SKILLEMALL.ai
GitHub ↗ RU Sign in

AB anson

Bootstrap and personalize an AI assistant with a rich, bespoke identity, user profile, and soul. Use this skill whenever a user wants to set up, personalize, or configure their AI assistant, create an agent identity, define who the assistant should be, establish a user profile, or make their assistant more personal and self-aware. Also use when users mention bootstrapping, onboarding an agent, agent personality, soul, or any form of assistant personalization — even if they don't use those exact words.

ClawHub Agent Skills author: Edmundworks v1.0.0 MIT-0 15 files body ≈ 1 366 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process B 70/100 · Nearly there — weak spots: result and completion, inputs and preconditions

GeneratorAI 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
70/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
70
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: 15. 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 70/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 70Failures and branches. 5 branches
    • 85Steps. 22 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1366 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • high The skill tells the model to perform an irreversible action with no human approval

    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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 506: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 22 items
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This personalization skill is not clearly malicious, but it stores durable user profiles, reads broad local context, generates more skills, and changes future agent behavior with too little explicit user control.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026