| name | literature-review |
| description | Search, summarize, and synthesize economics literature. find research gaps, position your contribution. |
Literature-Review
Purpose
This skill helps economists conduct rigorous literature reviews: actively searching academic databases, building reference list, summarizing individual papers, synthesizing views and evidence across the literature, identifying research gaps, and positioning the user's contribution.
When to Use
- Finish phase 1 of the workflow (Research Question Scoping) and handoff to phase 2
- Start a literature review for a new project
- Draft the Related Literature section of a paper
- Find prior work to cite in an introduction
- Respond to a referee's request to engage more with the literature
Step 0: OpenAlex API Key Setup
Before starting any search, Claude must run this check every time the skill is invoked.
0.1 Read the Config File
Read the file [plugin_root]/.env.
The file should contain:
OPENALEX_API_KEY=your-api-key-here
0.2 Decision Tree
If the key is absent / still a placeholder:
Use AskUserQuestion to prompt the user:
"请按以下步骤获取 OpenAlex API Key，然后粘贴到这里：
① 访问 https://openalex.org/settings/api → 注册 / 登录账号
② 在账户设置页面生成 API Key → Copy
③ 将 API Key 粘贴至此"
- If the user provides a key: Append
OPENALEX_API_KEY=<value> to [plugin_root]/.env (standard KEY=VALUE format, one entry per line — not JSON). If the key already exists in the file, replace that line in place. Confirm: "✅ API Key 已保存，下次不再询问。" Then proceed.
- If the user skips: Warn — "⚠️ 未配置 API Key，OpenAlex 请求将受到严格限速，文献检索结果可能不完整。" — then continue (omit
&api_key=... from all URLs).
If the key is already a valid non-placeholder string: proceed silently — no prompt needed.
Step 1: Confirm the Research Question
This skill is Phase 2 of the empirical research workflow. The confirmed research question from Phase 1 is the essential input. Claude must follow this decision tree:
1.1 Check for a Confirmed Research Question
Scan your working directory to find the research-question.md document produced by the question skill:
研究问题：…
研究对象：…
识别层级：Level [A/B/C]
数据来源（初步）：…
研究假设：H₀ / H₁ / 预期方向
Then ask only the two supplementary questions:
"文献搜索还需要以下信息：
① 文献时间范围：全部年份 / 2000年后 / 最近十年？
② 有没有 2–3 篇奠基性论文（作者 + 标题或 DOI）可作为搜索锚点？没有也可以直接跳过。"
Once the user answers (or skips ②), proceed directly to Step 2.
1.2 Back to /question command
If no research-question.md document is found (user jumped directly to literature review without going through Phase 1):
Run the /question command immediately:
"在开展文献检索之前，需要先明确研究问题。我将运行 /question 命令帮助你完成这一步。"
Run the full /question workflow. After the user confirms the research question, return to this skill at Step 1.1 (the block will now be present) and continue.
Step 2: Generate a Comprehensive Keyword Matrix
Before searching, expand the user's topic into a full keyword matrix. Do not rely on a single phrase.
2.1 Generate Three Layers × Multiple Synonyms
| Layer | Primary terms | Synonyms / variants |
|---|
| Core concept | e.g. "minimum wage" | "wage floor", "wage policy", "statutory wage" |
| Outcome | e.g. "employment" | "jobs", "labor demand", "hours worked", "unemployment" |
| Method | e.g. "difference-in-differences" | "DiD", "diff-in-diff", "natural experiment", "quasi-experiment" |
| Context | e.g. "United States" | "US", "OECD", "developing countries", "low-income countries" |
2.2 Generate JEL Code Candidates
Map the research question to 3–6 relevant JEL codes (e.g., J31 = Wage Level and Structure; J23 = Labor Demand; C21 = Cross-Sectional Models). These could be used in EconLit and NBER searches.
2.3 Construct Boolean Query Variants
Generate at least 5 distinct query strings covering different angles:
Query 1 (main): ("minimum wage" OR "wage floor") AND (employment OR "labor demand")
Query 2 (method): ("minimum wage") AND ("difference-in-differences" OR "DiD" OR "natural experiment")
Query 3 (mechanism): ("minimum wage") AND (prices OR "profit margins" OR "labor productivity")
Query 4 (subgroup): ("minimum wage") AND ("low-skilled" OR "teen employment" OR "small business")
Query 5 (recent): ("minimum wage") AND (employment) after:2018
Step 3: Execute Multi-Round Active Search
Claude must actively execute these searches autonomously.
