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    synthesize obsidian research

    1

    Turn fragmented Obsidian notes into a traceable, evidence-based claim ledger and research synthesis.

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    See it in action

    You say

    Run a health diagnostic on my 'Machine Learning Research' folder. I need to know which claims are currently unsupported and where my source notes are missing locators.

    Your agent does

    Diagnostic complete. Found 42 source notes.

    • Traceability: 12 notes missing locators (page numbers/URLs).
    • Claim Health: 5 propositions identified as 'unsupported' due to missing evidence links.
    • Recommendation: Convert 'Attention-Mechanism-Notes.md' from a general summary to a claim ledger.

    What you get

    Audit vault health to find untraceable highlights and unsupported claims.Map contradictions and agreements across multiple research papers automatically.Structure raw literature notes into a defensible evidence brief or report.Maintain strict separation between source material and original interpretations.

    About this skill

    The problem

    Research vaults in Obsidian often become "write-only" graveyards of fragmented highlights, ambiguous paraphrases, and untraceable claims. Developers and researchers struggle to distinguish their own ideas from source material, leading to weak arguments and evidence gaps.

    What it does

    • Performs a read-only health diagnostic to identify fragmentation, link quality, and citation coverage.
    • Maps existing notes into a structured research model including source notes, evidence items, and claims.
    • Constructs a claim ledger that links propositions to specific supporting or contradicting evidence with exact locators.
    • Generates synthesis reports that highlight source disagreement and identify missing data points.
    • Prepares reversible, incremental vault updates to improve organization without destroying original wording.

    Frameworks & tools

    Designed for Obsidian vaults using Markdown. Utilizes citation keys, BibTeX/Zotero conventions, and YAML frontmatter. Includes a Python-based vault scanner for local execution.

    Why this beats prompting it yourself

    Generic LLM prompts often hallucinate connections or "average away" source disagreements. This skill enforces strict evidence traceability, preventing the AI from turning semantic similarity into false evidential support while preserving your specific citation keys and folder structures.

    Use cases

    • Auditing a research vault for evidence gaps before starting a thesis or whitepaper.
    • Consolidating messy Zotero imports into a structured literature review.
    • Building a defensible claim-evidence matrix for technical reports or books.
    • Mapping contradictions between multiple sources in a specialized domain.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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