systematic-literature-review

What this skill does

# Systematic Literature Review Skill

## Overview

This skill produces a structured **systematic literature review (SLR)** across multiple academic papers on a research topic. Given a topic query, it searches arXiv, extracts structured metadata (research question, methodology, key findings, limitations) from each paper in parallel, synthesizes themes across the full set, and emits a final report with consistent citations.

**Distinct from `academic-paper-review`:** that skill does deep peer review of a single paper. This skill does breadth-first synthesis across many papers. If the user hands you one paper URL and asks "review this paper", route to `academic-paper-review` instead.

## When to Use This Skill

Use this skill when the user wants any of the following:

- A literature survey on a topic ("survey transformer attention variants", "review the literature on diffusion models")
- A synthesis across multiple papers ("what do recent papers say about X", "compare methodologies across papers on Y")
- A systematic review with consistent citation format ("do an SLR on Z in APA format")
- An annotated bibliography on a topic
- An overview of research trends in a field over a time window

Do **not** use this skill when:

- The user provides exactly one paper and asks to review it (use `academic-paper-review`)
- The user asks a factual question that does not require synthesizing multiple sources (answer directly)
- The user wants general web research without academic rigor (use standard web search)

## Workflow

The workflow has five phases. Follow them in order.

### Phase 1: Plan

Before doing any retrieval, confirm the following with the user. If any of these are unclear, ask **one** clarifying question that covers the missing pieces. Do not ask one question at a time.

- **Topic**: the research area in plain English (e.g. "transformer attention variants").
- **Scope**: how many papers (default 20, hard upper bound 50), optional time window (e.g. "last 2 years"), optional arXiv category (e.g. `cs.CL`, `cs.CV`).
- **Citation format**: APA, IEEE, or BibTeX (default APA if the user does not specify and does not seem to be writing for a specific venue).
- **Output location**: where to save the final report (default `/mnt/user-data/outputs/`).

If the user says "50+ papers", politely cap it at 50 and explain that synthesis quality degrades quickly past that — for larger surveys they should split by sub-topic.

### Phase 2: Search arXiv

Call the bundled search script. Do **not** try to scrape arXiv by other means and do **not** write your own HTTP client — this script handles URL encoding, Atom XML parsing, and id normalization correctly.

```bash
python /mnt/skills/public/systematic-literature-review/scripts/arxiv_search.py \
  "<topic>" \
  --max-results <N> \
  [--category <cat>] \
  [--sort-by relevance] \
  [--start-date YYYY-MM-DD] \
  [--end-date YYYY-MM-DD]
```

**IMPORTANT — extract 2-3 core keywords before searching.** Do not pass the user's full topic description as the query. Before calling the script, mentally reduce the topic to its 2-3 most essential terms. Drop qualifiers like "in computer vision", "for NLP", "variants", "recent" — those belong in `--category` or `--start-date`, not in the query string.

**Query phrasing — keep it short.** The script wraps multi-word queries in double quotes for phrase matching on arXiv. This means:

- `"diffusion models"` → searches for the exact phrase → good, returns relevant papers.
- `"diffusion models in computer vision"` → searches for that exact 5-word phrase → **too specific, likely returns 0 results** because few papers contain that exact string.

Use **2-3 core keywords** as the query, and use `--category` to narrow the field instead of stuffing field names into the query. Examples:

| User says | Good query | Bad query |
|---|---|---|
| "diffusion models in computer vision" | `"diffusion models" --category cs.CV` | `"diffusion models in computer vision"` |
| "transformer attention variants" | `"transformer attention"` | `"transformer attention variants in NLP"` |
| "graph neural networks for molecules" | `"graph neural networks" --category cs.LG` | `"graph neural networks for molecular property prediction"` |

The script prints a JSON array to stdout. Each paper has: `id`, `title`, `authors`, `abstract`, `published`, `updated`, `categories`, `pdf_url`, `abs_url`.

**Sort strategy**:

- **Always use `relevance` sorting** — arXiv's BM25-style scoring ensures results are actually about the user's topic. `submittedDate` sorting returns the most recently submitted papers in the category regardless of topic relevance, which produces mostly off-topic results.
- When the user asks for "recent" papers or gives a time window, use `--sort-by relevance` **combined with `--start-date`** to constrain the time range while keeping results on-topic. For example, "recent diffusion model papers" → `--sort-by relevance --start-date 2024-01-01`, not `--sort-by submittedDate`.
- `submittedDate` sorting is only appropriate when the user explicitly asks for chronological order (e.g. "show me papers in the order they were published"). This is rare.
- `lastUpdatedDate` is rarely useful; ignore it unless the user asks.

**Run the search exactly once.** Do not retry with modified queries if the results seem imperfect — arXiv's relevance ranking is what it is. Retrying with different query phrasings wastes tool calls and risks hitting the recursion limit. If the results are genuinely empty (0 papers), tell the user and suggest they broaden their topic or remove the category filter.

**If the script returns fewer papers than requested**, that is the real size of the arXiv result set for the query. Do not pad the list — report the actual count to the user and proceed.

**If the script fails** (network error, non-200 from arXiv), tell the user which error and stop. Do not try to fabricate paper metadata.

**Do not save the search results to a file** — the JSON stays in your context for Phase 3. The only file saved during the entire workflow is the final report in Phase 5.

### Phase 3: Extract metadata in parallel

**You MUST delegate extraction to subagents via the `task` tool — do not extract metadata yourself.** This is non-negotiable. Specifically, do NOT do any of the following:

- ❌ Write `python -c "papers = [...]"` or any Python/bash script to process papers
- ❌ Extract metadata inline in your own context by reading abstracts one by one
- ❌ Use any tool other than `task` for this phase

Instead, you MUST call the `task` tool to spawn subagents. The reason: extracting 10-50 papers in your own context consumes too many tokens and degrades synthesis quality in Phase 4. Each subagent runs in an isolated context with only its batch of papers, producing cleaner extractions.

Split papers into batches of ~5, then for each batch, call the `task` tool with `subagent_type: "general-purpose"`. Each subagent receives the paper abstracts as text and returns structured JSON.

**Concurrency limit: at most 3 subagents per turn.** The DeerFlow runtime enforces `MAX_CONCURRENT_SUBAGENTS = 3` and will silently drop any extra dispatches in the same turn — the LLM will not be told this happened, so strictly follow the round strategy below.

**Round strategy — use this decision table, do not compute the split yourself**:

| Paper count | Batches of ~5 papers | Rounds | Per-round subagent count |
|---|---|---|---|
| 1–5 | 1 batch | 1 round | 1 subagent |
| 6–10 | 2 batches | 1 round | 2 subagents |
| 11–15 | 3 batches | 1 round | 3 subagents |
| 16–20 | 4 batches | 2 rounds | 3 + 1 |
| 21–25 | 5 batches | 2 rounds | 3 + 2 |
| 26–30 | 6 batches | 2 rounds | 3 + 3 |
| 31–35 | 7 batches | 3 rounds | 3 + 3 + 1 |
| 36–40 | 8 batches | 3 rounds | 3 + 3 + 2 |
| 41–45 | 9 batches | 3 rounds | 3 + 3 + 3 |
| 46–50 | 10 batches | 4 rounds | 3 + 3 + 3 + 1 |

**Never dispatch more than 3 subagents in the same turn.** When a row says "2 rounds (3 + 1)"