Primary search engine: OpenAlex API via WebFetch.
Supplementary & optional sources: Semantic Scholar, NBER, SSRN and arXiv.
Search rules:
-
Use primary search engine for all rounds.
-
Use supplementary sources only for following purposes:
- More Coverage: OpenAlex results are clearly thin or missing a literature branch
- Cross-validation: Use Semantic Scholar to verify that key metadata (title, authors, year, citation count, DOI) returned by OpenAlex is accurate and not erroneous
- Working paper capture: recent NBER / SSRN papers are likely relevant
-
Semantic Scholar cross-validation is mandatory for all Core-tier papers — even when OpenAlex coverage is strong.
-
For Background / Methodological papers, cross-validation may be skipped if OpenAlex data looks internally consistent.
OpenAlex API
All OpenAlex requests follow the base URL https://api.openalex.org. Append &api_key=YOUR_KEY (from .env) to every request. Without a key, requests are very limited but still functional for moderate searches.
⚠️ 网络受限处理规则（Cowork 沙箱 WebFetch 被封锁时）
在发出第一个 OpenAlex WebFetch 请求后，若收到网络错误（连接超时、403、域名无法解析等），必须严格遵循以下顺序，禁止直接跳转到网络搜索兜底：
Step A — 立即生成本地运行脚本，并提示用户在自己的电脑上执行
向用户说明：
"Cowork 沙箱无法访问 OpenAlex API（网络受限）。请在您的本地终端运行以下 Python 脚本获取文献数据，然后将输出结果粘贴到这里或保存为文件后上传。
运行方式：
pip install requests
python fetch_openalex.py
```"*
随后生成完整的本地脚本 fetch_openalex.py（写入工作区）：
"""
OpenAlex 文献获取脚本 — 在本地终端运行
生成文件：openalex_results.json
"""
import requests, json, time
API_KEY = "YOUR_OPENALEX_API_KEY"
BASE = "https://api.openalex.org"
HEADERS = {"User-Agent": "mailto:your@email.com"}
def fetch(url):
if API_KEY:
url += f"&api_key={API_KEY}" if "?" in url else f"?api_key={API_KEY}"
r = requests.get(url, headers=HEADERS, timeout=30)
r.raise_for_status()
time.sleep(0.5)
return r.json()
queries = [
f"{BASE}/works?search=KEYWORD_1&filter=type:article&sort=cited_by_count:desc&per_page=25",
f"{BASE}/works?search=KEYWORD_2&filter=type:article&sort=cited_by_count:desc&per_page=25",
f"{BASE}/works?search=KEYWORD_3&filter=type:article,publication_year:>2018&sort=cited_by_count:desc&per_page=25",
]
results = {}
for i, url in enumerate(queries, 1):
print(f"查询 {i}/{len(queries)}: {url[:80]}...")
try:
data = fetch(url)
results[f"query_{i}"] = data.get("results", [])
print(f" → 获取 {len(results[f'query_{i}'])} 篇")
except Exception as e:
print(f" ✗ 失败：{e}")
results[f"query_{i}"] = []
with open("openalex_results.json", "w", encoding="utf-8") as f:
json.dump(results, f, ensure_ascii=False, indent=2)
print("\n✅ 结果已保存至 openalex_results.json")
print(f" 总计获取：{sum(len(v) for v in results.values())} 篇文献条目")
脚本中的占位符 KEYWORD_1/2/3 由 Claude 在生成时替换为 Step 2 生成的实际关键词查询字符串。
Step B — 等待用户上传 / 粘贴结果
等待用户执行后返回结果。接受以下任意格式：
- 上传
openalex_results.json 文件
- 粘贴 JSON 文本到对话中
- 粘贴终端输出的纯文本
收到数据后，正常解析并继续 Round 1 的候选文献筛选流程。
Step C — 仅当用户明确表示无法在本地运行时，才启用兜底方案
若用户回复"无法运行"或"没有 Python 环境"，此时才可使用以下兜底（按优先级）：
- 通过
WebSearch 搜索 Google Scholar / 学术搜索（降级，覆盖度有限）
- 提示用户在浏览器手动访问
https://openalex.org/works?search=... 并将页面内容粘贴
⛔ 严禁行为： 在未提示用户尝试本地运行之前，直接切换到网络搜索兜底。
Important — Two-step author lookup: Never filter by author name directly (names are ambiguous). Always first search for the author's OpenAlex ID, then filter works by that ID.
