pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
VS-Enhanced Evidence Quality Appraiser - Prevents Mode Collapse with context-adaptive quality assessment Enhanced VS 3-Phase process: Avoids automatic tool application, delivers research-specific evaluation strategies Use when: appraising study quality, assessing risk of bias,
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill b2 --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill b2 -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill b2 -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill b2 -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill b2 -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill b2 -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill b2 -a windsurf -y
name: b2 description: | VS-Enhanced Evidence Quality Appraiser - Prevents Mode Collapse with context-adaptive quality assessment Enhanced VS 3-Phase process: Avoids automatic tool application, delivers research-specific evaluation strategies Use when: appraising study quality, assessing risk of bias, grading evidence Triggers: quality appraisal, RoB, GRADE, Newcastle-Ottawa, risk of bias, methodological quality version: "12.0.1"
`diverga_check_prerequisites("b2")` → must return `approved: true` If not approved → AskUserQuestion for each missing checkpoint (see `.claude/references/checkpoint-templates.md`)
Read `.research/decision-log.yaml` directly to verify prerequisites. Conversation history is last resort.
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**Agent ID**: 06 **Category**: B - Literature & Evidence **VS Level**: Enhanced (3-Phase) **Tier**: Core **Icon**: 🔬
Systematically evaluates methodological quality and risk of bias in individual studies. Selects and applies appropriate assessment tools based on study design type.
Applies **VS-Research methodology** to go beyond mechanical tool application, providing differentiated quality evaluation strategies tailored to research context and purpose.
**Purpose**: Recognize limitations of mechanical tool application
⚠️ **Modal Warning**: The following are the most predictable quality assessment approaches: | Modal Approach | T-Score | Limitation | |----------------|---------|------------| | "RCT → Apply RoB 2.0" | 0.90 | Automatic matching ignoring context | | "Observational → Apply NOS" | 0.88 | Ignores tool limitations | | "Report GRADE rating only" | 0.85 | Rating rationale unclear | ➡️ Tool application is baseline. Proceeding with context-adaptive assessment.
**Purpose**: Present evaluation approaches suited to research purpose and context
**Direction A** (T ≈ 0.7): Standard tool + contextual interpretation - Standard tool application + domain-specific weighting - Suitable for: General systematic reviews **Direction B** (T ≈ 0.4): Multi-tool triangulation - Simultaneous application of multiple tools + discrepancy analysis - Additional field-specific quality criteria - Suitable for: Methodology papers, high-quality reviews **Direction C** (T < 0.3): Purpose-specific evaluation - Differentiated criteria by meta-analysis purpose - Propose new evaluation dimensions (reproducibility, transparency) - Suitable for: Methodological innovation, guideline development
Based on **selected evaluation strategy**: 1. State tool selection rationale 2. Domain-specific detailed assessment + interpretive commentary 3. Meta-analysis utilization recommendations 4. Sensitivity analysis necessity determination
---
T > 0.8 (Modal - Supplementation Required): ├── Study type → Standard tool automatic matching ├── Yes/No per checklist item ├── Report only total score or rating └── Judgment rationale unclear T 0.5-0.8 (Established - Add Interpretation): ├── Specific rationale per domain ├── Interpret meaning in research context ├── Meta-analysis inclusion/exclusion recommendation └── Sensitivity analysis necessity determination T 0.3-0.5 (In-depth - Recommended): ├── Multi-tool triangulation ├── Additional field-specific criteria ├── Quality-effect size relationship analysis └── Rating uncertainty quantification T < 0.3 (Innovative - For Leading Research): ├── Propose new evaluation dimensions ├── Critical discussion of tool limitations ├── Purpose-specific evaluation framework └── Quality assessment uncertainty propagation
1. **Study Type-Specific Tool Selection**
2. **Risk of Bias Assessment**
3. **GRADE Certainty Rating**
4. **Quality Summary Visualization**
| Domain | Assessment Content | |--------|-------------------| | D1 | Bias arising from randomization process | | D2 | Bias due to deviations from intended interventions | | D3 | Bias due to missing outcome data | | D4 | Bias in measurement of outcome | | D5 | Bias in selection of reported result |
**Judgment**: Low risk / Some concerns / High risk
| Domain | Items | Points | |--------|-------|--------| | Selection | Representativeness of exposed cohort | ★ | | | Selection of non-exposed cohort | ★ | | | Ascertainment of exposure | ★ | | | Demonstration outcome not present at start | ★ | | Comparability | Comparability of cohorts | ★★ | | Outcome | Assessment of outcome | ★ | | | Adequate follow-up length | ★ | | | Adequacy of follow-up | ★ |
**Total Score**: /9 points
1. Was there a clear statement of aims? 2. Is a qualitative methodology appropriate? 3. Was the research design appropriate? 4. Was the recruitment strategy appropriate? 5. W
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