Paper Search Skill

Unified paper search across arXiv, DBLP, OpenAlex, OpenReview (NeurIPS / ICLR / ICML), Semantic Scholar, and Crossref using ./scripts/search_papers.py. All sources are searched concurrently (in independent child processes) by default for maximum speed. Queries within one source remain serial. Returns results grouped by source.

When to use

Trigger this skill when the user asks things like:

• "Find papers on X published between 2023 and 2025."
• "Search NeurIPS / ICLR / ICML for work on X."
• "Get arXiv + Semantic Scholar results for X."
• "Show me recent prior art on X."

Inputs (all auto-inferred — NEVER ask the user for confirmation or clarification)

Derive these automatically from the user's message. Run the search immediately without asking for confirmation:

• query: Rephrase the user's question into a focused search phrase.
• start_year (int): If the user gives a year, use it directly. If they say "last 2 years", compute from today. Default: 2 years ago.
• end_year (int): Default: current year.
• max_papers (int): Number of results per source. Default: 10.
• sources: Which sources to query. Default: all 6 API sources plus the model-knowledge source, in this canonical order (highest-signal first, so the best results render before the user scrolls): semantic_scholar open_alex arxiv openreview crossref dblp model_knowledge. Only restrict sources if the user explicitly asks.

How to run

Preferred: call the CLI directly. The script lives at ${CLAUDE_PROJECT_DIR}/skills/paper_search/scripts/search_papers.py — invoke it by absolute path so the command works regardless of the current working directory (relying on cd scripts && ... breaks when the model is running from a different folder, which happens often).

For brevity in the examples below, treat $SEARCH as shorthand for that absolute path:

SEARCH="${CLAUDE_PROJECT_DIR}/skills/paper_search/scripts/search_papers.py"

Basic search:

python "$SEARCH" \
    --query "<QUERY>" \
    --start-year <YYYY> \
    --end-year <YYYY> \
    --max-papers 10

To restrict to specific sources:

python "$SEARCH" \
    --query "<QUERY>" \
    --start-year 2024 --end-year 2026 \
    --sources arxiv semantic_scholar openreview

Multi-query union (each query hits every source; results are unioned, deduped, and ranked against the combined term set):

python "$SEARCH" \
    --queries "diffusion watermarking|latent-space watermark|generative model IP protection" \
    --start-year 2024 --end-year 2026

Opt-in noise filter (drops papers with relevance score below N; the CLI always prints exactly how many were dropped — omit for full recall):

python "$SEARCH" --query "<QUERY>" --start-year 2024 --end-year 2026 --min-score 2

Always pass --json. Step 1.5 runs on every search and judges relevance from abstracts, which are never printed to stdout — this file is the only place they exist. It is also the unfiltered record the user can fall back to if the filter dropped something they wanted:

python "$SEARCH" --query "<QUERY>" --start-year 2024 --end-year 2026 \
    --json /tmp/paper_search_results.json

Legacy per-source view (no dedup, no ranking — raw connector output):

python "$SEARCH" --query "<QUERY>" --start-year 2024 --end-year 2026 --raw

To disable parallel execution (rarely needed):

python "$SEARCH" \
    --query "<QUERY>" \
    --start-year 2024 --end-year 2026 \
    --no-parallel

--no-parallel still uses an isolated worker process for each source, but runs those workers in source order instead of starting them together.

Or call the function directly when more control is needed (e.g. consuming the structured dict rather than CLI text output). This is rarely necessary — see references/programmatic_api.md for the snippet.

