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BC feishu-cli-chat

飞书会话浏览、消息互动与群聊管理。查看聊天记录、获取群聊历史消息、搜索群聊、 获取消息详情、Reaction 表情回应、Pin 置顶/取消置顶、删除消息、 群聊信息查询与管理（获取/更新/解散/成员管理）。 支持普通群和话题群两种模式，话题群自动获取线程回复。所有命令需要 User Token。 当用户请求"查看聊天记录"、"看和某人的消息"、"群聊历史"、"群消息"、"搜索群聊"、 "查群信息"、"群成员"、"最近消息"、"聊天记录"、"Reaction"、"表情回应"、 "置顶消息"、"Pin"、"删除消息"、"获取消息"、"消息详情"、 "和谁聊了什么"、"群里说了什么"、"总结群消息"、"话题回复"、"线程回复"、 "thread replies"时使用。 也适用于：用户给出一个群聊名称或 chat_id 并希望浏览其消息的场景， 即使没有明确说"聊天记录"。当用户想了解某个群最近在讨论什么、 想找和某人的对话内容、或想对消息进行互动操作时，都应使用此技能。

ClawHub Agent Skills author: GISwilson v1.0.0 MIT-0 2 files body ≈ 2 578 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructuretype 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
88/100
safety, quality, tests
Safety 60%
95
Quality 40%
77
Run on models
none yet
Process rating
C
51/100
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

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

Broad scope 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 skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash Read Write

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")

Process rating: all ten parameters 51/100

  • 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. 14 mutating operations with no state check
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2578 tokens
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 439: enough signal without eating the budget
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (26 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
This appears to be a legitimate Feishu chat-management helper, but it gives an agent broad user-token access to private chats and group-management actions without enough scoping or safeguards.
LLM: suspicious (high) · VirusTotal: · 29 May 2026