SKILLEMALL.ai
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AC paper-polisher

AI Detection · 4-Layer DeAI Gate · De-AI Rewriting · Terminology Check · Metaphor Audit · Quality Report. Bilingual (CN/EN), 100% local, zero upload. 9-layer word engine with 1002 patterns and model-specific fingerprinting for Chinese LLMs, PLUS stylometric style analysis that catches AI-paraphrased drafts which dodge word blacklists (EVAL gap 49.2), translation-smell layer, and a weighted-fusion composite verdict from one command.

ClawHub Agent Skills author: docsor1212 v2.0.0 MIT-0 19 files body ≈ 2 842 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructureData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
97
Quality 40%
79
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token data/terminology.json:3864
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "cn": "20%美国风湿病学会改善Americancollegeofrheumatology20%",
    quoted
  • low Secrets in code secret-high-entropy-token data/terminology.json:3870
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "cn": "50%美国风湿病学会改善Americancollegeofrheumatology50%",
    quoted
  • low Secrets in code secret-high-entropy-token data/terminology.json:3876
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "cn": "70%美国风湿病学会改善Americancollegeofrheumatology70%",
    quoted

Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 7 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 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 2842 tokens
  • 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

  • +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
  • +1No license
  • +2Single-language instructions
  • +3Description length 435: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 8 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
The skill is mostly a local paper-analysis tool, but it under-discloses remote web-note editing and PDF-generation behavior while also encouraging AI-detection evasion.
LLM: suspicious (high) · 15 Aug 2026