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
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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".
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
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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-usescripts/research.sh:74Credential used in a network call (verify the destination is the intended service)STATUS_RESP=$(curl -s -X GET "${BASE_URL}/api/conversation/status?sessionId=${SESSION_ID}" -H "x-api-key: ${API_KEY}") -
medium Exfiltration
net-credential-usescripts/research.sh:102Credential used in a network call (verify the destination is the intended service)HISTORY=$(curl -s -X GET "${BASE_URL}/api/conversation/history?sessionId=${SESSION_ID}&pageSize=10&pageNo=1" -H "x-api-key: ${API_KEY}")
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 (kelcey-zeelin-deep-research)
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 53 steps
- 100Execution cost. Instruction body is 3571 tokens
- low 15 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: 35 headings
- +3Step-by-step instructions: 53 items
- +4Has examples (24 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.