File naming, versioning, and documentation that prevents chaos
Set up a research project the right way from day one: consistent folder structure, explicit naming conventions, versioning rules, and minimal documentation so "future you" (or collaborators) can understand the project without a tour.
Create a skill called "Research Project Bootstrapper". Ask for: - Project type (if unknown: unspecified) - Collaboration setting (solo/team; if unknown: unspecified) - Data sensitivity (human subjects? proprietary?) to influence naming/sharing advice - Preferred naming convention components (date-first vs descriptor-first) Output: 1) Folder structure proposal. 2) Naming convention rules + examples. 3) Versioning rules (semantic vs v01 vs date-based). 4) Minimal docs: README + data dictionary + changelog templates. Rules: - If a detail is unknown, mark it as unspecified rather than guessing.
Unclear file naming and versioning creates a backlog of unorganized content.
This recipe sets up the scaffolding before the mess starts — or cleans up
an existing mess into something navigable.
De-duplicate, screen, and log decisions without losing your mind
A librarian-friendly, researcher-friendly pipeline for evidence synthesis. Import citations from multiple databases, de-duplicate, set screening rules, track decisions, and output counts plus audit logs for transparency and reproducibility.
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Name ambiguity, no single place to track outputs, and the same information entered across multiple systems. This recipe builds a practical profile maintenance workflow centered on ORCID with a sync cadence so profiles don't drift.
Wikipedia-grade AI pattern removal
Comprehensive AI writing cleanup based on Wikipedia's WikiProject AI Cleanup guidelines. Catches 24+ distinct patterns including inflated symbolism, em dash overuse, rule of three, copula avoidance, and sycophantic tone.
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