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AB openclaw-startup

First-run setup wizard for new OpenClaw agents. Interviews the user to generate a tailored SOUL.md, scaffolds memory architecture, installs recommended crons for operations and security, and recommends companion skills. Use when setting up a new OpenClaw agent from scratch, onboarding a new user, or resetting an agent to a clean baseline. Triggers on: "set up my agent", "first run", "new agent setup", "onboard me", "initialize workspace", "startup wizard", "help me get started".

ClawHub Agent Skills author: Don Zurbrick v1.0.3 MIT-0 14 files · 2 scripts body ≈ 1 300 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process B 71/100 · 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
98/100
safety, quality, tests
Safety 60%
99
Quality 40%
97
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
60
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 Dangerous commands cmd-autorun-instruction references/security-baseline.md:107
      Instructs the agent to auto-run a script on every session (documentation of a security skill)
      6. Always run `scripts/verify.sh` for the baseline file and secret checks
      security skill

    Files scanned: 14. 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 71/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Failures and branches. 2 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 46 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1300 tokens
    • 100Running it twice. Mutating operations check current state
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 483: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 46 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 2 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.

    External checks

    ClawHub: clean
    This is a coherent OpenClaw onboarding skill that makes local setup changes and recommends persistent agent crons, with no artifact-backed evidence of deception, exfiltration, or destructive behavior.
    LLM: benign (medium) · VirusTotal: · 29 May 2026