AC wechat-miniprogram-cloudbase-deploy
WeChat Mini Program deployment onto WeChat CloudBase (云开发): deploy cloud functions via tcb, manually create database collections in the console, and upload the frontend via miniprogram-ci. Covers real-environment pitfalls: CLI cannot create collections, tcb fn invoke returns a Namespace metadata bug, the upload IP-whitelist only accepts IPv4 (so an IPv6 egress is always rejected), and the trial env defaults to ap-shanghai. Trigger when the task involves 部署微信小程序, 云开发, CloudBase, tcb 部署云函数, or miniprogram-ci 上传.
WeChat Mini Program deployment onto WeChat CloudBase (云开发): deploy cloud functions via tcb, manually create database collections in the console, and upload…
As a process C 59/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
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 · 1
✓ No critical or high findings
Medium and low: 1
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low Dangerous commands
cmd-background-processreferences/pitfalls.md:156Starts a background / autostarted processnohup $NODE $TCB login |& tee /tmp/tcb-login.log &
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 59/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 19 mutating operations with no state check
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 14 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1171 tokens
- 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
- +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
- +2Single-language instructions
- +3Description length 515: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.