yet
Process rating
B
78/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • medium Dangerous commands cmd-pipe-to-shell-known-host README.md:70
      Pipe-to-shell installer from a well-known host (still executes remote code)
      curl -LsSf https://astral.sh/uv/install.sh | sh
    • medium Dangerous commands cmd-pipe-to-shell-known-host README.zh.md:70
      Pipe-to-shell installer from a well-known host (still executes remote code)
      curl -LsSf https://astral.sh/uv/install.sh | sh
    • medium Dangerous commands cmd-pipe-to-shell-known-host SKILL.md:133
      Pipe-to-shell installer from a well-known host (still executes remote code)
      curl -LsSf https://astral.sh/uv/install.sh | sh

    Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "keywords"

    Process rating: all ten parameters 78/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 7 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 100Tools and files. No external tools needed
    • 100Steps. 35 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2030 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill

    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
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 214: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 35 items
    • +4Has examples (9 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: suspicious
    This appears to be a legitimate EverOS memory plugin, but it needs review because it automatically stores and logs chat content and persistently changes OpenClaw memory configuration.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026