Source: https://github.com/aipoch/medical-research-skills

Discussion Section Architect

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

• Use this skill when the task needs 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.
• Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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).

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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...

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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...

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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]

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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.

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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.

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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.

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References

• references/guide.md - Detailed documentation
• references/examples/ - Sample inputs and outputs

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Skill ID: 950 | Version: 1.0 | License: MIT

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of discussion-section-architect and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

discussion-section-architect only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

• references/audit-reference.md - Supported scope, audit commands, and fallback boundaries

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

When Not to Use

• Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
• Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
• Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Required Inputs

Field	Required	Format/Source	Example	If Missing
User task description	Yes	Text	Research question, writing goal, analysis objective	Stop and ask user to provide
Primary input material	Depends on task	Text, file path, ID, table, or literature	PMID, PDF, CSV, DOCX, keywords, etc.	Specify which material type is missing
Output preference	No	Text	Language, format, target journal, template	Use skill default format

Output Contract

• Primary output: Structured result or target file aligned with this skill's objective.
• Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
• Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
• If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

Failure Handling

• Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
• Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
• Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

User Checkpoints

• Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
• Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.

Quick Validation

• Check that key scripts, templates, or reference file paths this skill depends on exist.
• Check that the final output contains the core fields, sections, or files specified for this task.
• Check that results clearly mark assumptions, limitations, and incomplete items.