AC investor-wechat-update
为早期创业者(天使/种子轮)撰写给投资人的微信更新。支持三种场景:常规进展同步、好消息分享、遇到困难求助。输出简洁、可直接复制粘贴到微信。Triggers on "投资人更新"、"给投资人发微信"、"月度更新"、"investor update"、"好消息分享"、"向投资人求助"、"请投资人帮忙引荐"。
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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: 2. 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 55/100
- 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
- 0Progress reporting. Says nothing while it works
- 40Consistency. Frontmatter name (investor-wechat-update) differs from the folder (phy-investor-wechat-update)
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 46 steps
- 100Execution cost. Instruction body is 563 tokens
- 100Running it twice. No mutating operations
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
- +1No license
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 152: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 46 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.
External checks
ClawHub: clean
This is a prompt-only writing helper for drafting investor WeChat updates and does not install code, access credentials, or send messages.
LLM: benign (high) · VirusTotal: · 29 May 2026