synthesis-writer

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Synthesize evidence into a structured narrative (`output/SYNTHESIS.md`) grounded in `papers/extraction_table.csv`, including limitations and bias considerations.**Trigger**: synthesis, evidence synthesis, systematic review writing, 综合写作, SYNTHESIS.md.**Use when**: systematic review 完成 screening+extraction（含 bias 评估）后进入写作阶段（C4）。**Skip if**: 还没有 `papers/extraction_table.csv`（或 protocol/screening 尚未完成）。**Network**: none.**Guardrail**: 以 extraction table 为证据底座；明确局限性与偏倚；不要在无数据支撑时扩写结论。

⭐1stars🔀0forks📁View on GitHub🕐Updated Jan 8, 2026
[Academic Research]

When & Why to Use This Skill

This Claude skill automates the synthesis phase of systematic reviews by transforming extracted data into a structured, evidence-based narrative. It ensures high academic integrity by grounding every claim in the provided extraction table, incorporating risk-of-bias assessments, and identifying research limitations to prevent over-generalization. By streamlining the transition from data extraction to manuscript drafting, it helps researchers produce traceable and rigorous scientific reports.

Use Cases

  • Case 1: Generating a structured synthesis for a systematic review after completing the screening and data extraction phases (C4 stage).
  • Case 2: Comparing results across multiple studies by grouping them into themes, interventions, or outcomes based on specific extraction fields.
  • Case 3: Summarizing Risk of Bias (RoB) patterns and study limitations to ensure research conclusions are strictly bounded by the quality of available evidence.
  • Case 4: Creating a descriptive summary of an evidence base, including study counts, time windows, and study types for the methods section of a paper.
namesynthesis-writer
description|
**Trigger**synthesis, evidence synthesis, systematic review writing, 综合写作, SYNTHESIS.md.
**Use when**systematic review 完成 screening+extraction（含 bias 评估）后进入写作阶段（C4）。
**Skip if**还没有 `papers/extraction_table.csv`（或 protocol/screening 尚未完成）。
**Network**none.
**Guardrail**以 extraction table 为证据底座；明确局限性与偏倚；不要在无数据支撑时扩写结论。

Synthesis Writer (systematic review)

Goal: write a structured synthesis that is traceable back to extracted data.

Inputs

Required:

  • papers/extraction_table.csv

Optional:

  • DECISIONS.md (approval to write prose, if your process requires it)
  • output/PROTOCOL.md (to restate scope and methods consistently)

Outputs

  • output/SYNTHESIS.md

Workflow

  1. Check writing approval (if applicable)

    • If your pipeline requires it, confirm DECISIONS.md indicates approval before writing prose.

  2. Describe the evidence base (methods snapshot)

    • Summarize the included set using papers/extraction_table.csv (counts, time window, study types).
    • Keep this strictly descriptive.

  3. Theme-based synthesis

    • Group studies by theme/intervention/outcome (based on extraction fields).
    • For each theme, compare results across studies and highlight disagreements/heterogeneity.

  4. Bias + limitations

    • Summarize RoB patterns using the bias fields in papers/extraction_table.csv.
    • Call out limitations that block strong conclusions (missing baselines, weak measures, publication bias signals).

  5. Conclusions (bounded)

    • State only what the extracted evidence supports.
    • Separate “supported conclusions” vs “needs more evidence”.

Suggested outline for output/SYNTHESIS.md

  • Research questions + scope (from output/PROTOCOL.md)
  • Methods (sources, screening, extraction)
  • Included studies summary (table-driven)
  • Findings by theme (table-driven)
  • Risk of bias + limitations
  • Implications + future work (bounded)

Definition of Done

  • Every major claim in output/SYNTHESIS.md is traceable to specific fields/rows in papers/extraction_table.csv.
  • Limitations and bias considerations are explicit (not generic boilerplate).

Troubleshooting

Issue: the synthesis starts inventing facts not in the table

Fix:

  • Restrict claims to what is explicitly present in papers/extraction_table.csv; move speculation to “needs more evidence”.

Issue: extraction table is too sparse to synthesize

Fix:

  • Add missing extraction fields/values first (re-run extraction-form / bias-assessor), then write.