research-to-practice

name	research-to-practice
description	Use when applying academic research to practical workflows, optimizing existing processes based on papers, or extracting actionable insights from research
version	1
author	agent

Research to Practice

Overview

Bridge the gap between academic research and practical workflow improvements.

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When to Use

Use this skill when:

• You discover a relevant academic paper and want to apply its insights
• You need to optimize existing workflows based on research findings
• You want to systematically extract actionable ideas from research
• Current methods show limitations that research might address

Typical scenarios:

• Reading ML/NLP papers for agent system improvements
• Finding optimization techniques for knowledge management
• Applying human-computer interaction research to UI/UX workflows
• Leveraging cognitive science for better user interactions

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Prerequisites

• Access to paper (URL, PDF, or bibliographic information)
• Understanding of current workspace workflows
• Knowledge of which systems/components might benefit
• Optional: specific pain points or optimization targets in mind

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Workflow

Step 1: Paper Acquisition & Initial Assessment

Goal: Obtain and understand the paper's core contribution

Actions:

1. Fetch paper content via URL or search for it
2. Identify: Title, authors, venue, year
3. Extract abstract and key claims
4. Determine: Is this relevant to our workflows?

Decision Point:

• If paper is not accessible or not relevant → Stop and report
• If paper is accessible and relevant → Continue to Step 2

Output Format:

## Paper Overview
- **Title**: [paper title]
- **Authors**: [authors]
- **Venue**: [conference/journal]
- **Year**: [year]
- **Core Contribution**: [1-2 sentence summary]
- **Relevance Score**: [High/Medium/Low] - [reasoning]

Step 2: Deep Reading & Insight Extraction

Goal: Extract specific techniques, insights, and principles

Actions:

1. Read methodology section → What did they do?
2. Read results section → What did they achieve?
3. Identify novel techniques or approaches
4. Note any ablation studies (what matters most?)
5. Extract key equations, algorithms, or frameworks

Key Questions to Answer:

• What is the core innovation?
• What problem does it solve?
• How does it compare to existing methods?
• What are the limitations?

Output Format:

## Core Insights

### 1. [Insight Category Name]
**Technique/Principle**: [description]
**Key Mechanism**: [how it works]
**Advantage**: [why it's better]
**Limitations**: [constraints or trade-offs]

### 2. [Insight Category Name]
...

## Technical Details
- [Key algorithm/framework]
- [Important parameters or configurations]
- [Evaluation metrics used]

Step 3: Current Workflow Analysis

Goal: Map paper insights to existing workflows

Actions:

1. Review current relevant workflows/skills
2. Identify pain points or inefficiencies
3. Map paper techniques to specific components
4. Prioritize based on impact and feasibility

Mapping Framework:

Paper Insight → Current System → Potential Improvement

Output Format:

## Current State Analysis

### Relevant Workflows
1. [Workflow/Skill name]
   - Current approach: [description]
   - Limitations: [problems]
   - Relevant paper insights: [which insights apply]

2. [Workflow/Skill name]
   ...

### Mapping: Insights → Workflows
| Paper Insight | Current Workflow | Improvement Opportunity |
|--------------|------------------|------------------------|
| [insight 1] | [workflow A] | [specific improvement] |
| [insight 2] | [workflow B] | [specific improvement] |

Step 4: Optimization Proposal Generation

Goal: Generate specific, actionable optimization proposals

Actions:

1. For each insight-workflow mapping:
   • Design concrete changes
   • Estimate impact (High/Medium/Low)
   • Estimate effort (High/Medium/Low)
   • Identify dependencies
2. Group related proposals
3. Prioritize by impact/effort ratio

Output Format:

## Optimization Proposals

### Proposal 1: [Name]
**Target**: [which workflow/component]
**Based on**: [which paper insight]
**Description**: [what to change]
**Implementation Steps**:
1. [step 1]
2. [step 2]
...

**Expected Benefits**:
- [benefit 1]
- [benefit 2]

**Impact**: [High/Medium/Low]
**Effort**: [High/Medium/Low]
**Dependencies**: [what's needed first]

### Proposal 2: [Name]
...

## Prioritization Matrix
| Proposal | Impact | Effort | Priority |
|----------|--------|--------|----------|
| [P1] | High | Low | ⭐⭐⭐ |
| [P2] | High | Medium | ⭐⭐⭐ |
| [P3] | Medium | Low | ⭐⭐ |

Step 5: Implementation Planning

Goal: Create actionable implementation plans for top proposals

Actions:

1. Select top 2-3 proposals
2. For each, create detailed implementation plan
3. Define success metrics
4. Identify risks and mitigation strategies

Output Format:

## Implementation Plans

### Plan 1: [Proposal Name]
**Goal**: [clear objective]

**Steps**:
1. [detailed step]
2. [detailed step]
...

**Files to Modify**:
- [file 1] - [changes]
- [file 2] - [changes]

**Success Metrics**:
- [metric 1]: [how to measure]
- [metric 2]: [how to measure]

**Risks & Mitigation**:
- Risk: [description] → Mitigation: [solution]

**Estimated Time**: [X hours/days]

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### Plan 2: [Proposal Name]
...

