SKILLEMALL.ai

CC fund-operation-workflow

[何时使用]当用户需要构建基金运营工作流时;当用户说"基金互金电商业务搭建"时;当需要按 MBTI 特质分配工作流节点任务时;当检测到"收集/归档/策略/实施/测试/监控"等工作流关键词时;当需要使用 STAR+ 合规前置框架设计提示词时

Not recommendedcritical or high security findings
ClawHub Agent Skills author: lj22503 v2.0.0 MIT-0 17 files body ≈ 1 003 tokens Open the sourceclawhub.ai analyzed 2 d ago

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

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
C
68/100
safety, quality, tests
Safety 60%
64
Quality 40%
73
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.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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.
  2. 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

  • high Dangerous commands cmd-pipe-to-shell GITHUB_README.md:53
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://openclaw.ai/install.sh | bash
  • high Dangerous commands cmd-pipe-to-shell WECHAT_FUNNEL_SOP.md:184
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://openclaw.ai/install.sh | bash

Files scanned: 17. 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")
  • note frontmatter-key unknown frontmatter key "created"
  • note frontmatter-key unknown frontmatter key "updated"
  • note frontmatter-key unknown frontmatter key "skill_type"
  • note frontmatter-key unknown frontmatter key "related_skills"

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
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1003 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 119: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -212 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The core skill is a prompt-only fund-operations workflow, but it also includes an off-platform WeChat/Feishu user-acquisition and tracking funnel that collects contact and company details without adequate privacy boundaries.
LLM: suspicious (high) · VirusTotal: · 29 May 2026