AC human-style-writing
Human-like writing for **daily chat + social media only** (CN/EN/mixed). Routes requests into daily chat (texts/DMs) or platform-specific social posts: X/Twitter (tweet/thread), Reddit (post/comment), LinkedIn, Instagram caption, TikTok caption, 小红书/RedNote 笔记, WeChat Moments/朋友圈, plus generic social posts. Use when the user asks to make writing sound human, less "AI", or explicitly mentions tweet/X/Twitter thread, Reddit post/comment, LinkedIn post, Instagram/TikTok caption, 小红书/RedNote, 朋友圈/WeChat Moments, or a social post/caption.
As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, 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 · 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
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Human-like writing for **daily chat + social media only** (CN/EN/m… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 64/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 5 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 70Failures and branches. 5 branches
- 100Tools and files. No external tools needed
- 100Steps. 62 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1421 tokens
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 539: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 62 items
- +4Reference files are cited in the instructions (8 of 8)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.