AF zmm
📐 詹明明 ——两套技能的总入口：做内容（选题/写稿/审核/复盘）+ 看生意（组合体检/营收归因/客户集中度/依赖风险/拿不准的决策）。三种模式：新手上路演示、任务前路由、任务后导航。不知道用哪个就回这里。 触发方式：/zmm、/新手上路、/zmm 教程、「做条视频」「出个口播」「今天拍什么」「内容下一步怎么走」；新手教程：/zmm 新手指南、/zmm 新手、「这个怎么用」「第一次用，带我走一遍」「不知道能干嘛」 Single entry point for both skill sets — content (topic, script, review, retro) and business (portfolio, revenue, concentration, dependency, decisions). Three modes: guided onboarding demo, pre-task routing, post-task navigation. Trigger: /zmm, "make a short video", "what should I shoot today", "how do I use this" —— 📐 詹明明 · 不给公式，给判据。每条规则都标了实测代价。
📐 詹明明 ——两套技能的总入口：做内容（选题/写稿/审核/复盘）+ 看生意（组合体检/营收归因/客户集中度/依赖风险/拿不准的决策）。三种模式：新手上路演示、任务前路由、任务后导航。不知道用哪个就回这里。 触发方式：/zmm、/新手上路、/zmm…
As a process F 35/100 · Will not run — References files that are not bundled: references/理论底座.md
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: references/理论底座.md - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName"
Process rating: all ten parameters 35/100
- 0Tools and files. 1 referenced file(s) missing: references/理论底座.md
- 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
- 20When it triggers. No condition that starts the skill
- 100Steps. 36 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3948 tokens
- 100Running it twice. No mutating operations
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -228 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 559: enough signal without eating the budget
- +4Structure: 41 headings
- +3Step-by-step instructions: 36 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (5 of 7)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.