SKILLEMALL.ai

AB ai-writing-diagnosis

AI-writing fingerprint diagnosis for Chinese text. Use when Codex needs to inspect a draft for overly smooth, formulaic, generic, or authorless writing patterns; quote the exact passages that feel machine-made; distinguish real problems from false alarms; and suggest what to fix first without immediately rewriting the whole piece.

ClawHub Agent Skills author: cellinlab v0.1.0 MIT-0 6 files body ≈ 949 tokens Open the sourceclawhub.ai analyzed 5 d ago

As a process B 69/100 · Nearly there — weak spots: inputs and preconditions, consistency, progress reporting

AnalyzerInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
69/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

    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: 6. 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 69/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (ai-writing-diagnosis) differs from the folder (cell-ai-writing-diagnosis)
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 55 steps
    • 100Execution cost. Instruction body is 949 tokens
    • 100Running it twice. No mutating operations

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 332: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 55 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    No malicious behavior is evident from the supplied scan signals or the available workspace context.
    LLM: benign (medium) · VirusTotal: · 29 May 2026