BD zeelin-deep-research
调用Zeelin Deep Research API进行深度研究任务。完全异步处理：提交任务后立即返回，后台进程自动确认大纲并定时检查任务状态，任务完成后自动保存md文件。自动配置定时通知（每2分钟检查），任务完成后主动通知用户。使用前必须先询问用户思考模式和搜索范围。
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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
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low Dangerous commands
cmd-pipe-to-shellreports/zeelin_openclaw实际的需求有哪些_20260304_140400.md:198Downloads and executes remote code from an unrecognised host (pipe to shell) (detector / deny-list definition; security demo / example)已披露的CVE-2026-25253漏洞可致远程代码执行，CVSS评分9.8[^30]。在macOS 13环境安装“DevSkill”后，OpenClaw读取~/.ssh/config与~/.aws/credentials，根据用户一句指令自动生成可执行脚本，并调用/usr/bin/code打开VS Code调试；脚本内直接嵌入AWS Access Key ID与Secret，且被赋予0755权限
detectordemo
Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
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 (zeelin-deep-research) differs from the folder (desearch-skill)
- 100Tools and files. No external tools needed
- 100Steps. 11 steps
- 100Execution cost. Instruction body is 320 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -42 reference files, but SKILL.md never points to them: the model will not open them
- -33 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 135: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 11 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.
External checks
ClawHub: suspicious
This research skill mostly does what it says, but it needs Review because it creates recurring background jobs and sends completion details to a fixed DingTalk recipient without user-scoped control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026