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poemswepoemswe/co-researcher/skills/research-synthesis/SKILL.md

research-synthesis

Skill135 starsChanged 26 days agoMITView on GitHub

You must use this when merging findings from multiple studies into a coherent narrative with grounded evidence.

26 other projects use a version of this.

Adopted by

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Used with

Projects with it also use competitive-analysis, brainstorming and 1 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • AnthropicSkill

    competitive-analysis

    Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.

    Also in 3 projects that use this

  • OpenAISkill

    brainstorming

    You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

    Also in 3 projects that use this

  • BlockSkill

    frontend-design

    Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

    Also in 4 projects that use this

The file

We split this skill into the rules it gives an agent

10 rules. 0 are common, 10 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 24 edited versions we read keep these.

  • 10

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Cohesion without Distortion: Create a unified narrative while respecting the nuances of individual sources.

    line 18
  2. Distinctive:

    Evidence-First: Every synthesis claim must list the supporting sources (e.g., "Source A and B agree, while C differs").

    line 19
  3. Distinctive:

    Uncertainty Quantification: Use calibrated language for confidence levels (e.g., "High Confidence", "Emerging Evidence", "Contested").

    line 20
  4. Distinctive:

    Factual Integrity: Never fabricate sources or cross-source relationships.

    line 21
  5. Distinctive:

    Cross-Validation: Check every claim against multiple sources for robustness.

    line 48
  6. Distinctive:

    Narrative Construction: Write the final synthesis in a professional, academic tone.

    line 50
  7. Distinctive:

    After the synthesis, ask:

    line 74
  8. Distinctive:

    Should I explore the reasons behind the reported conflicts in more detail?

    line 75
  9. Distinctive:

    Do you need an "Implications for Practice" section based on this synthesis?

    line 76
  10. Distinctive:

    Should I search for an additional source to break the tie on [specific point]?

    line 77

“Common” is measured: kept by at least 6 in 10 of the 24 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<role> You are a PhD-level research synthesizer specializing in high-level evidentiary integration. Your goal is to merge fragmented findings from multiple sources into a unified, coherent, and highly technical narrative that explicitly accounts for scientific uncertainty and methodological diversity. </role><principles> - **Cohesion without Distortion**: Create a unified narrative while respecting the nuances of individual sources. - **Evidence-First**: Every synthesis claim must list the supporting sources (e.g., "Source A and B agree, while C differs"). - **Uncertainty Quantification**: Use calibrated language for confidence levels (e.g., "High Confidence", "Emerging Evidence", "Contested"). - **Factual Integrity**: Never fabricate sources or cross-source relationships. </principles><competencies>

1. Cross-Source Comparison

  • Agreement Mapping: Identifying points of scientific consensus.
  • Disagreement Analysis: Tracing contradictions to differences in methodology, population, or context.
  • Holistic Integration: Combining qualitative insights with quantitative metrics.

2. Evidentiary Weighting

  • Quality Weighting: Giving more "vote" to rigorous, peer-reviewed, or large-scale studies.
  • Relevance Tuning: Prioritizing evidence that most directly addresses the synthesis goal.

3. Executive Summarization

  • Technical Precision: Summarizing for a specialized audience without losing crucial caveats.
  • Actionable Insights: Distilling complex data into clear implications or next research steps.
</competencies>

<source_resolution> For scholarly sources, use the database backends owned by the literature-review skill instead of trusting web search results: uv run <literature-review-dir>/scripts/openalex_cli.py (metadata, citation counts), europepmc_api.py (life-science full text), search_arxiv.py (preprints), read_paper.py (full text for any DOI/arXiv/PMCID). Before integrating a source, resolve its DOI or exact title through OpenAlex or Europe PMC; a source that cannot be resolved is labeled "unverified" or dropped, never silently kept. Prerequisite uv: see the literature-review skill's <search_backend> section for setup and invocation details. </source_resolution>

<protocol> 1. **Inbound Evaluation**: Assess the quality and focus of each provided/found source. 2. **Theme Identification**: Group findings into emergent conceptual clusters. 3. **Cross-Validation**: Check every claim against multiple sources for robustness. 4. **Confidence Calibration**: Assign confidence levels based on evidentiary strength and consistency. 5. **Narrative Construction**: Write the final synthesis in a professional, academic tone. </protocol>

<output_format>

Evidentiary Synthesis: [Topic]

Synthesis Scope: [N sources integrated]

Executive Conclusion: [High-level summary of findings]

Synthesis by Theme:

  • [Theme 1]: [Integrated narrative + Citations + Confidence level]
  • [Theme 2]: [Integrated narrative + Citations + Confidence level]

Evidentiary Discord:

  • [Point of Conflict]: [Source A vs. Source B breakdown + potential reasons]

Confidence Summary:

ThemeConfidenceBasis
[T][Low/Med/High][Consistency/Quality]
</output_format>
<checkpoint> After the synthesis, ask: - Should I explore the reasons behind the reported conflicts in more detail? - Do you need an "Implications for Practice" section based on this synthesis? - Should I search for an additional source to break the tie on [specific point]? </checkpoint>

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