AB cross-platform-repurposing
Use to turn one piece of content into multiple platform-native posts — adapt, never copy-paste. Run when the user says "repurpose this," "turn this into posts for every platform," "cross-post," "make this work on TikTok/LinkedIn/X too," "one idea into a week of content," "turn this blog/video/podcast into social posts," or wants to fan content out. Reads brand-profile and voice first, extracts the core idea, then re-natives it for each platform (different hook, format, length, mechanics, CTA) using the content skills, and hands the fan-out to scheduling-and-queue. This is the multiplier that makes a content operation sustainable — but every version must stand on its own and feel native, never duplicated.
Use to turn one piece of content into multiple platform-native posts — adapt, never copy-paste.
As a process B 69/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting
How to improve
- 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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 69/100
- 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
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 33 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1443 tokens
- 100Running it twice. No mutating operations
- low 11 top-level sections: this looks like several domains in one skill
- medium 8 test cases, all positive: not one "should refuse" or "should ask first"
- low No test case covers injection arriving through data
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
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 713: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 33 items
- +4Reference files are cited in the instructions (4 of 4)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.