Skill: aipoch-ai/discussion-section-architect (version 0.1.1)

--- name: discussion-section-architect description: Structures and writes discussion sections for academic papers and research reports. Use when writing a discussion section, interpreting research results, connecting findings to existing literature, addressing study limitations, synthesizing conclusions, or drafting any part of an academic discussion. Helps researchers organize arguments, contextualize data, and produce clear, publication-ready discussion prose. allowed-tools: "Read Write Bash Edit" license: MIT metadata: skill-author: AIPOCH version: "1.0" --- # Discussion Section Architect ## Quick Start 1. Provide your **research question**, **key results**, and any **prior literature** you want to reference. 2. Choose a structure (see workflows below). 3. Generate a draft discussion section with clearly organized subsections. 4. Run the **Draft → Revise loop** (see below). --- ## Core Capabilities ### 1. Interpret and Contextualize Results - State whether results support or contradict the original hypothesis. - Explain unexpected findings with reasoned interpretations. - Quantify effect sizes or patterns when relevant. **Example prompt input:** ``` Results: Group A showed a 23% reduction in symptom severity (p=0.003) vs. control. Hypothesis: Intervention would reduce symptom severity. Task: Interpret this result for the discussion section. ``` **Example output excerpt:** ``` The 23% reduction in symptom severity (p=0.003) supports the primary hypothesis. This effect size is clinically meaningful and consistent with the mechanistic rationale proposed in the introduction... ``` --- ### 2. Connect Findings to Existing Literature - Identify studies that corroborate the findings. - Highlight where results diverge from prior literature and offer explanations. - Use hedged academic language appropriate to the field. **Example:** ``` Finding: Effect was stronger in older participants. Literature: Smith et al. (2019) found age-moderated responses in a similar cohort. Task: Connect finding to literature. ``` **Output:** ``` The age-moderated effect aligns with Smith et al. (2019), who reported attenuated responses in younger adults. One possible explanation is differential receptor sensitivity across age groups, as suggested by... ``` --- ### 3. Address Limitations Draft a limitations subsection that is honest but does not undermine the contribution: ``` Limitation: [Describe constraint] Impact: [How it affects interpretation] Mitigation / Future direction: [How it could be addressed] ``` --- ### 4. Synthesize Conclusions Generate a closing paragraph that: - Restates the core finding in plain language. - States the theoretical or practical contribution. - Ends with a forward-looking statement about implications or next steps. --- ## Recommended Discussion Structure ``` 1. Opening: Restate the research question and summarize the key finding (2–3 sentences). 2. Interpretation: Explain what the results mean mechanistically or theoretically. 3. Comparison to Literature: Agree/contrast with prior studies; explain divergences. 4. Implications: Theoretical contributions and/or practical applications. 5. Limitations: Honest scope boundaries with future directions. 6. Conclusion: Synthesis and forward-looking close. ``` --- ## Draft → Revise Loop Use this iterative workflow after generating an initial draft: **Step 1 — Draft**: Generate the full discussion section using the structure above. **Step 2 — Check**: Review against the checklist: - [ ] Each finding from the Results section is explicitly addressed. - [ ] Claims are supported by citations or logical reasoning — not stated as facts. - [ ] Unexpected or null results are acknowledged and interpreted. - [ ] Limitations are stated without dismissing the study's contribution. - [ ] No new data or results are introduced in the discussion. - [ ] Hedged language used appropriately (e.g., "suggests," "indicates," "may reflect"). - [ ] Conclusion ties back to the original research question. **Step 3 — Revise**: For each failed checklist item, revise only the affected paragraph(s). **Step 4 — Re-check**: Re-run the checklist on revised paragraphs to confirm resolution before finalizing. --- ## References - `references/guide.md` - Detailed documentation - `references/examples/` - Sample inputs and outputs --- **Skill ID**: 950 | **Version**: 1.0 | **License**: MIT

path: scripts/main.py
content: #!/usr/bin/env python3
"""
Discussion Section Architect
Guided framework for structuring academic Discussion sections.
"""

import argparse


class DiscussionArchitect:
    """Guide writing of academic Discussion sections."""
    
