AB aisa-provider
Configure AIsa as a first-class model provider for OpenClaw, enabling production access to major Chinese AI models (Qwen, DeepSeek, Kimi K2.5, Doubao) through official partnerships with Alibaba Cloud, BytePlus, and Moonshot. Use this skill when the user wants to set up Chinese AI models, configure AIsa API access, compare pricing between AIsa and other providers (OpenRouter, Bailian), switch between Qwen/DeepSeek/Kimi models, or troubleshoot AIsa provider configuration in OpenClaw. Also use when the user mentions AISA_API_KEY, asks about Chinese LLM pricing, Kimi K2.5 setup, or needs help with Qwen Key Account setup.
As a process B 75/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting
How to improve
- 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
-
low Exfiltration
net-credential-usereferences/guide-zh-CN.md:88Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)curl https://api.aisa.one/v1/models -H "Authorization: Bearer $AISA_API_KEY"
vendor-host -
low Exfiltration
net-credential-useSKILL.md:240Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)curl https://api.aisa.one/v1/models -H "Authorization: Bearer $AISA_API_KEY"
vendor-host -
low Exfiltration
net-credential-useSKILL.md:259Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)curl https://api.aisa.one/v1/models -H "Authorization: Bearer $AISA_API_KEY"
vendor-host
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "source" - note
frontmatter-keyunknown frontmatter key "primaryEnv" - note
frontmatter-keyunknown frontmatter key "requires"
Process rating: all ten parameters 75/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 51 steps
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3089 tokens
- low 11 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)
- -233 emoji in the instructions: noise for the model
- -43 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +3Description length 624: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 51 items
- +3Output format is stated explicitly
- +4Has examples (12 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.