SKILLEMALL.ai

AF qianfan-deepresearch

调用百度千帆 DeepResearch Agent API 自动生成深度研究报告(输出 Markdown 与 HTML 两种格式),一键完成「创建会话 → 发起研究 → 自动跳过澄清 → 自动 确认大纲 → 获取报告下载链接」完整流程。支持三种深度模式:lite(轻量版, 速度快,默认)/ standard(标准版,深度均衡)/ pro(高级版,最详尽,适合 长报告深度研究)。适用于行业研究、市场调研、竞品分析、技术综述、政策解 读、决策论证等需要结构化长报告的场景;当用户提及"轻量版/标准版/高级版/ lite/standard/pro"或"深入研究/详尽报告/快速调研"等深度偏好时,应优先 使用本技能。

ClawHub Agent Skills author: ide-rea v1.1.4 MIT-0 5 files body ≈ 2 138 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process F 31/100 · Will not run — References files that are not bundled: URL

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: URL
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 5. 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")
  • warning missing-ref reference to a missing file: URL

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: URL
  • 0Tools and files. 1 referenced file(s) missing: URL
  • 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 (qianfan-deepresearch) differs from the folder (deepresearch-conversation)
  • 100Steps. 28 steps
  • 100Execution cost. Instruction body is 2138 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -225 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 307: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This appears to be a legitimate Baidu Qianfan report generator, but it needs Review because it sends research content to a third-party API, encourages command-line API keys, and automatically approves remote workflow steps.
LLM: suspicious (high) · VirusTotal: benign · 10 Sept 2026