SKILLEMALL.ai

BB annual-insurance-word-report-openclaw

Generate annual insurance welfare Word reports from `gh_hg_bscyearall_dues` in OpenClaw format. The packaged Python entry extracts the target year, inspects MySQL column comments, maps business fields into the bundled Word template, and writes the final `.docx` report.

ClawHub Agent Skills author: 番茄番茄番茄范 v1.0.0 MIT-0 11 files · 1 script body ≈ 454 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 66/100 · Nearly there — weak spots: result and completion, when it triggers, consistency

GeneratorWordMySQLData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
94
Quality 40%
68
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Obfuscation medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

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.

For the author

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

  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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Obfuscation obf-base64-blob assets/beijing_office_annual_template.docx.base64.txt:1
    Long base64-looking blob
    UEsD…AJy+tmdA…STU/jMBC9I/Efot…g2i/34nl…4JO/og7ZmmRaL
  • low Secrets in code secret-high-entropy-token assets/beijing_office_annual_template.docx.base64.txt:1
    High-entropy token-like string (may be an id, hash or a credential)
    UEsD…AJy+tmdA…STU/jMBC9I/Efot…g2i/34nl…4JO/og7ZmmRaL

Files scanned: 11. 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 66/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (annual-insurance-word-report-openclaw) differs from the folder (annual-insurance-word-report)
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 21 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 454 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)
  • +3Output format is not stated: the model decides each time
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 269: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: clean
This skill appears to generate a Word report from a MySQL database as advertised, with credential-handling caveats users should understand.
LLM: benign (high) · VirusTotal: · 29 May 2026