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BD fangcloud_ai

亿方云 (Fangcloud) AI 能力集成 Skill。支持文件管理（列表、上传、下载、分享）、协作邀请、知识库对话 (DeepSeek) 以及智能体交互。当用户需要操作亿方云文件、查询最近文档或创建分享链接时，使用此 Skill。

ClawHub Agent Skills author: jiema v1.0.1 MIT-0 7 files · 3 scripts body ≈ 2 121 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
85/100
safety, quality, tests
Safety 60%
99
Quality 40%
64
Run on models
none yet
Process rating
D
39/100
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

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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Obfuscation obf-hex-escape-chain references/openapi.md:1825
    Escaped/char-code string obfuscation (quoted — discussed, not commanded)
    --header 'x-file-name: 1758513460275_%E3%80%8A%E6%99%BA%E8%83%BD%E4%BD%93%E8%9C%82%E7%BE%A4%E5%AE%9E%E6%88%98%E5%9F%B9%E8%AE%AD%E3%80%8B%E7%90%86%E8%AE%BA%E9%83%A8%E5%88%86%E8%B5%84%E6%96%99.pdf' \
    quoted

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 39/100

  • 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
  • 30Running it twice. 9 mutating operations with no state check
  • 40Consistency. Frontmatter name (fangcloud_ai) differs from the folder (fangcloudai)
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 100Steps. 131 steps
  • 100Execution cost. Instruction body is 2121 tokens
  • low The response is described with custom markup (5 tags): a typed call is more reliable

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 131 items
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
This Fangcloud skill is not clearly malicious, but it needs Review because it combines powerful cloud/admin access, live-looking tokens in documentation, and unverified remote executable downloads.
LLM: suspicious (high) · VirusTotal: · 29 May 2026