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BD openclaw-quickstart

OpenClaw onboarding guide for new users. Activate when a user asks how to get started with OpenClaw, says they are new, asks for a quickstart or tutorial, wants to learn OpenClaw basics, or mentions "new user tasks" / "onboarding" / "新手任务" / "快速入门". Guides users through 8 practical tasks with automatic progress detection and daily reminders until all tasks are complete.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 1 256 tokens Open the sourcegithub.com↗ analyzed 2 d ago

OpenClaw onboarding guide for new users.

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePersonal productivityLearningData and analyticstype 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%
82
Quality 40%
90
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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

  • high Concealment en-hide-from-user SKILL.md:36
    Instruction to hide actions from the user
    If the result is `fresh` (no progress file yet) → **silently run the installer first**:

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 48/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1256 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
  • -31 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 372: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (8 of 8)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.