# Openalex Ajg Insights

> ---

- Skill: `chanw-research/openalex-ajg-insights` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add chanw-research/openalex-ajg-insights`
- Raw SKILL.md: https://api.skillmd.com/api/skills/chanw-research/openalex-ajg-insights/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: chanw-research (https://skillmd.com/u/chanw-research)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/chanw-research/openalex-ajg-insights

---

---
name: openalex-ajg-insights
description: Search ABS/AJG-ranked literature through the bundled openalex-ajg-mcp backend, preserve review corpora for systematic literature reviews, download shortlisted papers through the bundled paper-download backend, convert collected PDFs to Markdown with MinerU, and retrieve abstract or full-text viewpoints efficiently.
---

# OpenAlex AJG Insights

Use this skill when the task is a literature review, gap scan, theory-building exercise, journal-targeted search, or any research workflow that needs business and management papers filtered by ABS/AJG and then upgraded into a reusable review corpus.

## Bundled Backends

This skill now ships with two embedded backends inside `review-gen`:

- `<review-gen-home>/backend/openalex-ajg-mcp`
- `<review-gen-home>/backend/paper-download-mcp`

This means a fresh machine usually only needs to clone `review-gen` and install `requirements.txt`.

For MinerU auth, use `MINERU_API_KEY`.

If needed, you can still override backend locations with:

- `--repo-root <path>` for the OpenAlex bridge
- `OPENALEX_AJG_MCP_ROOT=<path>`
- `PAPER_DOWNLOAD_MCP_ROOT=<path>`

## Core Scripts

- `scripts/openalex_ajg_bridge.py`
  Use for search, journal scans, and report summaries.
- `scripts/review_workflow.py`
  Use for systematic-review workspaces, corpus merging, manifest preparation, MinerU conversion, Markdown chunking, and retrieval.
- `scripts/download_manifest_papers.py`
  Use for incremental PDF downloading from `fulltext_manifest.csv`.

## Default Decision Rules

1. Start with abstract screening.
2. Upgrade to full text only for necessary papers.
3. When the user wants a reusable or systematic review, create a review workspace.
4. If classic or foundational papers should be collected first, download them from the manifest before expanding the set.
5. When the user has collected or downloaded PDFs, convert them to Markdown before asking AI to read them.
6. When the user asks for viewpoints from full text, do not load whole papers at once.
7. For long-running review projects, hand off to `review-orchestrator` after the corpus is ready.

## Platform-Agnostic Quick Start

Use these placeholders on any operating system:

- `<workflow-python>`: the Python interpreter that can run the workflow scripts
- `<review-gen-home>`: the folder containing the `review-gen` package or its installed skills
- `<review-workspace>`: the target review workspace

### A. Initialize a workspace

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/review_workflow.py \
  init-workspace \
  --topic "Entrepreneurial bricolage"
```

### B. Search through the bundled OpenAlex backend

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/openalex_ajg_bridge.py \
  search-abs \
  --query "AI agents" \
  --field "INFO MAN" \
  --min-rank "4*" \
  --year-start 2023 \
  --limit 10
```

### C. Merge raw search results into a corpus

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/review_workflow.py \
  --workspace <review-workspace> \
  merge-search-results
```

### D. Prepare the full-text manifest

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/review_workflow.py \
  --workspace <review-workspace> \
  prepare-fulltext-manifest --min-priority medium
```

### E. Download classic or priority PDFs first

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/download_manifest_papers.py \
  --workspace <review-workspace> \
  --min-priority high \
  --max-papers 5
```

The downloader is incremental. By default it only targets papers whose PDFs are still missing. This makes it easy to download classic papers first and then come back later for newly needed papers.

### F. Convert PDFs to Markdown with MinerU

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/review_workflow.py \
  --workspace <review-workspace> \
  convert-pdfs-with-mineru \
  --env-path <review-workspace>/04_fulltext/mineru.env
```

### G. Chunk and retrieve before reading

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/review_workflow.py \
  --workspace <review-workspace> \
  chunk-markdown
```

```text
python <review-gen-home>/skills/openalex-ajg-insights/scripts/review_workflow.py \
  --format markdown \
  --workspace <review-workspace> \
  retrieve-chunks \
  --query "What is the paper's core view?" \
  --purpose viewpoint \
  --top-k 6 \
  --include-neighbors
```

### H. Hand off to the orchestrator

- Use `review-orchestrator` to decide whether the project should go to planning or writing next.

## How To Think While Using This Skill

- For a fast literature scan, stay at the abstract layer.
- For a structured review, keep five layers separate:
  - raw search layer
  - merged corpus and screening layer
  - full-text manifest and download layer
  - full-text evidence layer
  - frozen review-plan layer
- For download planning, let citations and theory centrality drive which classic papers are fetched first.
- For full-text work, retrieve only the chunks needed for the active question.

## References

Read only what the request needs:

- `references/systematic-review-workflow.md`
- `references/pdf-fulltext-pipeline.md`
- `references/full-text-reading-strategy.md`