skills/33-Galaxy-Dawn-claude-scholar/skills/obsidian-literature-workflow/SKILL.md
Use this skill when the user keeps paper notes inside an Obsidian project knowledge base and wants filesystem-first literature review, explicit agent-first Zotero ingestion, `Papers/` plus `Knowledge/` synthesis, collection-wide normalization, and a default literature canvas without Obsidian MCP.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research obsidian-literature-workflowInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Handle the literature sub-workflow inside the filesystem-first Obsidian project knowledge base.
This is a supporting skill under obsidian-project-memory.
Use it when the user says things like:
Papers/”.claude/project-memory/registry.yaml, or can be imported with obsidian-project-bootstrapPapers/ inside Research/{project-slug}/$zotero-obsidian-bridge when the source corpus is in Zotero$zotero-obsidian-bridge to pull them into canonical Papers/*.md notes.Papers/ and adjacent synthesis notes using filesystem tools.$obsidian-markdown.
ClaimMethodEvidenceLimitationDirect relevance to repoRelation to other papersKnowledge/ notes,Writing/ if the user asked for a review or comparison deliverable.Knowledge/, not Experiments/ or Results/:
Knowledge/Literature-Overview.mdKnowledge/Method-Families.mdKnowledge/Research-Gaps.md
when the synthesis is stable enough to deserve canonical notes.Maps/literature.canvas after major paper-note changes or batch note creation.
Maps/literature-main.canvas only when a lightweight presentation graph is needed.Papers/ remains first-class: one durable paper note per paper whenever possibleKnowledge/ holds durable literature synthesis notesMaps/literature.canvas is the default visual graph surfaceDo not assume by default:
Concepts/Datasets/.base viewsThe literature workflow may create Maps/literature.canvas by default. Other artifacts still require explicit justification.
Load only what is needed:
references/PAPER-NOTE-SCHEMA.md - detailed paper-note frontmatter and sectionsreferences/CANVAS-WORKFLOW.md - how and when to refresh Maps/literature.canvasdevelopment
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.