BC operator-humanizer
Transform AI-generated text into authentic human writing. Detects and eliminates AI tells across 24 content/language/style/communication patterns, 500+ AI vocabulary terms, and structural clichés (binary contrasts, negative listings, false agency, dramatic fragmentation, narrator-from-a-distance). Analyzes statistical signals (burstiness, vocabulary diversity, sentence uniformity). Injects personality through parenthetical asides, tangents, rhythm variation, and strategic specificity. Use when humanizing content, checking for AI tells, removing robotic patterns, making text sound less polished, or writing like a specific person. Works on social posts, articles, emails, marketing copy, newsletters, scripts, or any text that needs to sound like a real human wrote it.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.
Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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 · 2
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high Obfuscation
uni-zero-widthreferences/personality-injection.md:484Zero-width / invisible characters (possible hidden text) (3 occurrences)␀
Medium and low: 1
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medium Obfuscation
uni-zero-widthreferences/examples.md:130Zero-width / invisible characters (possible hidden text) (4 occurrences) (test fixture / example file)␀
fixture
Files scanned: 11. 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 59/100
- 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. 9 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 50 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2487 tokens
- low 10 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
- +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
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 775: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 50 items
- +4Reference files are cited in the instructions (7 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.