AD Content Humanizer Audit
AI content signature detector for social media posts. Measures 8 linguistic dimensions that LinkedIn's 360Brew and other platforms use to detect AI-generated content — lexical diversity, sentence length variance, transition word density, hedging ratio, contraction usage, personal pronoun density, question frequency, and specific data density. Not a humanizer that rewrites your text — an auditor that tells you exactly which signals are triggering detection so you fix only what's wrong. Research-backed (DivEye arXiv:2509.18880, LinkedIn 360Brew algorithm analysis, stylometric detection studies). Per-platform thresholds for LinkedIn (strictest), Reddit, Twitter/X, HackerNews. Zero external dependencies.
As a process D 39/100 · Unfinished process — 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
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 39/100
- 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
- 30Running it twice. 6 mutating operations with no state check
- 40Consistency. Frontmatter name (Content Humanizer Audit) differs from the folder (phy-content-humanizer-audit)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 15 steps
- 100Execution cost. Instruction body is 1934 tokens
- low 12 top-level sections: this looks like several domains in one skill
- high The skill tells the model to perform an irreversible action with no human approval
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
- +2Single-language instructions
- +3Description length 709: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (5 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.