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Academic Deep Research
by
MarjorieBroad
· GitHub ↗
· v1.0.0
· MIT-0
57
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0
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1
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Install in OpenClaw
/install mar-academic-deep-research
Description
Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per t...
Usage Guidance
This skill appears to do what it says (multi-cycle web research) and is low-risk to install because it has no install steps or credential requests. Before enabling it, consider: (1) The author claims it "works offline" but the instructions call web_search/web_fetch — expect network activity and many outgoing requests. (2) The skill will read platform memory (memory_search/memory_get) if available — remove or audit sensitive memories you don't want included. (3) After you approve the research plan the skill will run fully and autonomously for that job (many requests and long outputs); if you need finer control, decline autonomous execution or require repeated approvals. (4) The skill mandates APA citations and 1–2 citations per paragraph — ensure the platform preserves verifiable source links because the skill can otherwise produce plausible but fabricated citations. If you rely on this for regulated or sensitive topics (healthcare, legal, proprietary IP), test with non-sensitive queries first and confirm how web_fetch results and memory content are logged, retained, and shared by the platform.
Capability Analysis
Type: OpenClaw Skill
Name: mar-academic-deep-research
Version: 1.0.0
The 'academic-deep-research' skill bundle is a highly structured prompt engineering template designed to guide an AI agent through a rigorous, multi-phase research process. It utilizes standard OpenClaw platform tools (web_search, web_fetch, sessions_spawn) for their intended purposes of data gathering and parallel processing. The instructions in SKILL.md emphasize transparency, academic rigor (APA citations), and user control through three mandatory checkpoints, which actually enhances safety rather than circumventing it. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the logic is entirely consistent with the stated goal of providing deep, reproducible research.
Capability Assessment
Purpose & Capability
The skill claims to be a self-contained, reproducible research assistant and its runtime instructions use only platform tools (web_search, web_fetch, sessions_spawn, memory_get). That aligns with the stated purpose. However, the README asserts 'Works offline — No third-party API keys required' which contradicts explicit use of web_search/web_fetch (network I/O). This is an internal inconsistency a user should be aware of.
Instruction Scope
SKILL.md stays focused on research: multi-cycle web searches, fetches, synthesis, and mandatory user approvals for planning. It does instruct use of memory_search/memory_get and to 'Check MEMORY.md for related context' — which means the agent will pull prior user memories or stored context. That could surface private or sensitive user data if present. The skill also mandates showing analysis after every tool call and performing two full cycles per theme, which will result in many external requests and lots of aggregated output; this is expected for the stated purpose but increases the potential for unintended disclosure or heavy resource use.
Install Mechanism
This is an instruction-only skill with no install spec, no added binaries, and no code files to execute on disk. From an install-mechanism perspective there is nothing being downloaded or installed, which minimizes supply-chain risk.
Credentials
The skill declares no required environment variables, no credentials, and no config paths. That is proportionate to its described web-research purpose. Note: it does request access to platform memory APIs (memory_search/memory_get) which is not an environment variable but is a data source; that access is reasonable for contextual research but can expose stored memories.
Persistence & Privilege
The skill is not marked always: true and uses the default autonomous-invocation capability. Autonomous invocation is platform-default and not flagged here by itself. Be aware Phase 3 (execution) is 'NO STOPS — EXECUTE FULLY' after the Phase 2 user approval, so once the plan is approved the skill will run many steps without further confirmation. That increases the blast radius if the research topic or memory context is sensitive.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install mar-academic-deep-research - After installation, invoke the skill by name or use
/mar-academic-deep-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of the Academic Deep Research skill for transparent, rigorous, and reproducible investigations.
- Implements mandated two-cycle research per theme, with explicit evidence trails and contradiction documentation.
- Structured workflow with three user checkpoints: clarifying questions, research plan approval, and final synthesis.
- Utilizes only SkillBoss platform-native tools (web_search, web_fetch, sessions_spawn) for all research tasks.
- Emphasizes APA 7th citation style, evidence hierarchy, and analysis methodology suitable for literature reviews and academic-quality reports.
- Integrated protocol for handling insufficient data, unresolved contradictions, and source quality concerns.
Metadata
Frequently Asked Questions
What is Academic Deep Research?
Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per t... It is an AI Agent Skill for Claude Code / OpenClaw, with 57 downloads so far.
How do I install Academic Deep Research?
Run "/install mar-academic-deep-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Academic Deep Research free?
Yes, Academic Deep Research is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Academic Deep Research support?
Academic Deep Research is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Academic Deep Research?
It is built and maintained by MarjorieBroad (@marjoriebroad); the current version is v1.0.0.
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