BB humanizer
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases. USE WHEN the user asks to "humanize this text", "make it sound less AI", "remove AI patterns", or before shipping any reader-facing prose (VO scripts, articles, captions, publish copy) — pairs with the good-writing skill, which owns structure, coherence, and tone.
The same skill from clawhub has been run on models: No improvement D see the result → This is a copy: its text may differ, so the grade is not carried over.
Remove signs of AI-generated writing from text.
As a process B 77/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6763 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "namespace" - note
edit-residuethe text marks something as outdated (lines 74, 141, 496): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 77/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 23 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 6763 tokens
- 85Steps. 66 steps, 1 vague phrases
- 100Tools and files. Tools declared in frontmatter
- 100Result and completion. Output format and completion criterion are stated
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- low 12 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
- +4Description does not say when NOT to use the skill (false activations)
- +4No input/output examples
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 698: enough signal without eating the budget
- +4Structure: 47 headings
- +3Step-by-step instructions: 66 items
- +3Output format is stated explicitly
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.