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Get Started Free →Guide to writing clear and reproducible methodology sections
.claude/skills/brycewang-stanford-methods-section-guide/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 287% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 108% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 247% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 90% | 0% |
| case-24 | ✗→✓ | ▲ Improved | 83% | 0% |
Write methodology sections that are clear, complete, and reproducible, following discipline-specific conventions and best practices.
The methods section answers: "How did you do this study, and can someone else replicate it?" A well-written methods section:
The methods section typically follows this order (adapt to your discipline):
| Subsection | Contents | |-----------|----------| | Study Design | Overall approach (experimental, observational, computational, qualitative) | | Participants / Samples | Population, sampling strategy, inclusion/exclusion criteria, sample size justification | | Materials / Instruments | Equipment, software, reagents, questionnaires, datasets | | Procedure | Step-by-step protocol, chronological order of data collection | | Data Analysis | Statistical tests, software, significance thresholds, model specifications | | Ethical Considerations | IRB approval, informed consent, data privacy |
markdown## Materials and Methods ### Cell Culture and Treatment HeLa cells (ATCC CCL-2) were maintained in DMEM (Gibco, #11965092) supplemented with 10% FBS (Gibco, #26140079) and 1% penicillin- streptomycin (Gibco, #15140122) at 37C in 5% CO2. Cells were seeded at 5 x 10^4 cells/well in 24-well plates and treated with compound X (0.1, 1, 10 uM) for 24 hours. ### Western Blot Analysis Total protein was extracted using RIPA buffer (Thermo, #89900) with protease inhibitor cocktail (Roche, #04693116001). Proteins (30 ug/lane) were separated on 10% SDS-PAGE gels and transferred to PVDF membranes. Primary antibodies: anti-TargetProtein (Cell Signaling, #1234, 1:1000), anti-beta-actin (Sigma, #A5441, 1:5000). Secondary antibodies: HRP-conjugated (1:10000).
Key conventions:
markdown## Methods ### Dataset We evaluated our method on three benchmark datasets: - **ImageNet-1K** (Russakovsky et al., 2015): 1.28M training images, 50K validation images across 1,000 classes - **CIFAR-100** (Krizhevsky, 2009): 50K training, 10K test, 100 classes - **Oxford Flowers-102** (Nilsback & Zisserman, 2008): 8,189 images, 102 classes ### Model Architecture Our model extends the Vision Transformer (ViT-B/16) with the following modifications: 1. Replaced standard self-attention with linear attention (Katharopoulos et al., 2020) 2. Added a learnable class-conditional normalization layer after each block 3. Used patch size 16x16 with input resolution 224x224 ### Training Details | Hyperparameter | Value | |---------------|-------| | Optimizer | AdamW (beta1=0.9, beta2=0.999) | | Learning rate | 1e-3 with cosine decay | | Weight decay | 0.05 | | Batch size | 256 (across 4 A100 GPUs) | | Training epochs | 300 | | Warmup epochs | 10 | | Data augmentation | RandAugment (N=2, M=9), Mixup (alpha=0.8) | | Label smoothing | 0.1 | All experiments were implemented in PyTorch 2.1 and run on 4x NVIDIA A100 80GB GPUs. Training took approximately 18 hours per run. Code is available at [repository URL].
markdown## Methods ### Participants A total of 412 participants (245 female, 162 male, 5 non-binary; M_age = 34.2, SD = 11.8) were recruited via Prolific. Inclusion criteria: (a) aged 18-65, (b) fluent in English, (c) resided in the US. Exclusion criteria: (a) failed two or more attention checks, (b) completed the survey in under 3 minutes. After exclusions, 387 participants remained (attrition: 6.1%). Sample size was determined a priori using G*Power 3.1 (Faul et al., 2007). For a medium effect size (f^2 = 0.15), alpha = .05, and power = .80 in a multiple regression with 5 predictors, the required sample was 92. We oversampled to ensure adequate power for subgroup analyses. ### Measures **Perceived Stress Scale (PSS-10)** (Cohen et al., 1983): 10 items, 5-point Likert scale (0 = never, 4 = very often). Cronbach's alpha in the current sample: .87. **Big Five Inventory (BFI-10)** (Rammstedt & John, 2007): 10 items, 5-point Likert scale. Subscale alphas ranged from .68 to .81. ### Procedure After providing informed consent, participants completed measures in the following fixed order: demographics, PSS-10, BFI-10, experimental task, manipulation check, debriefing. Median completion time: 14 minutes. Participants were compensated GBP 2.50. ### Ethical Approval This study was approved by the [University] IRB (Protocol #2024-0123). All participants provided informed consent.
