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marjoriebroad

Academic Deep Research

作者 MarjorieBroad · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
65
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当前安装
1
版本数
在 OpenClaw 中安装
/install marjorie-academic-deep-research
功能描述
Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per t...
安全使用建议
This skill is an instruction-only 'deep research' workflow and otherwise low-footprint, but it has two issues you should consider: (1) It mandates revealing detailed internal analysis and chain-of-thought after every tool call — this can surface system prompts, private memory contents, or other sensitive details. If you care about privacy or not exposing internal reasoning, ask the author to remove or limit 'show your work' requirements and to redact internal deliberations. (2) The README's claim 'Works offline' conflicts with the explicit use of web_search/web_fetch; confirm whether the platform's search/fetch tools access the public web (and whether that is acceptable for your data). Practical next steps: (a) Ask the skill author to (i) make 'show your work' optional or produce redacted summaries rather than raw chain-of-thought and (ii) clarify the offline vs. web-fetch behavior; (b) If you approve the skill, deny or control memory access (so it can't read stored personal/PHI data) and require the Phase 1/2 stop points to be manual approvals (do not allow fully autonomous execution); (c) Test on a non-sensitive topic to observe what the agent outputs before using it with confidential topics. If you need help drafting a safer SKILL.md variant (e.g., explicit redaction rules, no chain-of-thought output, only cite sources and short rationale), I can propose edits.
功能分析
Type: OpenClaw Skill Name: marjorie-academic-deep-research Version: 1.0.0 The 'academic-deep-research' skill bundle is a highly structured and transparent framework for conducting multi-cycle research. It utilizes standard OpenClaw tools like web_search, web_fetch, and sessions_spawn for their intended purposes, incorporating mandatory user checkpoints and strict academic standards (APA citations, evidence hierarchy). No evidence of malicious intent, data exfiltration, or unauthorized execution was found across SKILL.md, README.md, or the supporting documentation.
能力评估
Purpose & Capability
The name/description match the instructions: this is an instruction-only research workflow that uses platform tools (web_search, web_fetch, sessions_spawn). However, README claims 'Works offline' and 'No external dependencies' while the SKILL.md explicitly instructs repeated web_search/web_fetch calls; that is inconsistent and should be clarified.
Instruction Scope
The SKILL.md requires the agent to 'show your work' after EACH tool call and to 'document the thinking process explicitly' (connect findings to prior results, show how understanding evolved). That mandates outputting detailed chain-of-thought/analysis which can leak internal deliberations, private memory contents (the skill also references memory_search/memory_get), or sensitive context. It also requires aggressive web fetching (count=20) and multi-cycle probing; while relevant to deep research, the mandatory disclosure of internal reasoning and the automatic repeated web fetches broaden the data the agent will surface and transmit.
Install Mechanism
No install spec and no code files — lowest-risk delivery mechanism. Nothing will be written to disk or downloaded as part of an installer.
Credentials
No environment variables, credentials, or config paths are requested. The skill does reference platform tools (memory_search/memory_get) but does not declare or require additional secrets; this is proportionate to a research workflow. Still, use of memory APIs means the agent could access stored user data if permitted by the platform.
Persistence & Privilege
always:false (no forced inclusion) and model invocation is allowed (default). The skill does not request elevated persistence or modify other skills. Autonomous invocation is allowed by default on the platform; that alone is not a new concern but combines with the instruction-level concerns above.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install marjorie-academic-deep-research
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /marjorie-academic-deep-research 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release
元数据
Slug marjorie-academic-deep-research
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Academic Deep Research 是什么?

Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per t... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 65 次。

如何安装 Academic Deep Research?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install marjorie-academic-deep-research」即可一键安装,无需额外配置。

Academic Deep Research 是免费的吗?

是的,Academic Deep Research 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Academic Deep Research 支持哪些平台?

Academic Deep Research 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Academic Deep Research?

由 MarjorieBroad(@marjoriebroad)开发并维护,当前版本 v1.0.0。

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