SKILLEMALL.ai
GitHub ↗ RU Sign in

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。

ClawHub Agent Skills author: tomFoxxxx v4.1.4 MIT-0 31 files body ≈ 4 826 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
D
45/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: ClawHub, ClawHub

How to improve

  1. 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: 31. 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")

Process rating: all ten parameters 45/100

  • 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
  • 40Consistency. Frontmatter name (hf-daily-deep-researcher) differs from the folder (hf-daily-researcher-v2)
  • 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 4826 tokens
  • 100Steps. 76 steps
  • 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 159: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 76 items
  • +4Has examples (17 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This paper-research skill is not malicious, but it needs review because it can read local profile and memory files to personalize research and has an external Feishu report path.
LLM: suspicious (high) · 8 Jul 2026