skills/43-wentorai-research-plugins/skills/writing/composition/discussion-writing-guide/SKILL.md
Write effective discussion sections that interpret results and impact
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research discussion-writing-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
A skill for writing compelling discussion sections in academic papers. Covers the standard structure, strategies for interpreting results, addressing limitations, connecting to existing literature, and articulating the broader implications of your findings.
The discussion mirrors the introduction in reverse -- it starts narrow (your specific findings) and broadens to implications:
Introduction: Broad context -> Specific gap -> Your question
Discussion: Your findings -> Broader literature -> Implications
Standard Structure:
1. Summary of key findings (1-2 paragraphs)
2. Interpretation and comparison with literature (bulk of section)
3. Limitations (1-2 paragraphs)
4. Implications and future directions (1-2 paragraphs)
5. Conclusion (optional, or as separate section)
def outline_discussion(findings: list[dict],
literature_connections: list[dict],
limitations: list[str],
implications: list[str]) -> dict:
"""
Generate a structured discussion outline.
Args:
findings: List of dicts with 'result' and 'interpretation'
literature_connections: Dicts with 'finding', 'related_work', 'comparison'
limitations: List of limitation statements
implications: List of implication statements
"""
outline = {
"opening_paragraph": {
"purpose": "Restate the research question and summarize key findings",
"template": (
"This study examined [research question]. "
"The principal finding was [main result], "
"which [supports/contradicts/extends] [hypothesis or expectation]."
),
"tips": [
"Do NOT repeat numbers from Results -- summarize in words",
"State whether hypotheses were supported",
"Lead with the most important finding"
]
},
"interpretation_paragraphs": [
{
"finding": f["result"],
"interpretation": f["interpretation"],
"literature": next(
(lc for lc in literature_connections
if lc["finding"] == f["result"]), None
)
}
for f in findings
],
"limitations": {
"items": limitations,
"tip": "Frame limitations honestly but not apologetically"
},
"implications": {
"items": implications,
"tip": "Distinguish practical implications from theoretical ones"
}
}
return outline
Each major finding should be discussed in relation to prior work:
Pattern 1 - Consistent with prior work:
"Our finding that X is associated with Y is consistent with
Smith et al. (2022), who reported a similar relationship in
[different context]. This convergence across [populations/methods]
strengthens the evidence that [mechanism/explanation]."
Pattern 2 - Contradicts prior work:
"In contrast to Jones et al. (2021), who found no effect of X
on Y, our results suggest a significant positive relationship.
This discrepancy may be explained by [methodological differences,
population differences, measurement differences]."
Pattern 3 - Extends prior work:
"While previous studies have established that X affects Y,
our results extend this finding by showing that this effect
is moderated by Z, suggesting [new insight]."
Pattern 4 - Novel finding:
"To our knowledge, this is the first study to demonstrate [finding].
One possible explanation is [mechanism]. However, this interpretation
should be treated with caution until [replication/additional evidence]."
DO NOT:
- Simply restate results with numbers (that is the Results section)
- Introduce new results not presented in the Results section
- Overclaim: "This proves that..." (use "suggests," "indicates")
- Ignore findings that contradict your hypothesis
- Speculate without clearly labeling it as speculation
DO:
- Interpret what the results MEAN, not just what they ARE
- Address unexpected or negative findings
- Explain WHY your results may differ from others
- Connect findings to theory or conceptual frameworks
- Use hedging language appropriately (may, might, suggests, appears)
Weak framing:
"A limitation of this study is that the sample size was small."
Better framing:
"The sample size (N=45) may have limited statistical power to
detect small effects. However, the effect sizes observed for
our primary outcomes were medium to large (Cohen's d = 0.6-0.8),
suggesting that the main findings are robust. Future studies
with larger samples could examine whether the non-significant
trends observed for secondary outcomes reach significance."
Structure for each limitation:
1. State the limitation clearly
2. Explain its potential impact on the findings
3. Note any mitigating factors
4. Suggest how future work could address it
| Category | Examples | |----------|---------| | Design | Cross-sectional (cannot infer causation), no control group | | Sample | Small N, non-representative, convenience sampling | | Measurement | Self-report bias, single-item measures, validity concerns | | Analysis | Multiple comparisons, missing data, model assumptions | | Generalizability | Single site, specific population, cultural context |
Theoretical implications:
"These findings support the [theory name] by demonstrating that
[specific contribution to theory]."
Practical implications:
"These results suggest that [practitioners/policymakers] should
consider [actionable recommendation] when [context]."
Methodological implications:
"Our comparison of [methods] suggests that [method recommendation]
is preferable when [condition], which may inform future study design."
Future research directions:
"Three avenues for future research emerge from these findings:
(1) [replication in different context], (2) [testing the proposed
mechanism], and (3) [extending to related outcomes]."
The final paragraph should leave readers with a clear takeaway. State the single most important finding and its significance in 2-3 sentences. Avoid introducing new information or hedging excessively in the conclusion. End on a forward-looking note that motivates continued research.
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.