GRIND-Lab-Core.

data-download

Discover, evaluate, and download publicly available datasets from the internet. Infers data needs from a research question or task, selects authoritative sources, downloads reproducibly, validates file integrity, and documents provenance. Pauses for user input when authentication, API keys, or major tradeoffs require a decision. Use when user says "download data", "get data", "find a dataset", "I need boundary files", "download census data", or needs any external dataset for analysis.

deploy-experiment

Deploy and run experiments for ML/DL training (local, remote, or Modal GPU) AND spatial data science / GIScience experiments (local, data-driven). Reads from output/refine-logs/EXPERIMENT_PLAN.md and output/refine-logs/FINAL_PROPOSAL.md, writes to output/experiment/. Use when user says "run experiment", "deploy experiment", "execute experiment plan", or needs to launch training / spatial analysis jobs.

experiment-design-pipeline

Run an end-to-end workflow that chains the skills `refine-research` and `experiment-design`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to build a pipeline, do it end-to-end, or generate both the method and experiment plan together.

full-pipeline

Complete 4-stage end-to-end research pipeline. Orchestrates idea-discovery-pipeline → deploy-experiment → auto-review-loop → generate-report. Reads RESEARCH_PLAN.md (or BRIEF.md as fallback) for context that overrides $ARGUMENTS.

generate-idea

Generate and rank research ideas given a broad direction. Use when user input "brainstorm ideas", "generate research ideas", "what can we work on", or wants to explore a research area for publishable directions.

idea-discovery-pipeline

The full pipeline for idea generation. It generates 8-12 novel research ideas from literature gaps and evaluates each on novelty, feasibility, and domain fit. Orchestrates lit-review → generate-idea → novelty-check → idea-review → experiment-design-pipeline to go from a broad research direction to a validated, pilot-tested idea with a refined proposal and experiment plan. Produces output/IDEA_REPORT.md plus refinement and experiment artifacts.

lit-review

Retrieves papers from local folder or ArXiv and Semantic Scholar using domain-aware keyword expansion, builds synthesis matrix, identifies gaps. Calls tools/arxiv_fetch.py and tools/semantic_scholar_fetch.py. Writes to output/paper-cache/ and output/LIT_REVIEW_REPORT.md.

paper-covert

Converts the final Markdown manuscript from `paper-draft` / `paper-review-loop` into a submission package for the target venue — modular LaTeX (one file per section), compiled PDF, and Word `.docx`. Venue is read from `output/PAPER_PLAN.md` (or argument) and routed through a small YAML profile. Does not rewrite prose, score, or invent citations.

paper-draft

Transforms output/PAPER_PLAN.md into a journal-quality Markdown manuscript draft for GIScience, GeoAI, spatial data science, and remote sensing venues (IJGIS, ISPRS JPRS, RSE, TGIS, AAG Annals). Consults referenced literature, experiment, figure, and claim artifacts; supports full drafts, partial drafts, and skeleton drafts depending on readiness. Never fabricates results, metrics, or citations — produces a claim-to-evidence map and coverage-gap report alongside the manuscript.

paper-figure-generate

Generates publication-quality figures and diagrams from output/PAPER_PLAN.md for GIScience, GeoAI, and remote sensing journals (IJGIS, ISPRS JPRS, RSE, TGIS). Decides per-figure whether to produce reproducible code-generated plots/maps or structured prompts for external image-generation models (nano banana, ChatGPT image). Produces figure files, source scripts, captions, manifest, and prompt artifacts. Never fabricates results — uses only evidence from project files.

paper-review-loop

Reviews the manuscript produced by `paper-draft` (in `output/manuscript/`) as a demanding IJGIS / ISPRS JPRS reviewer-editor, cross-checks it against `output/PAPER_PLAN.md` and its evidence artifacts, then revises it into a stronger draft. Produces a reviewed manuscript, a revised manuscript, a structured review report, a prioritized issue log, a revision log, claim-risk notes, journal-fit notes, and next-loop priorities. Supports full, section-scoped, and mode-scoped review (structural / argument / novelty / methods / results-discussion / journal-fit / language / integrated). Safe on partial or skeletal drafts. Never fabricates results, citations, or figures.

paper-writing-pipeline

Full paper writing pipeline. Orchestrates paper-plan → paper-figure-generate → paper-draft → paper-review-loop → paper-covert to go from upstream research artifacts to a polished, submission-ready manuscript package (Markdown → LaTeX → PDF + DOCX). Use when user says \"write paper pipeline\", \"paper writing\", or wants the complete paper generation workflow.

spatial-analysis

Guideline-driven spatial analysis skill. Given a research question and data context, provides decision frameworks for selecting appropriate spatial methods, diagnostics, and interpretation strategies. Adapts to available data, spatial units, and analytical objectives — Claude Code determines the optimal workflow. Use when user says "spatial analysis", "analyze spatial data", "run spatial regression", "check for clustering", "map this", or needs to go from a research question to a complete spatial analysis.

submit-check

Validates a manuscript against target journal requirements before submission. Checks word count, section structure, figure/table count, reference format, and geo-specific reporting standards. Writes a PASS/FAIL checklist report to output/reports/.