Literature Review

Literature Review

Orchestrate a structured two-pass review. The script drives state management and data fetching; the agent drives the judgment calls (screening, synthesis). Each review is a persistent session — you can pause, resume, and revisit.

Phases

1. init      Create session, capture the research question
2. search    Wide net across Semantic Scholar (+ arXiv optional), dedup
3. screen    Agent reads titles/abstracts/tldrs, marks keepers
4. fetch     Script produces a fetch plan (arxiv-analyze or PDF URL per paper)
5. [read]    Agent reads each shortlisted paper via the indicated strategy
6. [synth]   Agent writes the review: established findings, tensions, gaps

Steps 5 and 6 are agent-driven (no new subcommand); the script's job is to set up the material.

Usage

# 1. Start a session
python3 scripts/literature_review.py init \
    "what makes sparse autoencoders interpretable in practice" \
    [--out-dir /path/to/reviews]

# Returns: {"session": "./literature-reviews/what-makes-sparse-..", "slug": "..."}

# 2. Cast a wide net
python3 scripts/literature_review.py search  \
    --limit 50 --year 2023-2026 --min-citations 5 \
    [--venue ICLR,NeurIPS] [--include-arxiv]

# 3. Agent reads candidates.json, picks keepers
python3 scripts/literature_review.py screen  \
    --include "a3ec0b75...,2501.11120,10.48550/arXiv.2401.00032" \
    [--reasons-file keep-reasons.json]

# 4. Get the fetch plan
python3 scripts/literature_review.py fetch 

# 5-6. Agent reads each paper and writes ./literature-reviews//final.md

# Status check at any time
python3 scripts/literature_review.py status 
python3 scripts/literature_review.py list-sessions [--out-dir /path]

Session layout

./literature-reviews//
├── state.json         # phase, question, counters
├── candidates.json    # phase-1 output: full search results
├── shortlist.json     # phase-2 output: papers the agent kept + reasons
├── fetch_plan.json    # phase-3 output: per-paper read strategy
└── final.md           # phase-4 output: agent writes this

Everything is JSON except final.md. Sessions can be re-run (rerun search to refresh, redo screen to revise shortlist). Delete the session dir to start over.

Workflow

Phase 1: init

Capture the research question clearly. Good questions are specific: "how does X differ from Y under condition Z" beats "X and Y". If the question is vague, prompt the user to sharpen it before running init.

Phase 2: search

Default limit 50. Use --year to scope recency, --min-citations to cut noise. Add --include-arxiv if arxiv-search is installed and you want preprint coverage the scholar index may lag behind on.

Script dedups by arxiv_id, doi, and lowercased title.

Phase 3: screen (the agent does this)

Read candidates.json. For each paper, decide: include or exclude. Use the first 1-2 sentences of the abstract for fast scanning; drill into the full abstract only for ambiguous calls.

Explicit inclusion criteria help consistency:

• Does the paper address the question directly?
• Is the method/claim specific enough to learn from?
• Is the venue/year/citation count above threshold?

Invoke screen with the comma-separated IDs of keepers. Optionally pass --reasons-file mapping each included ID to a one-line reason — these flow into shortlist.json and into the final synthesis.

Target shortlist size: 10-25 papers. Fewer and you're missing coverage; more and synthesis gets unwieldy.

Phase 4: fetch

Script produces fetch_plan.json: for each shortlisted paper, the best way to read it:

• arxiv-analyze — when arxiv_id is present (preferred: tiered markdown → HTML → TeX fallback)
• open-access-pdf — direct PDF URL
• abstract-only — no full-text available; agent decides whether to keep

Phase 5: read (agent-driven)

For each paper in the fetch plan, pull the full text using the indicated strategy. Extract: claim, method, result, limitation. Keep notes in memory or in scratch files inside the session dir.

Phase 6: synthesize (agent-driven)

Write final.md with this structure:

# Literature Review: 

## TL;DR
One paragraph. What does the literature say?

## Established findings
Claims multiple papers converge on, with citations.

## Active debates
Claims papers disagree about. Who holds each position, and on what grounds.

## Methodological patterns
Dominant approaches and their trade-offs.

## Gaps
What's unstudied, understudied, or inconclusive.

## Most important paper to read next
One paper. Why.

## Appendix: included papers
Table: title | authors | year | venue | one-line takeaway | full-text status

Hard rules

• Never fabricate citations or TLDRs. If a field is missing in shortlist.json, mark it as such in the synthesis.
• Two-pass screening is not optional. Skipping the screen phase and going straight to full-text reads for 50 papers is a token disaster and a quality disaster (signal-to-noise).
• Session state is append-only by convention. If you need to revise, prefer creating a new session over editing state.json in place.
• Target ~10-25 shortlist papers. Fewer misses coverage; more is unwieldy.
• Note what you couldn't read. abstract-only papers are mentioned in the review with a note that full-text was unavailable — don't pretend you read them.

Token budget

| Phase | Tokens (rough) | |---|---| | search (50 results with abstracts) | 15K-30K | | screen (agent skim) | 15K-30K in, minimal out | | fetch plan | 2K | | read (15 papers via arxiv-analyze md tier) | 150K-300K | | synthesis | 5K-15K |

Total: a real review burns 200K-400K tokens. Plan accordingly — split phases across sessions if your context budget is tight.

The One Thing

End every status report with the single most impactful next action for the current phase — not a status summary. Example: "Phase: screened (12 shortlisted). Next: run fetch, then start with paper 2501.11120 — it's cited by 6 others in your shortlist, so reading it first will anchor the rest."

Requirements

• Python 3.11+ (stdlib only)
• scholar-search skill installed as a sibling (same skills/ directory)
• Optional: arxiv-search and arxiv-analyze skills for arxiv integration (install from https://github.com/dsebastien/ai-skill-arxiv)
• Optional: OPENALEX_EMAIL env var for polite-pool access (no API key needed; OpenAlex is free)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dsebastien
• Source: dsebastien/ai-skill-scholar
• License: MIT

Install and usage instructions live in the source repository linked above.