Orchestrate local AutoReview pipeline stages via Claude Code subagents with snapshot resume.
npx skills add https://github.com/mcleanT/AutoReview --skill run-local-mcleant
Please help me install this Agent Skill. Skill: run-local Source: https://github.com/mcleanT/AutoReview/tree/main/.claude/skills/run-local Command: npx skills add https://github.com/mcleanT/AutoReview --skill run-local-mcleant
Running the AutoReview pipeline locally without external LLM API keys is complex and error-prone; this Skill provides a single, auditable execution contract that orchestrates each pipeline stage via Claude Code subagents, enforces validation gates, and preserves resumable snapshots to avoid silent stage-skips and lost work.
Ask the skill to "Run the AutoReview pipeline locally on the topic the role of gut microbiome in neurodegenerative diseases and save outputs to output/."
High-intent search queries and answers about installing and using this skill.
You can run an automated literature review locally without external API keys by orchestrating Claude Code subagents across each pipeline stage, enforcing per-stage validation gates, and saving intermediate outputs to snapshot files for resumable execution.
Yes, you can resume an interrupted literature review pipeline from a specific stage using snapshot-based resume workflows that load previously saved per-stage JSON snapshots and token timing logs to continue execution without silent stage-skips or lost work.
The best way to debug a literature review pipeline stage by stage is to use deterministic stage orchestration with per-stage validation gates and snapshot files, allowing you to isolate, inspect, and resume execution from individual stages like discovery, analysis, enrichment, or writing.
Yes, a mandatory configuration interview is required before execution, collecting the research topic, domain, depth, model or provider, output targets, and resume options to ensure deterministic stage orchestration and proper search and citation validation.
Citation validation during automated literature review writing works by mandating direct Python SearchAggregator usage for pipeline searches, applying tiered citation planning, and enforcing strict citation validation gates during the section writing and assembly stages.
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