SKILLEMALL.ai
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CC humanizer

Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."

QuivrHQ/quivr Agent Skills author: The-Vibe-Company MIT 3 files body ≈ 7 970 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Rewrite AI-sounding text so it reads like the writer without changing what it says.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorWriting and documentsSales and CRMCustomer supporttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • 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 body-long SKILL.md body ≈ 7970 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 70, 199, 200): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 23 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7970 tokens
  • 100Steps. 15 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • 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
  • +2Single-language instructions
  • +3Description length 341: enough signal without eating the budget
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 15 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.