    STRUCTURE_TEMPLATE = {
        "opening": {
            "title": "Summary of Key Findings",
            "prompts": [
                "Restate your main findings in 2-3 sentences",
                "Connect findings to your original research question",
                "Highlight the most significant result"
            ]
        },
        "literature": {
            "title": "Comparison with Existing Literature",
            "prompts": [
                "How do your findings compare to previous studies?",
                "Do your results confirm or contradict prior work?",
                "What new insights do you provide?"
            ]
        },
        "mechanism": {
            "title": "Mechanistic Interpretation",
            "prompts": [
                "What biological/physical mechanisms explain your findings?",
                "Provide rationale for observed effects",
                "Connect to underlying theory"
            ]
        },
        "limitations": {
            "title": "Study Limitations",
            "prompts": [
                "What are the main limitations of your study?",
                "How might these limitations affect interpretation?",
                "Be honest but don't undermine your work"
            ]
        },
        "implications": {
            "title": "Clinical/Research Implications",
            "prompts": [
                "What are the practical applications of your findings?",
                "How might this change clinical practice?",
                "What is the significance for the field?"
            ]
        },
        "future": {
            "title": "Future Directions",
            "prompts": [
                "What questions remain unanswered?",
                "What are the logical next steps?",
                "Suggest specific follow-up studies"
            ]
        }
    }
    
    def generate_outline(self, findings, include_sections=None):
        """Generate Discussion section outline."""
        if include_sections is None:
            include_sections = list(self.STRUCTURE_TEMPLATE.keys())
        
        outline = []
        outline.append("DISCUSSION SECTION OUTLINE")
        outline.append("="*70)
        outline.append("")
        
        for section_key in include_sections:
            if section_key in self.STRUCTURE_TEMPLATE:
                section = self.STRUCTURE_TEMPLATE[section_key]
                outline.append(f"\n{section['title'].upper()}")
                outline.append("-"*50)
                
                for prompt in section['prompts']:
                    outline.append(f"  • {prompt}")
                
                outline.append("")
                outline.append("  [Write your content here]")
                outline.append("")
        
        return "\n".join(outline)
    
    def generate_transitions(self):
        """Provide transition phrase suggestions."""
        transitions = {
            "summary_to_literature": [
                "These findings are consistent with...",
                "Our results align with previous studies showing...",
                "In contrast to earlier work..."
            ],
            "literature_to_mechanism": [
                "The mechanism underlying this observation may be...",
                "These effects can be explained by...",
                "One possible explanation is..."
            ],
            "mechanism_to_limitations": [
                "Despite these insights, several limitations should be noted...",
                "However, this interpretation is subject to certain constraints...",
                "It is important to acknowledge..."
            ],
            "limitations_to_implications": [
                "Nevertheless, our findings have important implications...",
                "Despite these limitations, this work demonstrates...",
                "These results suggest that..."
            ]
        }
        return transitions


def main():
    parser = argparse.ArgumentParser(description="Discussion Section Architect")
    parser.add_argument("--findings", "-f", help="Key findings to discuss")
    parser.add_argument("--sections", "-s", help="Sections to include (comma-separated)")
    parser.add_argument("--output", "-o", help="Output file")
    parser.add_argument("--transitions", "-t", action="store_true",
                       help="Show transition phrases")
    
    args = parser.parse_args()
    
    architect = DiscussionArchitect()
    
    if args.transitions:
        print("\n" + "="*70)
        print("SUGGESTED TRANSITION PHRASES")
        print("="*70)
        transitions = architect.generate_transitions()
        for section, phrases in transitions.items():
            print(f"\n{section.replace('_', ' ').title()}:")
            for phrase in phrases:
                print(f"  • {phrase}")
        print("="*70 + "\n")
        return
    
    sections = None
    if args.sections:
        sections = [s.strip() for s in args.sections.split(",")]
    
    outline = architect.generate_outline(args.findings, sections)
    
    print("\n" + outline)
    
    if args.output:
        with open(args.output, 'w') as f:
            f.write(outline)
        print(f"\nOutline saved to: {args.output}")


if __name__ == "__main__":
    main()


path: tile.json
content: {
  "name": "aipoch/discussion-section-architect",
  "version": "0.1.0",
  "private": true,
  "summary": "Use when working with discussion section architect",
  "skills": {
    "discussion-section-architect": {
      "path": "SKILL.md"
    }
  }
}

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