Pattern 1 — Works keyword search
https://api.openalex.org/works?search=minimum+wage+employment+difference-in-differences&filter=type:article&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
Key filter modifiers (combine with comma = AND):
type:article — journal articles onlypublication_year:2015-2024 — year rangepublication_year:>2018 — after a yearis_oa:true — open access onlytopics.id:T10325 — by OpenAlex topic IDauthorships.institutions.country_code:US — by country
Key sort options:
cited_by_count:desc — most cited firstpublication_year:desc — most recent firstrelevance_score:desc — best text match first (default when search= used)
Pattern 2 — Author ID lookup (step 1 of 2)
https://api.openalex.org/authors?search=David+Card&per_page=5&api_key=YOUR_KEY
Parse the response: pick the top result's id field (e.g., https://openalex.org/A5023888391). Extract the short ID: A5023888391.
Pattern 3 — Works by a specific author (step 2 of 2)
https://api.openalex.org/works?filter=authorships.author.id:A5023888391&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
Pattern 4 — Retrieve a single work by DOI or OpenAlex ID
https://api.openalex.org/works/doi:10.2307/2118030?api_key=YOUR_KEY
https://api.openalex.org/works/W2170494700?api_key=YOUR_KEY
Pattern 5 — Forward citations (papers that cite a given work)
https://api.openalex.org/works?filter=cites:W2170494700&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
Pattern 6 — Backward references (papers cited by a given work)
Retrieve the work directly (Pattern 4) and read its referenced_works array. Then batch-fetch metadata:
https://api.openalex.org/works?filter=openalex_id:W111|W222|W333&per_page=50&api_key=YOUR_KEY
(Use pipe | to OR up to 100 IDs in one request.)
Pattern 7 — Search with Boolean operators
https://api.openalex.org/works?search=(minimum+wage+OR+wage+floor)+AND+(employment+OR+labor+demand)&filter=type:article,publication_year:2000-2024&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
Use AND, OR, NOT (uppercase).
Round 1: Broad Sweep (Target: 25+ candidate papers)
Primay Source — OpenAlex API
Run at least 4–5 distinct queries. For each query variant:
- Keyword search (Pattern 1) with
sort=cited_by_count:desc → capture top 25 results
- Author lookup (Patterns 2+3) for 2–3 key authors identified in results → capture their top works
- Combined filter adding
publication_year:>2018 → capture recent papers
Example sequence:
# Query 1: Main search
https://api.openalex.org/works?search=minimum+wage+employment&filter=type:article&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
# Query 2: Method-focused
https://api.openalex.org/works?search=minimum+wage+"difference-in-differences"&filter=type:article&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
# Query 3: Mechanism
https://api.openalex.org/works?search=minimum+wage+price+pass-through+profit+mechanism&filter=type:article&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
# Query 4: Recent papers
https://api.openalex.org/works?search=minimum+wage+employment&filter=type:article,publication_year:>2018&sort=cited_by_count:desc&per_page=25&api_key=YOUR_KEY
# Query 5: Author lookup → Card
https://api.openalex.org/authors?search=David+Card&per_page=3&api_key=YOUR_KEY
# → get author ID, then:
https://api.openalex.org/works?filter=authorships.author.id:AUTHOR_ID&sort=cited_by_count:desc&per_page=20&api_key=YOUR_KEY
Supplementary Sources
Semantic Scholar (for more coverage + cross-validation of OpenAlex data)
API Key Check (one-time per session):
Read .env and look for SEMANTIC_SCHOLAR_API_KEY.
-
If the key is absent or still a placeholder: Use AskUserQuestion to prompt:
"Semantic Scholar 可作为 OpenAlex 的补充来源（捕获遗漏文献或作交叉检验）。配置 API Key 可获得更高请求速率：
① 访问 https://www.semanticscholar.org/product/api → 申请 API Key (建议直接闲鱼搜索Semantic Scholar API 购买)
② 将 Key 粘贴至此；若暂无 Key，直接回车跳过（将使用公共接口，速率受限）。"
- If user provides key: append
SEMANTIC_SCHOLAR_API_KEY=<value> to .env (standard KEY=VALUE format — not JSON; replace existing line if present). Set flag SS_AUTH = True. Confirm: "✅ Semantic Scholar API Key 已保存。"
- If user skips: set flag
SS_AUTH = False. Note: "⚠️ 将使用 Semantic Scholar 公共接口，调用速率受限。"
-
If a valid key already exists in .env: Set SS_AUTH = True, proceed silently.
