Install
$ agentstack add skill-mathews-tom-armory-literature-review ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Literature Review
Systematic discovery, extraction, and synthesis of academic research on a defined topic.
When to use this skill vs. others
| Need | Skill | | ----------------------------------------------------- | ---------------------------------- | | Survey a research area, synthesize multiple papers | literature-review (this skill) | | Critique a single paper's methodology and claims | research-critique | | Audit a manuscript's formatting, structure, citations | manuscript-review | | Verify a manuscript's numbers trace to code | manuscript-provenance | | Search arXiv for papers matching a query | arxiv-search (utility) |
Workflow
Phase 1: Scope Definition
Before searching, establish the review boundaries:
- Research question — What specific question does the review answer? Vague topics produce vague reviews. "What techniques exist for X" is weaker than "How do methods for X compare on metric Y across domains Z?"
- Inclusion criteria — Define what counts:
- Date range (e.g., 2020–present)
- Publication type (peer-reviewed, preprints, both)
- Domains/categories (e.g., cs.CL, cs.AI)
- Minimum relevance threshold
- Exclusion criteria — Define what does not count:
- Tangentially related work
- Non-primary sources (blog posts, tutorials) unless explicitly included
- Duplicate or superseded versions
- Expected output — What form should the review take? Narrative synthesis, tabular comparison, gap analysis, annotated bibliography, or related-work section?
Present the scope to the user for confirmation before proceeding.
Phase 2: Search & Discovery
Execute searches across available sources. Use multiple queries with varying specificity to avoid single-query blind spots.
Primary source: arXiv (via arxiv-search utility)
uv run --with arxiv python scripts/arxiv_search.py "QUERY" --max-results 30 --sort-by relevance
Vary queries systematically:
- Broad topic query:
"retrieval augmented generation" - Field-scoped query:
ti:retrieval AND abs:generation AND cat:cs.CL - Author-anchored query:
au:lewis AND abs:retrieval(when key authors are known) - Recency query: same terms with
--sort-by submitted
Secondary sources (via web search/fetch):
- Semantic Scholar API:
https://api.semanticscholar.org/graph/v1/paper/search?query=QUERY&limit=20&fields=title,authors,abstract,year,citationCount,externalIds - Google Scholar (via web search):
site:scholar.google.com QUERY - Connected Papers (for citation graph exploration):
https://www.connectedpapers.com/search?q=QUERY
Snowball strategy:
- Forward snowball: find papers that cite a key paper (Semantic Scholar citations endpoint)
- Backward snowball: follow the references of key papers
- Use citation count as a signal for influence, not quality
Phase 3: Screening & Filtering
For each discovered paper, apply the inclusion/exclusion criteria from Phase 1.
Produce a screening table:
| # | ID | Title | Authors | Year | Relevant? | Reason | | --- | ---------- | ----------- | ------------- | ---- | --------- | ------------------------------- | | 1 | 2301.07041 | Paper Title | Author et al. | 2023 | Yes | Directly addresses RQ | | 2 | 2302.12345 | Other Paper | Author B | 2023 | No | Tangential — focuses on X not Y |
Rules:
- Screen on title + abstract first. Read full paper only for borderline cases.
- When uncertain, include. It is cheaper to drop a paper later than to miss it.
- Track exclusion reasons — they inform the review's limitations section.
- Flag papers that appear in multiple search queries as likely high-relevance.
Phase 4: Data Extraction
For each included paper, extract a structured record:
- id: "2301.07041"
title: "Paper Title"
authors: ["Author One", "Author Two"]
year: 2023
venue: "NeurIPS 2023"
research_question: "How does X affect Y?"
methodology: "Controlled experiment with N=1000"
key_findings:
- "Finding 1 with quantitative result"
- "Finding 2 with effect size"
limitations: "Single-domain evaluation"
relevance_to_rq: "Directly compares methods A and B on metric Y"
citation_count: 142
Extraction discipline:
- Record what the paper demonstrates, not what it claims to demonstrate.
- Distinguish empirical findings (data-backed) from interpretive claims (author's framing).
- Note methodology details that enable cross-paper comparison (datasets, metrics, baselines).
- If the paper is available via
pdf_url, read it for extraction. Do not extract from abstracts alone for included papers.
Phase 5: Synthesis
Transform extracted records into structured analysis. The synthesis method depends on the output format requested in Phase 1.
Thematic synthesis — Group papers by theme, approach, or finding:
- Identify recurring themes across papers
- Note where papers agree, disagree, or address different aspects
- Highlight methodological trends (what approaches are gaining/losing traction)
Comparative synthesis — Build comparison tables:
| Method | Paper(s) | Dataset | Metric | Result | Limitations | | -------- | -------- | ------- | ------ | ------ | --------------- | | Method A | [1], [3] | D1 | F1 | 0.85 | Domain-specific | | Method B | [2], [4] | D1, D2 | F1 | 0.82 | Requires X |
Chronological synthesis — Map the evolution of the field:
- What was the state of knowledge at time T?
- What shifted and why?
- Where is the field heading?
Gap analysis — Identify what is missing:
- Questions raised but not answered by existing work
- Methodological gaps (no one has tried approach X on problem Y)
- Domain gaps (studied in domain A but not B)
- Contradictions between studies that remain unresolved
Phase 6: Output
Produce the review document in the format specified in Phase 1.
Standard structure for a narrative review:
- Introduction — Research question, scope, and motivation for the review
- Search methodology — Databases searched, queries used, inclusion/exclusion criteria, screening results (N found → N screened → N included)
- Findings — Thematic or chronological synthesis of included papers
- Discussion — Cross-cutting analysis, trends, contradictions, gaps
- Limitations of this review — Search scope restrictions, potential biases, papers not accessible
- References — Full citation list for all included papers
Standard structure for a tabular review:
- Summary table (all included papers with key metadata)
- Comparison matrix (methods × metrics × results)
- Gap analysis table (questions × coverage)
- Reference list
Citation format: Default to Author et al. (Year) in-text with full references at the end. Adapt to the user's specified format (APA, Chicago, IEEE) if requested.
Quality Checks
Before delivering the review, verify:
- [ ] Every included paper has a structured extraction record
- [ ] Every claim in the synthesis is traceable to at least one extracted finding
- [ ] The gap analysis identifies at least one concrete research opportunity
- [ ] The review acknowledges its own limitations (search scope, access, biases)
- [ ] Citation format is consistent throughout
- [ ] No paper is cited that was excluded during screening
- [ ] Contradictions between papers are noted, not silently resolved by picking a side
Edge Cases
| Situation | Adaptation | | ----------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Very few papers found (100) | Tighten inclusion criteria. Consider limiting to top venues, recent years, or high-citation papers. Produce a scoping review rather than exhaustive review. | | User provides a paper list instead of a topic | Skip Phase 2 (Search). Start from Phase 3 (Screening) with the provided list. | | User wants a related-work section for their own paper | Tailor synthesis to position the user's contribution. Organize by approaches the user's work builds on, alternatives it competes with, and gaps it fills. | | No full-text access to key papers | Extract from abstracts and note the limitation. Do not fabricate methodology details. Flag which papers were abstract-only in the extraction records. | | Interdisciplinary topic | Search across multiple category prefixes. Note when different fields use different terminology for the same concept. | | User asks for a "quick" literature review | Reduce Phase 2 to a single search query, Phase 3 to title-only screening, Phase 4 to abstract-only extraction. Label the output as a preliminary survey, not a systematic review. |
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Mathews-Tom
- Source: Mathews-Tom/armory
- License: MIT
- Homepage: https://mathews-tom.github.io/armory/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.