Research Skillbox
A modular router for 14 research skills covering academic writing, grant proposals, and critical paper reading.
Free
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Example session with this skill installed
I have 5 papers on reinforcement learning for grid stability. I need to triage them, extract the core methodologies into a matrix, and draft a short literature review section for my thesis.
- Read your context and instructions
- Compiled the research-skillbox
I will process your reinforcement learning papers by routing through the critical reading and paper writing modules. First, I will perform a triage and extraction to build your literature matrix, followed by the draft construction.
Literature Synthesis & Research Matrix
| Paper Reference | Core Methodology | Key Contribution | Limitations |
|---|---|---|---|
| Zhang et al. (2023) | Multi-Agent PPO | Decentralized voltage control | High communication overhead |
| Li & Wang (2022) | Deep Q-Learning | Frequency restoration | Only tested on IEEE 14-bus |
| Chen (2024) | Soft Actor-Critic | Real-time dispatch | Assumes perfect state sensing |
| Miller (2021) | DDPG | Reactive power compensation | Slow convergence in large grids |
| Gupta et al. (2023) | Federated RL | Privacy-preserving stability | Performance gap vs centralized |
Literature Review Draft: Grid Stability via RL
The application of Reinforcement Learning (RL) to power grid stability has transitioned from centralized control to decentralized multi-agent architectures. Recent work by Zhang et al. (2023) demonstrates the efficacy of Proximal Policy Optimization (PPO) in managing voltage fluctuations, though it introduces significant communication latency. To address data privacy, Gupta et al. (2023) proposed federated learning frameworks, albeit with a slight degradation in global optimization compared to the DDPG approaches seen in earlier benchmarks (Miller, 2021). The current research gap remains the robust handling of imperfect state sensing, a challenge highlighted in recent Soft Actor-Critic implementations (Chen, 2024).
Next steps
- Review the "Limitations" column to identify which gap your thesis will address.
- Refine the draft by applying the
academic-deai-writingmodule to ensure technical tone. - Expand the matrix with your specific experimental results for the Results section.
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About this skill
Managing a complex academic project requires more than just a chat interface. It demands specialized workflows for literature reviews, grant proposals, and rigorous peer-review standards. This skill acts as a central router for 14 specialized research modules, ensuring your agent follows proven academic methodologies rather than improvising.
What it does
- Academic writing provides structured workflows for drafting theses, abstracts, and full paper sections.
- Literature synthesis handles multi-paper screening, critical reading, and the creation of literature matrices.
- Grant proposal engineering guides you through every section of a research proposal with structured templates.
- Human-parity editing identifies and removes AI writing traces to improve manuscript quality and lower AIGC scores.
- Methodological rigor supports mathematical proofs, algorithm experiment design, and theorem checking.
- Note-taking systems implements Zettelkasten and Socratic reading methods to turn books into permanent knowledge.
How it works
- Request a specific research task like writing a review or critiquing a paper.
- Routing occurs as the agent identifies the correct sub-skill from the internal registry.
- Execution follows the specific sub-skill workflow, including specialized reference files and controlled language rules.
- Chaining allows you to link multiple skills, such as moving from critical reading to note-taking to final writing.
Frameworks & tools
This skill integrates Zettelkasten note-taking, Socratic inquiry, and ASD-STE100 Simplified Technical English standards. It is optimized for LaTeX environments and academic publishing workflows.
Why this beats prompting it yourself
Generic prompts often lead to "AI-flavored" academic writing that fails peer review or detection tests. This skill uses a modular architecture that separates concerns, ensuring the agent uses high-precision rules for specific tasks like theorem verification or grant formatting without getting distracted by unrelated context.
Use cases
- Drafting a comprehensive literature review from a batch of PDF transcripts.
- Rewriting a manuscript to remove boilerplate and improve technical clarity.
- Designing an algorithm experiment and checking the mathematical derivation.
- Organizing graduate research notes into a publication-ready article.
Known limitations
Primary instructions and several modules are written in Chinese. While it supports English academic standards, users should expect Chinese-language internal logic and routing.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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