Branch A — With API Key (SS_AUTH = True): use Python via Bash
Authentication is via HTTP header x-api-key, which WebFetch cannot set. Use requests in a Bash script instead.
import requests, json, time
SS_KEY = "YOUR_SEMANTIC_SCHOLAR_API_KEY"
headers = {"x-api-key": SS_KEY}
BASE = "https://api.semanticscholar.org/graph/v1"
FIELDS = "title,year,authors,abstract,citationCount,externalIds,openAccessPdf,venue,publicationTypes"
params = {
"query": "minimum wage employment difference-in-differences",
"fields": FIELDS,
"year": "2000-2024",
"sort": "citationCount",
"limit": 100,
}
r = requests.get(f"{BASE}/paper/search/bulk", headers=headers, params=params)
results = r.json()
papers = results.get("data", [])
time.sleep(1)
doi = "10.2307/2118030"
r2 = requests.get(
f"{BASE}/paper/DOI:{doi}",
headers=headers,
params={"fields": FIELDS},
)
paper = r2.json()
time.sleep(1)
ss_id = "paper_id_here"
r3 = requests.get(
f"{BASE}/paper/{ss_id}",
headers=headers,
params={"fields": FIELDS},
)
Key response fields:
| Field | Description |
|---|
paperId | Semantic Scholar internal ID |
externalIds.DOI | DOI — use to match against OpenAlex entries |
externalIds.ArXiv | arXiv ID (if preprint available) |
citationCount | Total citations in Semantic Scholar's index |
abstract | Full abstract text (no reconstruction needed, unlike OpenAlex) |
venue | Journal or conference name |
openAccessPdf.url | Direct PDF link if open access |
publicationTypes | e.g., ["JournalArticle"], ["Preprint"] |
Branch B — Without API Key (SS_AUTH = False): use WebFetch
The public Semantic Scholar API works without authentication but shares a global rate limit. Use WebFetch for individual calls and space them out.
# Keyword search (rate-limited)
https://api.semanticscholar.org/graph/v1/paper/search/bulk?query=minimum+wage+employment+difference-in-differences&fields=title,year,authors,abstract,citationCount,externalIds,venue&sort=citationCount
# Cross-validate a specific paper by DOI
https://api.semanticscholar.org/graph/v1/paper/DOI:10.2307/2118030?fields=title,year,authors,citationCount,abstract,externalIds,venue
# Cross-validate by arXiv ID
https://api.semanticscholar.org/graph/v1/paper/ARXIV:2301.05345?fields=title,year,authors,citationCount,abstract,externalIds
⚠️ Rate limit discipline (no key): Issue at most 5–8 WebFetch calls to Semantic Scholar per round. Space calls at least 3–5 seconds apart. If a 429 status is returned, pause and retry after 30 seconds.
What to do with Semantic Scholar results in Round 1:
-
Coverage gap check — run 1–2 keyword searches using queries that diverged from OpenAlex (e.g., different phrasing). Flag any papers in the top 20 Semantic Scholar results that are absent from the OpenAlex candidate list with citation count > 20.
-
Abstract retrieval — if OpenAlex abstract_inverted_index is missing for a Core candidate, use Semantic Scholar to retrieve the full plain-text abstract directly.
-
Cross-validation of OpenAlex metadata — for each paper, look it up on Semantic Scholar by DOI or title and compare the following fields against the OpenAlex record:
| Field | Check |
|---|
| Title | Exact match? Flag truncations or garbled characters |
| Authors | Same author list and order? Flag missing/extra authors |
| Year | Published year matches? |
| Journal / Venue | Consistent journal name? |
| DOI | DOI present and resolves to the same paper? |
If a discrepancy is found: note it in the paper entry with a ⚠️ Data discrepancy tag and flag for manual verification. Do not silently carry forward potentially incorrect OpenAlex metadata into the final literature table.
arXiv (for latest economics preprints and working papers)
What to do with arXiv results in Round 1:
- Recency capture — collect papers from the last 12 months not yet published in journals. These often represent the methodological frontier.
- Preprint–publication link — for papers already in OpenAlex, check
externalIds.ArXiv (via Semantic Scholar) or search arXiv ID to confirm if the working paper version has additional material.