Valid source names

Output schema

search_papers() returns a dict mapping source name to a list of paper dicts:

{
  "arxiv": [
    {
      "title": str,
      "authors": [str, ...],
      "year": int,
      "abstract": str,
      "url": str,
      "venue": str,
      "citation_count": int,
      "publication_date": str,
      "source": str,
      "doi": str | None,
      "arxiv_id": str | None
    }, ...
  ],
  "semantic_scholar": [...],
  ...
}

CLI output (default): a single deduped, relevance-ranked list. Cross-source duplicates are merged into one record (matched by DOI, then arXiv id, then normalized title) that keeps the highest-signal source's fields, the max citation count, and a Sources: provenance line; every paper carries a lexical relevance score against the query; survey/review-titled papers are tagged [survey] and sunk to the bottom (never dropped). Nothing is filtered unless --min-score is passed, and then the drop count is printed. A per-source hit-count line plus "N cross-source duplicate records merged" precedes the list. --raw restores the legacy grouped-by-source printout.

Output to the user

After running, display every unique paper in the CLI's ranked order (it is already deduped and sorted by relevance), then the Model Knowledge section, then a summary. With --raw, fall back to per-source groups in this order: Semantic Scholar, OpenAlex, arXiv, OpenReview, Crossref, DBLP, Model Knowledge.

Recall still matters inside the kept set: users invoking this skill are doing literature reviews, related-work surveys, or prior-art checks. Everything that survives Step 1.5 is displayed in full — summaries augment the tables, they never replace them, so don't collapse results into a digest "to save space." The only papers that may disappear are the ones Step 1.5 judged irrelevant against the user's own question, and only under its when-unsure-keep rule.

Step 1: Display all results that survive the filter

Default (unified ranked view): display every unique paper that Step 1.5 kept, in ONE markdown table, preserving the CLI's rank order. Prefix [survey] in the Title cell where tagged. Reproduce the CLI's per-source hit counts + merged-duplicates line above the table, and the drop count line when --min-score was used.

per-source hits: semantic_scholar=10, open_alex=10, arxiv=10 … · 22 unique (8 duplicates merged)

| #   | Title       | Date    | Venue   | Citations | Score | Sources |
|-----|-------------|---------|---------|-----------|-------|---------|
| [1](paper url) | Title here | 2024-03 | NeurIPS | 42 | 5 | SS, arXiv |
| [2](paper url) | [survey] Title here | 2023-11 | ICLR | 10 | 4 | OpenAlex |

With --raw: one table per source under a source heading (legacy format). If a source returned 0 results, note it explicitly (e.g. "### OpenReview (0 papers) — No matches found in this window").

If errors occurred during search, they are printed to stderr by the script — surface them to the user, never hide them.

Step 1.5: Relevance filter (semantic, ALWAYS run)

Lexical scores rank; they do not understand. They admit generic-word noise and they miss synonym-only matches — a paper on "IP protection for generative models" scores 0 against a "watermarking" query yet may be the closest prior art in the set. So read the abstracts and drop what does not belong.

When to run: always, on every search, over every paper in the ranked set.

Which model: your own — the model already running this skill. Do NOT hand this off to a cheaper/faster tier to save tokens. This is an open-ended intent judgment, not labelling against a fixed list: the boundary of "relevant" lives only in the user's question, and is nowhere written down. Cheaper tiers systematically misjudge neighbouring work as unrelated, and under a hard filter that error is invisible and unrecoverable.

How:

1. Re-run (or have run) the CLI with --json <path> and read that file. It carries the abstract for each ranked record; stdout does not.
2. For every paper, judge relevant | irrelevant against the USER'S ORIGINAL QUESTION — not the rephrased search query, which is lossier than what the user actually asked for. Base the judgment on title AND abstract; a paper whose abstract you did not read cannot be judged irrelevant.
3. When unsure, relevant. irrelevant is for papers that are clearly about something else, not for papers you are not sure about. This is the one rule that keeps a hard filter safe: the user never sees what you drop, so the burden of proof sits on dropping, not on keeping.
4. Drop every irrelevant paper from the displayed table.

