# Paper Scan

> Parallel execution workflow for paper scan using the Unified Parallel Engine

- Skill: `knuckles-team/paper-scan` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add knuckles-team/paper-scan`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knuckles-team/paper-scan/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/paper-scan

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# Paper Scan Workflow

**CONCEPT:RESEARCH-001**

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## Steps

### Step 1: Topic Extractor
**Agent**: `search-agent`
**Tools**: `sx_search, graph_query`

Extract focus topics from the Knowledge Graph to build a relevance taxonomy.

### Step 2: Scholarx Fetcher
**Agent**: `analyzer-agent`
**Tools**: `graph_analyze, sx_storage`

Fetch daily papers via the scholarx MCP using the extracted taxonomy.

### Step 3: Paper Scorer
**Agent**: `synthesizer-agent`
**Tools**: `graph_analyze, document_tools`

Score the fetched papers locally against the relevance taxonomy.

### Step 4: Paper Downloader
**Agent**: `ingestor-agent`
**Tools**: `graph_write, kg_graph_ingest`

Bulk download the most valuable papers for ingestion.

### Step 5: KG Persistence [depends_on: paper-downloader]
**Agent**: `ingestor-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
- Paper Scan 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 1 — Topic Extractor; Step 2 — Scholarx Fetcher; Step 3 — Paper Scorer; Step 4 — Paper Downloader
- **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.