## Recommended Execution Order
1. [Plan X] - [reasoning]
2. [Plan Y] - [reasoning]

Step 6: Validation & Documentation

Goal: Validate proposals and document for future reference

Actions:

1. Review proposals against original paper claims
2. Check for misinterpretations
3. Document the entire analysis in workspace
4. Create summary for knowledge base

Output Format:

## Validation Checklist
- [ ] Proposals align with paper's core contribution
- [ ] Technical details correctly understood
- [ ] Limitations acknowledged in proposals
- [ ] Implementation plans are feasible
- [ ] Success metrics are measurable

## Knowledge Base Entry
**Paper**: [title]
**Applied to**: [workflows]
**Key Improvements**: [summary]
**Status**: [Proposed/In Progress/Implemented]
**Results**: [to be filled after implementation]

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Best Practices

Do's

✅ Verify paper accessibility first - Don't proceed if you can't read the paper ✅ Focus on transferable insights - Not all research applies to practical workflows ✅ Consider constraints - Academic methods may have assumptions that don't hold in practice ✅ Start small - Implement one insight before moving to the next ✅ Document everything - Research insights are valuable institutional knowledge ✅ Validate assumptions - What works in the paper's context may not work in yours

Don'ts

❌ Don't over-engineer - Simple solutions are often better than complex research methods ❌ Don't ignore limitations - Every paper has constraints; acknowledge them ❌ Don't apply blindly - Adapt techniques to your specific context ❌ Don't skip the mapping step - Understanding current state is crucial ❌ Don't promise unrealistic gains - Be honest about expected improvements

Quality Checks

Before finalizing proposals, verify:

1. Correctness: Do I understand the paper correctly?
2. Relevance: Does this actually address a real problem?
3. Feasibility: Can this be implemented with available resources?
4. Measurability: Can we tell if it worked?

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Common Issues

Issue 1: Paper Not Accessible

Symptom: Cannot fetch PDF or paper is behind paywall

Solutions:

• Search for arXiv preprint version
• Look for author's personal webpage
• Check if paper is cited in accessible sources
• Use abstract + citations to infer content

Fallback:

⚠️ Paper not directly accessible
Alternative approaches:
1. Search for: [title] site:arxiv.org
2. Check author pages: [author homepages]
3. Use secondary sources: blog posts, talks, reviews

Issue 2: Paper Too Theoretical

Symptom: Techniques are too abstract to apply directly

Solutions:

• Look for implementation details or pseudocode
• Find applied papers that cite this work
• Break down into simpler components
• Focus on the core insight rather than full method

Issue 3: Unclear Relevance

Symptom: Not sure if paper applies to current workflows

Solutions:

• List current workflow pain points
• Check if paper addresses similar problems
• Look for indirect applications (e.g., evaluation methods)
• Discuss with user to clarify priorities

Issue 4: Overlapping Insights

Symptom: Multiple papers suggest similar improvements

Solutions:

• Compare approaches and choose best fit
• Consider combining complementary insights
• Prioritize based on implementation effort
• Document the relationship between papers

Issue 5: Implementation Too Complex

Symptom: Paper's method requires significant infrastructure

Solutions:

• Simplify: Use core insight with simpler implementation
• Phase: Break into incremental improvements
• Alternative: Find simpler papers with similar insights
• Hybrid: Combine with existing proven methods

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Example: Hierarchical Attention Networks → Workflow Optimization

Paper Summary

Hierarchical Attention Networks for Document Classification (Yang et al., NAACL 2016)

Core Insight: Documents have natural hierarchy (words → sentences → document), and attention mechanisms at each level improve classification by focusing on important parts.

Current Workflows Analyzed

• knowledge-base-cache: 3-tier cache system
• memory: Daily log and long-term memory
• code-analysis: Code understanding workflow

Optimization Proposals

Proposal 1: Attention-Based Knowledge Retrieval

Target: knowledge-base-cache Insight: Hierarchical attention for information retrieval Description: Add attention weights to cache layers based on query relevance Impact: High | Effort: Medium

Proposal 2: Hierarchical Memory Filtering

Target: memory system Insight: Word-level + sentence-level + document-level attention Description: Filter memories at multiple granularities Impact: High | Effort: Medium

Implementation Plan (Selected)

## Plan: Attention-Based Knowledge Retrieval

**Goal**: Improve knowledge retrieval relevance using attention weights

**Steps**:
1. Add embedding-based similarity scoring to WorkingMemoryManager
2. Implement attention weight calculation for cache layers
3. Modify retrieval to use weighted assembly
4. Test with historical queries

**Files**:
- `repository/core/working_memory.py` - Add attention scoring
- `repository/adapters/hot_cache_adapter.py` - Weighted retrieval

**Success Metrics**:
- Relevance score: User satisfaction with retrieved context
- Token efficiency: Reduction in irrelevant context

**Time Estimate**: 4-6 hours

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See Also

• knowledge-base-cache - Knowledge management system
• code-analysis - Structured code understanding
• mvp-design - Design implementation plans
• daily-log - Record research application outcomes

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Version History

• v1.0 (2026-02-12) - Initial release
  • 6-step workflow from paper to practice
  • Mapping framework for insights → workflows
  • Prioritization matrix
  • Common issues and solutions
  • Complete example with HAN paper