Search patterns:
# Economics preprints — keyword search, sorted by newest first
https://export.arxiv.org/api/query?search_query=all:minimum+wage+employment&start=0&max_results=25&sortBy=submittedDate&sortOrder=descending
# Restrict to econ.* categories (General Economics, Econometrics, Labour)
https://export.arxiv.org/api/query?search_query=cat:econ.*+AND+all:minimum+wage+employment&start=0&max_results=25&sortBy=submittedDate&sortOrder=descending
# Retrieve most-relevant results (default relevance ranking)
https://export.arxiv.org/api/query?search_query=all:minimum+wage+"difference-in-differences"&start=0&max_results=25
# Fetch a specific paper by arXiv ID
https://export.arxiv.org/api/query?id_list=2301.05345
NBER (for latest working papers)
What to do with NBER results in Round 1:
- Recency capture, NBER working papers from last 12 months that are absent from OpenAlex.
NBER is the most authoritative working paper repository in economics. It does not have a structured public search API, so use a combination of WebSearch and WebFetch for individual paper pages.
Step 1 — Discover via WebSearch:
site:nber.org/papers "minimum wage" employment 2023 2024
site:nber.org/papers "minimum wage" inequality recent
Step 2 — Fetch individual paper metadata via WebFetch:
# NBER paper page (contains title, authors, abstract, publication date, JEL codes)
https://www.nber.org/papers/w31234
# NBER also exposes a JSON metadata endpoint — faster than HTML parsing:
https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/_id/w31234
SSRN (for latest working papers)
What to do with SSRN results in Round 1:
- Recency capture, SSRN working papers from last 12 months that are absent from OpenAlex.
- Topics involves intersection of law and economics.
SSRN is a preprint repository especially strong in law, finance, and applied economics. It does not offer a structured public API; use WebSearch as the primary discovery method.
Step 1 — Discover via WebSearch:
site:papers.ssrn.com "minimum wage" employment effects 2023 2024
site:papers.ssrn.com "minimum wage" causal inference difference-in-differences
site:papers.ssrn.com "minimum wage" inequality labour market 2024
Step 2 — Fetch individual paper metadata via WebFetch:
# SSRN abstract page — contains title, authors, abstract, date, download count
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4567890
⚠️ SSRN pages can be slow or return partial content. If WebFetch returns incomplete HTML, limit to 2–3 SSRN fetches per round and rely on WebSearch snippets for basic metadata.
Record every returned paper in a running list: Author(s), Year, Title, Source, OpenAlex ID (if available), Citation Count.
Round 2: Citation Network Expansion (Target: +5–10 papers)
All searches in this round use OpenAlex only.
Seed Paper Selection
Do not use citation count alone. Select 5 seed papers as follows:
- Top 3 from the Round 1 candidate list ranked by
cited_by_count — these are the seminal works whose citation network is most productive to explore.
- Top 2 most recently published candidates from Round 1 — these anchor the frontier and surface newer work not yet highly cited.
If fewer than 5 papers exist in Round 1, use all of them.
For each seed paper, crawl their citation network using OpenAlex.
Forward Citations
For each seed paper, retrieve works that cite it, sorted by citation count:
https://api.openalex.org/works?filter=cites:SEED_OPENALEX_ID&sort=cited_by_count:desc&per_page=25&select=id,title,authorships,publication_year,cited_by_count,primary_location&api_key=YOUR_KEY
Collect the top 10 citing works per seed paper.
Backward References
# Step 1: retrieve the seed paper's reference list
https://api.openalex.org/works/SEED_OPENALEX_ID?select=id,referenced_works&api_key=YOUR_KEY
# Step 2: batch-fetch metadata for up to 50 references
https://api.openalex.org/works?filter=openalex_id:W111|W222|W333&select=id,title,authorships,publication_year,cited_by_count,primary_location&per_page=50&api_key=YOUR_KEY
Inclusion Threshold
Add a paper to the candidate list if it meets at least one of:
cited_by_count > 5, or- Published within the last 3 years, or
Deduplication and Stopping Rule
Deduplication: after each seed paper's expansion, remove any paper whose OpenAlex ID already appears in the Round 1 candidate list. Work only with net-new entries.
Stopping rule: if across any 3 consecutive seed papers, forward citation expansion yields fewer than 2 new papers meeting the inclusion threshold, stop early — further expansion has diminishing returns.
