SKILLEMALL.ai

BC huo15-xiaohongshu

Use when the user wants to write, analyze, or improve Xiaohongshu (小红书) content — drafting notes, coaching writing skills, diagnosing AI-speak or Jarvis-trap patterns, researching trending topics, reverse-engineering viral notes, designing brand wordplay or content series, running weekly reviews, or learning copywriting craft. Also use when the user mentions 小红书, xhs, xiaohongshu, 爆款文案, Allen 流, or asks about content strategy for Chinese social media platforms. Do NOT use for automated posting or account automation.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Job Zhao v3.10.0 MIT-0 79 files body ≈ 2 084 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

AnalyzerMarketingWriting and documentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
82
Quality 40%
86
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Exfiltration
If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 1

  • high Exfiltration intent-browser-credential-store skill-card.md:22
    Accesses a browser credential / cookie store
    Risk: The skill can use Xiaohongshu cookies or a logged-in Chrome profile. <br>

Files scanned: 79. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "aliases"
  • note frontmatter-key unknown frontmatter key "dependencies"

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
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 25 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2084 tokens
  • low 18 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
  • +3Output format is not stated: the model decides each time
  • -315 of 44 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 521: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

External checks

ClawHub: suspicious
This is mostly a Xiaohongshu writing assistant, but it includes logged-in scraping and anti-detection browser automation that users should review carefully before installing.
LLM: suspicious (high) · VirusTotal: · 29 May 2026