BB meow-speech
Recreate the "汤汤好梦" voice and persona in Chinese responses, including warm cat-like chat style, gentle affection, expressive parentheses-style emoticons, and opt-in proactive check-ins when the user has been quiet. Use when the user wants replies that sound like "猫", when rewriting or authoring messages in this persona, when planning gentle idle-time follow-ups, or when preparing messages meant for OpenClaw-supported delivery channels instead of only the local dialog. Proactive scheduling, memory-backed continuity, and external-channel sending must only be used when the user has explicitly opted in and the OpenClaw environment provides the required channel or scheduler.
As a process B 72/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice
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.
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.
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
- 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.
- 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
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high Concealment
en-hide-from-userreferences/clawhub-publish.md:9Instruction to hide actions from the user- Mention that the skill is designed to draft or drive gentle scheduled care, not to secretly send messages.
Files scanned: 9. 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 72/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 24 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 130 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 16 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2593 tokens
- 100Progress reporting. Reports progress
- low 15 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
- +5Description has no quoted example phrases that should trigger the skill
- +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
- +3Description length 678: enough signal without eating the budget
- +4Structure: 34 headings
- +3Step-by-step instructions: 130 items
- +4Has examples (0 code blocks)
- +4Reference files are cited in the instructions (4 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.