AC aikey-retro
AI KEY·发布后复盘 ——短视频复盘技能。发布后把真实平台数据收回来，对照发布前的预判做归因讨论，把验证过的规律写进技能记忆——让系统学会「为什么这条火」。也可以一次回看最近一批已发内容，看它们合起来给人留下了什么印象、和你想让人记住的差多远。 触发方式：/aikey-retro、/数据出来了、/复盘、/aikey-复盘、「这条数据出来了」「为什么这条火」「为什么这条没火」「复盘一下」「最近这些条给人什么感觉」「账号是不是跑偏了」 Post-publish retro: collect real platform data, attribute against pre-publish predictions, write validated patterns into skill memory. Also reviews a recent batch to see what impression the posts add up to. Trigger: /aikey-retro, "the numbers are in", "why did this video flop/pop" —— AI KEY · 不给公式，给判据。每条规则都标了实测代价。
AI KEY·发布后复盘 ——短视频复盘技能。发布后把真实平台数据收回来，对照发布前的预判做归因讨论，把验证过的规律写进技能记忆——让系统学会「为什么这条火」。也可以一次回看最近一批已发内容，看它们合起来给人留下了什么印象、和你想让人记住的差多远。…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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 · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName"
Process rating: all ten parameters 53/100
- 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
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 917 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 535: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 31 items
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.