Update the Candidate List
Append all qualifying net-new papers to the running list using the same fields as Round 1:
Author(s) | Year | Title | OpenAlex ID | Citation Count
Step 4: Triage and Organize Papers
4.1 Fetch Abstracts for Triage
For each candidate paper, retrieve the abstract if not already fetched.
Via OpenAlex (preferred):
https://api.openalex.org/works/W2170494700?select=id,title,abstract_inverted_index,authorships,publication_year,cited_by_count,primary_location&api_key=YOUR_KEY
Note: OpenAlex stores abstracts as abstract_inverted_index (a word→position map). Reconstruct the abstract by sorting positions and joining words. Example Python reconstruction:
inv = work["abstract_inverted_index"]
abstract = " ".join(w for w, _ in sorted(
((w, p) for w, positions in inv.items() for p in positions),
key=lambda x: x[1]
))
Via Semantic Scholar (fallback):
https://api.semanticscholar.org/graph/v1/paper/[PAPER_ID]?fields=title,abstract,authors,year,citationCount,venue
Assign each paper to one category:
- Core (directly relevant, must cite and engage)
- Background (motivates the topic, cite briefly)
- Methodological (uses a technique you adopt)
- Contradictory (finds different results — must address)
- Excluded (off-topic, weak identification, or low-quality)
After triage, proceed to 4.2 for Core papers and 4.3 for all other retained categories.
4.2 Full-Text Acquisition for Core Papers
For papers classified as Core, go beyond the abstract and acquire the full PDF. Limit this to a maximum of 10 papers to keep the review tractable.
Priority Order for PDF Sources
Work through these sources in order until a PDF is obtained:
① OpenAlex open-access URL (fastest)
Retrieve the paper's open-access status from OpenAlex:
https://api.openalex.org/works/OPENALEX_ID?select=id,title,open_access,primary_location,locations&api_key=YOUR_KEY
Check open_access.is_oa. If true, use open_access.oa_url as the direct PDF link.
② Semantic Scholar open-access PDF
If OpenAlex has no OA URL, check Semantic Scholar:
# With API key (SS_AUTH = True)
GET https://api.semanticscholar.org/graph/v1/paper/DOI:{doi}?fields=openAccessPdf,externalIds
Header: x-api-key: YOUR_SS_KEY
# Without API key (SS_AUTH = False) — WebFetch:
https://api.semanticscholar.org/graph/v1/paper/DOI:{doi}?fields=openAccessPdf,externalIds
Use openAccessPdf.url if present.
③ Source-specific direct URLs
If externalIds reveal a known working paper source, use the known URL pattern:
| Source | URL pattern |
|---|
| arXiv | https://arxiv.org/pdf/{arxiv_id} |
| NBER | https://www.nber.org/system/files/working_papers/w{NNNN}/w{NNNN}.pdf |
| SSRN | Fetch abstract page → find download link in HTML |
④ WebFetch the DOI landing page
As a last open-access check, fetch the DOI resolver page and look for an OA download link:
https://doi.org/{doi}
Some journals (AEA, NBER, IZA) serve open-access PDFs directly from the DOI redirect.
Downloading and Reading the PDF
Once a PDF URL is identified, download it using Python via Bash and then read it with the Read tool:
import requests, re, os
pdf_url = "https://arxiv.org/pdf/2301.05345"
paper_slug = "card_krueger_1994"
save_path = f"/sessions/lucid-nifty-goodall/{paper_slug}.pdf"
r = requests.get(pdf_url, timeout=60, headers={"User-Agent": "Mozilla/5.0"})
if r.status_code == 200 and "pdf" in r.headers.get("Content-Type", ""):
with open(save_path, "wb") as f:
f.write(r.content)
print(f"Saved: {save_path}")
else:
print(f"Failed: HTTP {r.status_code}, Content-Type: {r.headers.get('Content-Type')}")
Then read with the Read tool. For these core papers, read each in sections — Introduction + Conclusion first, then Data, then Empirical Strategy, then Results.
When PDF Is Unavailable (Paywalled)
If no open-access version is found after all steps above:
- Note
[PAYWALLED] in the candidate list for this paper.
- Proceed with the abstract-only summary template (Section 4.3B).
- Flag the paper for the user with a note: "Full text unavailable — please upload the PDF manually if you have institutional access."
4.3 Paper Summary Templates
Use the appropriate template based on paper category and whether full text was obtained.
4.3A Full-Text Summary — Core Papers
Use this template when the full PDF has been read. It is the most detailed version and forms the primary input to Step 5.
