BD ZeeLin Deep Research 深度研究
ZeeLin Deep Research 深度研究是一款 AI 驱动的专业研究辅助平台，支持一句话生成与多步骤生成，提供深度、专家两大研究路径。从快速信息梳理、系统分析到超万字专家报告全流程覆盖，依托多轮推理与多源数据整合，高效完成企业分析、市场洞察、招商研究等复杂任务，一站式提升研究效率与决策质量。
As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
This is a copy of a skill from another catalog; the rating counts the canonical one: ZeeLin Deep Research 深度研究 (ClawHub)
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 · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription 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. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (ZeeLin Deep Research 深度研究) differs from the folder (desearch)
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 50 steps
- 100Execution cost. Instruction body is 3449 tokens
- low 13 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)
- +3Output format is not stated: the model decides each time
- -216 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 151: enough signal without eating the budget
- +4Structure: 33 headings
- +3Step-by-step instructions: 50 items
- +4Has examples (23 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.
External checks
ClawHub: clean
This is a disclosed ZeeLin research API connector that sends prompts to ZeeLin, retrieves generated reports, and delivers them back to the user.
LLM: benign (high) · VirusTotal: · 29 May 2026