skills/43-wentorai-research-plugins/skills/writing/composition/abstract-writing-guide/SKILL.md
Craft structured research abstracts that maximize clarity and journal acceptance
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research abstract-writing-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A skill for writing effective research abstracts that clearly communicate your study's purpose, methods, results, and significance. Covers structured and unstructured formats, word count optimization, and common pitfalls.
Most journals in science and medicine require a structured abstract:
BACKGROUND/OBJECTIVE: 1-2 sentences on the problem and study aim
METHODS: 2-3 sentences on study design, participants, and analysis
RESULTS: 2-4 sentences with key quantitative findings
CONCLUSION: 1-2 sentences on implications and significance
Total: typically 150-300 words (check journal requirements)
Common in social sciences and humanities:
Sentence 1: Context and problem statement
Sentence 2: Research gap or objective
Sentence 3: Methods overview
Sentence 4-5: Key findings
Sentence 6: Implications and significance
Total: typically 150-250 words
def generate_abstract_outline(study_info: dict) -> dict:
"""
Generate an abstract outline from study information.
Args:
study_info: Dict with keys: 'problem', 'gap', 'objective',
'design', 'sample', 'methods', 'key_findings',
'implications', 'word_limit'
Returns:
Structured abstract outline with estimated word counts
"""
word_limit = study_info.get('word_limit', 250)
# Allocate words proportionally
sections = {
'background': {
'allocation_pct': 0.15,
'content': [study_info['problem'], study_info['gap']],
'template': "Despite {problem}, {gap}. This study aimed to {objective}."
},
'methods': {
'allocation_pct': 0.25,
'content': [study_info['design'], study_info['sample'], study_info['methods']],
'template': "A {design} was conducted with {sample}. {methods}."
},
'results': {
'allocation_pct': 0.35,
'content': study_info['key_findings'],
'template': "Results showed {findings}."
},
'conclusion': {
'allocation_pct': 0.25,
'content': study_info['implications'],
'template': "These findings suggest {implications}."
}
}
for section in sections.values():
section['target_words'] = round(word_limit * section['allocation_pct'])
return {
'sections': sections,
'word_limit': word_limit,
'tips': [
'Write the Results section first (most important)',
'Use past tense for methods and results',
'Avoid abbreviations unless space-saving is critical',
'Include at least one quantitative result with effect size',
'Do not include citations in the abstract'
]
}
Background: Chronic low back pain affects 23% of the global adult population,
yet optimal exercise interventions remain unclear. This systematic review and
meta-analysis aimed to compare the effectiveness of yoga, Pilates, and
general exercise for reducing pain and disability in chronic low back pain.
Methods: We searched PubMed, CINAHL, Cochrane Library, and PEDro from
inception to December 2025. Randomized controlled trials comparing yoga,
Pilates, or general exercise to usual care were included. Pain (VAS/NRS)
and disability (ODI/RMDQ) were pooled using random-effects meta-analysis.
Results: Thirty-seven RCTs (N = 3,421) met inclusion criteria. All exercise
types significantly reduced pain compared to usual care: yoga (SMD = -0.73,
95% CI [-0.94, -0.52]), Pilates (SMD = -0.62, 95% CI [-0.81, -0.43]),
and general exercise (SMD = -0.48, 95% CI [-0.63, -0.33]). Yoga showed
significantly greater pain reduction than general exercise (p = 0.03)
but not Pilates (p = 0.28). Heterogeneity was moderate (I-squared = 48-61%).
Conclusion: Yoga, Pilates, and general exercise all effectively reduce
chronic low back pain, with yoga showing a modest advantage over general
exercise. Clinicians should consider patient preferences when recommending
exercise modalities.
def evaluate_title(title: str) -> dict:
"""Evaluate a paper title against best practices."""
checks = {}
# Length check
word_count = len(title.split())
checks['word_count'] = word_count
checks['length_ok'] = 10 <= word_count <= 20
# Key content checks
checks['has_colon'] = ':' in title # Structured titles often use colons
checks['starts_with_article'] = title.lower().startswith(('a ', 'an ', 'the '))
checks['has_abbreviation'] = any(w.isupper() and len(w) > 1 for w in title.split())
# Title type classification
if '?' in title:
checks['type'] = 'question'
elif ':' in title:
checks['type'] = 'compound'
elif title.split()[0].endswith('ing'):
checks['type'] = 'gerund'
else:
checks['type'] = 'declarative'
checks['recommendations'] = []
if word_count > 20:
checks['recommendations'].append('Consider shortening -- aim for 10-15 words')
if word_count < 8:
checks['recommendations'].append('Consider adding specificity')
if checks['starts_with_article']:
checks['recommendations'].append('Avoid starting with articles (A/An/The)')
if checks['has_abbreviation']:
checks['recommendations'].append('Spell out abbreviations in titles')
return checks
| Pattern | Example | Common In | |---------|---------|-----------| | Method: Finding | "Deep Learning for Protein Structure: A Comparative Study" | CS, Engineering | | Declarative | "Mindfulness Reduces Cortisol in College Students" | Psychology, Medicine | | Question | "Does Remote Work Improve Productivity?" | Social Sciences | | Descriptive | "Gene Expression Patterns in Early-Stage Lung Cancer" | Biology, Medicine |
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