AB zm-wechat-draft-publish-verify
ZM 公众号草稿发布与核验。用于将已准备好的 Markdown/HTML 公众号稿通过 zm-md2wechat-conversion-tool 推送到公众号草稿箱，并强制执行 draft/get 真实落库与排版核验。
ZM 公众号草稿发布与核验。用于将已准备好的 Markdown/HTML 公众号稿通过 zm-md2wechat-conversion-tool 推送到公众号草稿箱，并强制执行 draft/get 真实落库与排版核验。
As a process B 71/100 · Nearly there — weak spots: failures and branches, running it twice, progress reporting
AnalyzerAI and agentsSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 0
✓ No critical or high findings
Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 71/100
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 27 mutating operations with no state check
- 60Tools and files. Uses tools (bash) 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. 78 steps
- 100Result and completion. Output format and completion criterion are stated
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1994 tokens
- low 16 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)
- +3Description length 109: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +4Structure: 22 headings
- +3Step-by-step instructions: 78 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.
External checks
ClawHub: clean
This skill is a disclosed WeChat draft-publishing helper that uses expected credentials and network calls to create and verify drafts, with no evidence of hidden exfiltration or destructive behavior.
LLM: benign (high) · VirusTotal: · 23 Jun 2026