## [Author(s)] ([Year])
**Title:** [Full title]
**Published in:** [Journal / Working Paper Series, Volume/Number]
**JEL Codes:** [e.g., J31, C21]
**Citation count:** [N]
**OpenAlex ID:** [W...]
**Full text read:** Yes / Abstract only (paywalled)
### Research Question
[One sentence stating the causal question.]
### Data
- **Source:** [Dataset name, e.g., CPS, NLSY, administrative records]
- **Period:** [Years covered]
- **Sample:** [N observations; unit of analysis, e.g., county-month, individual]
- **Geography:** [Country / region / states]
- **Key variables:** [Outcome, treatment, main controls — with exact names if possible]
- **Sample restrictions:** [Any non-obvious exclusions applied]
### Identification Strategy
- **Method:** [DiD / IV / RDD / RCT / Matching / OLS]
- **Source of variation:** [What drives the treatment? e.g., state-level minimum wage changes]
- **Key assumption:** [e.g., parallel trends, exclusion restriction, continuity at threshold]
- **Assumption test(s):** [What did the authors test? What did they find?]
- **First-stage / relevance:** [For IV: F-statistic; for RDD: bandwidth, density test result]
### Main Findings
1. [Key result with coefficient magnitude + units + statistical significance, e.g., "A $1 MW increase reduces teen employment by 0.7 pp (se=0.2), significant at 5%"]
2. [Second key result]
3. [Third key result or primary heterogeneity finding]
### Robustness Checks
| Check | Result | Location |
|-------|--------|----------|
| [e.g., Alternative control group] | [Consistent / Attenuated / Null] | [Table A1] |
| [e.g., Placebo test] | [...] | [...] |
### Limitations
- **External validity:** [Main concern about generalisability]
- **Internal validity:** [Main remaining threat, e.g., parallel trends not perfectly supported]
- **Data:** [Key measurement or coverage limitation]
### Relevance to Your Project
[2–3 sentences: How does this paper shape your identification strategy, data choices, or expected results?]
4.3B Abstract-Only Summary — non-Core / Paywalled Core
Use this shorter template for non-Core papers or Core papers where full text is unavailable.
## [Author(s)] ([Year])
**Title:** [Full title]
**Published in:** [Journal / Working Paper Series]
**Category:** Background / Methodological / Contradictory / Core (paywalled)
**Citation count:** [N] | **OpenAlex ID:** [W...]
**Research Question:** [One sentence]
**Method:** [DiD / IV / RDD / OLS / other]
**Key finding:** [One sentence with magnitude if available]
**Relevance:** [One sentence — why is this paper in the list?]
Step 5: Narrative Synthesis
After summarizing relevant papers, write a synthesis, but Do not just list papers. Economics papers use narrative paragraphs. Follow this structure:
Paragraph 1 — Establish the big picture:
"A large literature examines the effect of X on Y. Early work using OLS found [result], but identification concerns motivated subsequent quasi-experimental research (Author A, Year; Author B, Year). Taken together, this literature establishes that [broad conclusion]."
Paragraph 2 — Highlight consensus:
"There is now broad agreement that [specific finding]. [Author A (Year)] shows [result] using [method] in [context]. [Author B (Year)] confirms this using [different method/data], finding [comparable result]."
Paragraph 3 — Highlight disagreements (must engage, not ignore):
"[Author C (Year)], however, finds [contradictory result]. This discrepancy may reflect [methodological difference / data difference / context difference]. We return to this issue in Section X."
Paragraph 4 — Identify gaps that motivate your paper:
"Despite this progress, two questions remain unanswered. First, [Gap 1]. Second, [Gap 2]. Our paper contributes by [your approach]."
Research Gaps Taxonomy
| Gap type | Example |
|---|
| Geographic | Existing evidence is US-only; no developing country evidence |
| Temporal | Studies focus on short-run effects; long-run unknown |
| Subgroup | Effects on high-skill workers unstudied |
| Mechanism | What drives the effect? (price, profit, productivity?) |
| Methodological | All existing papers use OLS; no credible IV evidence |
| Data | All use survey data; no administrative records |
| Policy counterfactual | Effects at lower/higher magnitudes unknown |
Paragraph 5 — Position Your Contribution
"This paper makes [N] contributions to the literature on [topic]. First, we [contribution 1] — while [Author A (Year)] studies [X], our paper is the first to [Y]. Second, we use [data/method] which allows us to [identify/measure] [Z], addressing [limitation in prior work]. Third, our findings [extend/challenge/reconcile] the evidence from [Author B (Year)] by showing [how/why]."