Use this checklist to ensure your methods section is complete:
| Issue | Example | Fix | |-------|---------|-----| | Vague descriptions | "Data was analyzed statistically" | Specify exact tests: "We used a two-tailed independent samples t-test" | | Missing software versions | "Analysis done in R" | "Analysis conducted in R 4.3.1 using lme4 v1.1-35" | | No sample size justification | Just reporting N | Include power analysis or justify based on conventions | | Ambiguous order | Reader cannot tell what happened when | Use numbered steps or chronological narrative | | Results in methods | Including p-values or outcomes | Save all results for the Results section | | Over-referencing | Citing a protocol without summarizing key details | Provide enough detail to understand without reading the reference |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 6,179 | 9,623 | +56% | 1 | 1 | 0% | 891 | 3,452 | +287% | 0 | 0 | — |
case-02 | fail→fail | 15,320 | 10,827 | -29% | 1 | 1 | 0% | 2,199 | 3,784 | +72% | 0 | 0 | — |
case-03 | pass→fail | 11,600 | 11,694 | +1% | 1 | 1 | 0% | 1,956 | 3,845 | +97% | 0 | 0 | — |
case-04 | pass→pass | 27,527 | 13,449 | -51% | 1 | 1 | 0% | 2,649 | 4,425 | +67% | 0 | 0 | — |
case-05 | pass→pass | 8,526 | 6,975 | -18% | 1 | 1 | 0% | 1,594 | 3,132 | +96% | 0 | 0 | — |
case-06 | fail→pass | 9,118 | 9,542 | +5% | 1 | 1 | 0% | 1,738 | 3,608 | +108% | 0 | 0 | — |
case-07 | pass→pass | 8,562 | 9,903 | +16% | 1 | 1 | 0% | 1,565 | 3,622 | +131% | 0 | 0 | — |
case-08 | fail→pass | 4,799 | 6,958 | +45% | 1 | 1 | 0% | 879 | 3,050 | +247% | 0 | 0 | — |
case-09 | pass→pass | 18,151 | 15,780 | -13% | 1 | 1 | 0% | 2,745 | 4,662 | +70% | 0 | 0 | — |
case-10 | pass→pass | 10,815 | 8,443 | -22% | 1 | 1 | 0% | 1,472 | 3,332 | +126% | 0 | 0 | — |
case-11 | fail→fail | 10,050 | 9,478 | -6% | 1 | 1 | 0% | 1,587 | 3,514 | +121% | 0 | 0 | — |
case-12 | pass→pass | 14,401 | 13,481 | -6% | 1 | 1 | 0% | 2,334 | 4,228 | +81% | 0 | 0 | — |
case-13 | pass→pass | 11,847 | 11,379 | -4% | 1 | 1 | 0% | 2,018 | 4,039 | +100% | 0 | 0 | — |
case-14 | pass→pass | 13,724 | 12,291 | -10% | 1 | 1 | 0% | 2,476 | 4,161 | +68% | 0 | 0 | — |
case-15 | fail→fail | 17,348 | 13,880 | -20% | 1 | 1 | 0% | 2,350 | 4,222 | +80% | 0 | 0 | — |
case-16 | pass→pass | 8,677 | 5,099 | -41% | 1 | 1 | 0% | 1,231 | 2,703 | +120% | 0 | 0 | — |
case-17 | pass→pass | 18,073 | 14,764 | -18% | 1 | 1 | 0% | 3,110 | 4,085 | +31% | 0 | 0 | — |
case-18 | fail→fail | 12,521 | 18,537 | +48% | 1 | 1 | 0% | 1,710 | 3,913 | +129% | 0 | 0 | — |
case-19 | pass→pass | 10,862 | 18,329 | +69% | 1 | 1 | 0% | 2,087 | 4,707 | +126% | 0 | 0 | — |
case-20 | pass→pass | 8,752 | 8,738 | -0% | 1 | 1 | 0% | 1,785 | 3,619 | +103% | 0 | 0 | — |
case-21 | pass→pass | 36,371 | 37,886 | +4% | 1 | 1 | 0% | 5,601 | 7,585 | +35% | 0 | 0 | — |
case-22 | fail→pass | 19,176 | 21,996 | +15% | 1 | 1 | 0% | 2,733 | 5,182 | +90% | 0 | 0 | — |
case-23 | fail→fail | 14,558 | 13,261 | -9% | 1 | 1 | 0% | 2,164 | 4,199 | +94% | 0 | 0 | — |
case-24 | fail→pass | 13,912 | 9,723 | -30% | 1 | 1 | 0% | 2,025 | 3,715 | +83% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +17 percentage points is the difference between those two pass rates over the 24 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.