BD hf-daily-deep-researcher
HuggingFace Daily Papers + arXiv 多Agent深度研究系统。 采用编排器+专业Agent架构，支持两种模式： 1. 轻量扫描模式：周期性追踪（周/月），发现新论文 2. 深度调研模式：全时间范围调研，产出全面深入的研究报告 支持动态配置、自适应关键词、周期版本控制、跨平台搜索工具适配。
HuggingFace Daily Papers + arXiv 多Agent深度研究系统。 采用编排器+专业Agent架构，支持两种模式： 1.
As a process D 47/100 · 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: hf-daily-deep-researcher (ClawHub)
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 1. 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") - warning
body-longSKILL.md body ≈ 7826 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 47/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 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
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70Execution cost. Instruction body is 7826 tokens
- 100Steps. 146 steps
- 100Consistency. Name and required fields are in place
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
- -215 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 159: enough signal without eating the budget
- +4Structure: 41 headings
- +3Step-by-step instructions: 146 items
- +4Has examples (29 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.
External checks
ClawHub: suspicious
This research-report skill is not clearly malicious, but it needs Review because it can import workspace profile or memory data into persistent configuration and encourages automatic Feishu upload of generated reports.
LLM: suspicious (medium) · VirusTotal: · 26 Aug 2026