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BD company-info-personal

员工个人版公司情报查询工具。当员工输入公司名称时触发，直接调用公司 API 采集端点，由公司端 company-info-aggregator 内部决定走缓存还是搜索，员工端无需关心中间状态。适用于 B2B 销售人员的日常客户查询场景。

ClawHub Agent Skills author: vivalavida-say-hi v1.0.1 MIT-0 2 files body ≈ 2 681 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

员工个人版公司情报查询工具。当员工输入公司名称时触发，直接调用公司 API 采集端点，由公司端 company-info-aggregator 内部决定走缓存还是搜索，员工端无需关心中间状态。适用于 B2B 销售人员的日常客户查询场景。

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationData and analyticstype 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
B
81/100
safety, quality, tests
Safety 60%
90
Quality 40%
68
Run on models
none yet
Process rating
D
49/100
Unfinished process
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

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

Exfiltration 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 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. 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 Exfiltration net-credential-use SKILL.md:91
    Credential used in a network call (verify the destination is the intended service)
    response=$(curl -s -X POST "$AUTO_TOKEN_URL" \
  • medium Exfiltration net-credential-use SKILL.md:143
    Credential used in a network call (verify the destination is the intended service)
    response=$(curl -s -X POST "$AUTO_TOKEN_URL" \

Files scanned: 2. 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 "triggers"

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (company-info-personal) differs from the folder (yoooclaw-company-info-personal)
  • 100Tools and files. No external tools needed
  • 100Steps. 9 steps
  • 100Execution cost. Instruction body is 2681 tokens
  • 100Running it twice. No mutating operations
  • 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)
  • +3Description length 118: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (11 code blocks)

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

External checks

ClawHub: suspicious
This company lookup skill is mostly purpose-aligned, but it quietly stores and refreshes employee access tokens and uses broad triggers that could send company queries to a remote service unexpectedly.
LLM: suspicious (high) · VirusTotal: · 8 Jun 2026