# Research Scanner Workflow

> Discover research papers using ScholarX and bulk ingest them into the Graph-OS Knowledge Graph

- Skill: `knuckles-team/research-scanner-workflow` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add knuckles-team/research-scanner-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knuckles-team/research-scanner-workflow/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: Knuckles-Team (https://skillmd.com/u/knuckles-team)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/knuckles-team/research-scanner-workflow

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# Research Scanner Workflow

**CONCEPT:DEV-001**

Discover research papers using ScholarX and bulk ingest them into the Graph-OS Knowledge Graph

## Steps

### Step 0: collect-research-request [skill: user-interaction]
**Agent**: `scanner-agent`
**Tools**: `rep_rm_workspace, rep_rm_git`

Get the user's research focus query, lookback days, specific target scientific sources (e.g., arxiv, biorxiv), and maximum results.

### Step 1: search-papers [skill: scholarx-mcp]
**Agent**: `builder-agent`
**Tools**: `rep_rm_projects`

Search for the research papers based on the search parameters using the `scholarx_search` action with `action='search'`.

### Step 2: download-papers [skill: scholarx-mcp]
**Agent**: `validator-agent`
**Tools**: `rep_rm_projects, gl_pipelines`

Submit bulk download jobs for the discovered paper IDs via `scholarx_storage` with `action='bulk_download'`. Check the download queue status via `action='status'`.

### Step 3: Graph Os
**Agent**: `publisher-agent`
**Tools**: `rep_rm_git, gl_merge_requests`

Ingest the newly downloaded research PDFs into the Knowledge Graph via the ingest action `mcp_graph-os_graph_ingest` with `action='ingest'`.

### Step 4: present-research-summary [skill: user-interaction]
**Agent**: `scanner-agent`
**Tools**: `rep_rm_workspace, rep_rm_git`

Display the completed research summary and details of the ingested papers to the user.

### Step 5: KG Persistence [depends_on: Step 0, Step 4]
**Agent**: `publisher-agent`
**Tools**: `graph_write`

Persist workflow results as nodes and edges in the Knowledge Graph.
Create appropriate typed nodes with metadata and link to existing domain entities.

## Output
- Research Scanner results persisted in KG
- Structured report (MD/PDF)
- Audit trail with timestamps and agent attributions

## Execution

Run this workflow as a dependency-ordered DAG. Steps with no unmet `depends_on` run in parallel; dependents run after their prerequisites complete.

- **Run first (in parallel):** Step 0 — collect-research-request; Step 1 — search-papers; Step 2 — download-papers; Step 3 — Graph Os; Step 4 — present-research-summary
- **After level 0:** Step 5 — KG Persistence

**Execution:** If graph-os is reachable, offload the whole DAG via `graph_orchestrate action=execute_workflow` (or the `kg-delegate` skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet `depends_on` in parallel, then their dependents.