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Data, Research & Analysis

academic-pipeline

Academic Pipeline is a source-indexed Agent Skill for data, research & analysis work. Based on the SKILL.md description, it focuses on orchestrator for the full academic research pipeline: research -> write -> integrity check -> review ->…. Use the linked GitHub file to confirm scope and prerequisites before enabling it.

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What this Skill does

Academic Pipeline is a source-indexed Agent Skill for data, research & analysis work. Based on the SKILL.md description, it focuses on orchestrator for the full academic research pipeline: research -> write -> integrity check -> review ->…. Use the linked GitHub file to confirm scope and prerequisites before enabling it.

What the author says

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

Compatibility and requirements

  • Use an agent runtime that supports Agent Skills and the linked SKILL.md format.
  • Confirm the source repository's tools, SDKs, and platform prerequisites for academic research work.

How to install or import it

  • Open the linked GitHub SKILL.md and review its scope and setup instructions.
  • Install or import the skill using the agent runtime's documented workflow.
  • Run a small, non-sensitive test and verify the output before broader use.

Permissions and risks

  • Review every command, file path, network request, dependency, and credential scope before enabling it.
  • GitHub stars indicate popularity, not safety or correctness; validate the source and test with non-sensitive data.

Example workflows

  • Ask an AI agent to apply Academic Pipeline to academic research work described in the source record.
  • Have the agent state assumptions, required tools, and expected output before using this data, research & analysis skill.
  • Compare the result with the linked GitHub SKILL.md and verify it against your project requirements.

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