Step 6: Deliver a Full Reference List
After completing the search and synthesis, produce a full BibTeX reference list for all Core and Background papers cited.
Retrieve structured metadata via OpenAlex:
https://api.openalex.org/works/W2170494700?select=id,doi,title,authorships,publication_year,primary_location,biblio&api_key=YOUR_KEY
The doi field, primary_location.source.display_name (journal name), biblio.volume, biblio.issue, and biblio.first_page / biblio.last_page provide everything needed for BibTeX.
BibTeX format:
@article{CardKrueger1994,
author = {Card, David and Krueger, Alan B.},
title = {Minimum Wages and Employment: A Case Study of the Fast-Food
Industry in New Jersey and Pennsylvania},
journal = {American Economic Review},
year = {1994},
volume = {84},
number = {4},
pages = {772--793},
doi = {10.2307/2118030}
}
Use @unpublished for working papers not yet published in a journal.
Final Output: literature-review-report.md
After finishing all steps above, write the final literature review report and save it as literature-review-report.md in the working directory.
Mandatory post-delivery reminder:
After presenting the report link to the user, you must append the following notice:
⚠️ 学术诚信提示：请用其他大模型交叉核验文献
本报告已通过 OpenAlex + Semantic Scholar 交叉检验文献的元数据（标题、作者、期刊、年份）。尽管如此，AI 生成的文献列表仍可能存在幻觉。
强烈建议将文献列表粘贴至 ChatGPT、Gemini 或其他独立大模型，并询问：
"请逐条检验以下文献是否真实存在，标题、作者、期刊、年份是否准确无误。"
Example format of literature review report:
# Literature Review Report
**Topic:** Effects of Minimum Wage on Employment
**Source:** OpenAlex(primary), Semantic Scholar/NBER/arXiv/SSRN(supplementary)
**Date Range:** 1990-2026
**Papers reviewed:** 52 total → 12 Core, 9 Background, 6 Methodological, 4 Contradictory, 21 Excluded
---
## Core Papers
### Card and Krueger (1994)
**Title:** Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania
**Published in:** *American Economic Review*, 84(4), 772–793
**JEL Codes:** J31, J38
**Citation count:** 8,214 (OpenAlex) | 9,102 (Semantic Scholar)
**OpenAlex ID:** W2133060252
**Full text read:** Yes (NBER PDF)
**Research Question:**
**Data**
**Identification Strategy**
**Main Findings**
**Limitations**
**Relevance to Your Project**
---
## Non-Core Papers
### Neumark and Wascher (2000)
**Title:** Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania: Comment
**Published in:** *American Economic Review*, 90(5), 1362–1396
**Category:** Contradictory
**Citation count:** 1,247 | **OpenAlex ID:** W2048872744
**Full text read:** Yes (user uploaded)
**Research Question:**
**Method:**
**Key finding:**
**Relevance:**
---
## Narrative Synthesis
A large empirical literature examines the effect of minimum wages on employment......
The modern quasi-experimental literature, launched by Card and Krueger (1994), challenged this consensus using a difference-in-differences design......
Despite this progress, three questions remain underexplored......
This paper makes [N] contributions to the literature on [topic]. First...
---
## References
Autor, D. H., Manning, A., & Smith, C. L. (2016). The contribution of the minimum wage to US wage inequality over three decades: A reassessment. *American Economic Journal: Applied Economics*, 8(1), 58–99. https://doi.org/10.1257/app.20140073
Common Pitfalls
- ❌ Presenting a search framework instead of actually running searches
- ❌ Stopping after one round of search queries — always do three rounds
- ❌ Filtering by author name directly (use two-step ID lookup)
- ❌ Only citing papers that support your argument
- ❌ Ignoring contradictory findings
- ❌ Confusing correlation with causation when describing OLS results
- ❌ Citing papers you have not read (mischaracterizing findings)
- ❌ Vague gap identification ("more research is needed") — be specific
- ❌ Skipping the post-delivery integrity reminder — always tell the user to cross-check the final reference list with ChatGPT / Gemini or another independent LLM before citing. AI-generated reference lists carry hallucination risk even after API-based cross-validation; a second-model sanity check is the last line of defence for academic integrity.