How to present: one table, filtered. Do not print the dropped papers, do not add a collapsed section for them. State the count on the hit-count line so the user knows the filter ran and by how much:

per-source hits: semantic_scholar=10, open_alex=10, arxiv=10 … · 22 unique (8 duplicates merged) · 5 filtered as irrelevant

If --json was written, say once that the unfiltered set is in that file. That is the recovery path if the filter took too much; it costs one line and it is the only way a user can audit a drop.

Step 2: Summary of all searched results

After displaying all papers, provide a comprehensive summary with the following sections, in this exact order:

1. Overview: query used, year range, and total number of papers found. One or two sentences framing what the corpus covers.
2. Trends: Temporal patterns (e.g. "interest surged in 2024"), dominant venues, methodological shifts, and recurring author groups or labs.
3. Key themes: 3–6 main research themes / clusters across all results, each with a one-line description and 2–3 representative paper numbers.
4. Keywords frequency: A table of the most frequent technical terms / concepts extracted from titles (abstracts are in the JSON schema but not printed by the CLI), with counts. Format: | Keyword | Count |. Include the top 5.
5. Most cited by accepted paper: Top 5 most-cited accepted papers across all sources, ranked by citation count, as a table: | Rank | Title | Year | Citations |.
6. Most cited by first author: Top 5 first authors ranked by total citations accumulated across papers in this result set, as a table: | Rank | Author | Papers in set | Total citations |. The Author column must contain ONLY the author's name (e.g. Jane Doe). Do not append paper titles, affiliations, venues, or any other information in this column — paper counts and citation totals live in their own columns.
7. Recommendations for reading: 3–5 papers most relevant and impactful to the user's original query, ordered as a reading path (foundational → recent), each with a one-line justification.

Dependencies & failure modes

• arXiv: uses the arXiv API.
• DBLP: uses DBLP API.
• OpenAlex: uses OpenAlex API.
• OpenReview: requires pip install openreview-py.
• Semantic Scholar: uses Semantic Scholar API.
• Crossref: uses Crossref API.
• Model knowledge: no API call. Papers are recalled from the model's own training data — fast and free, but capped by the model's knowledge cutoff and prone to hallucination. See the "Model knowledge source" section below for how to use it responsibly.

HTTP 429/500/502/503/504 responses use bounded retries. If a source still fails, its worker prints the error and the other source workers continue. Surface those errors to the user.

HTTP timeout configuration

All network sources use separate connection and socket read-idle timeouts:

Every configured value must be positive; malformed, zero, negative, NaN, or infinite values fail before workers start. The 300-second read timeout is not a total source budget: a response can take longer overall if it continues making socket-level progress.

Model knowledge source

The model_knowledge source is different from the others: it has no API and no script call. Instead, after the CLI search returns, recall 5–10 additional papers from your own training data that match the query and year range, and present them as a separate source in the output.

Why include it

API search is high-precision but low-recall in two predictable cases:

1. Foundational older papers that practitioners always cite but that keyword search misses (e.g. the original BERT or ResNet paper when the query is about a recent variant).
2. Cross-disciplinary classics that live in venues the APIs index poorly.

Model recall complements the APIs by surfacing the "everyone knows this one" papers that don't always come back from a fresh keyword query.

How to populate it

After the CLI run completes:

1. Reflect on what you know about the query topic.
2. List up to 10 papers from your training data that fit the query and year range, with: title, primary author(s), year, venue, and a one-line reason it's relevant.
3. Deduplicate against the API results — if a paper already appeared in any API source, do not repeat it under model_knowledge.
4. Flag confidence honestly. The model knowledge column has no citation count and no live URL; if you're not sure a paper exists exactly as you remember it, mark it (uncertain — verify) in the table rather than presenting it as confirmed.

Why honesty matters here

Hallucinated paper titles are the classic LLM failure mode for this task. A fake "Smith et al., 2023, NeurIPS" looks identical to a real one in a markdown table, and the user has no way to tell. The point of this source is to surface real papers the APIs missed — not to pad the list. If you can't recall ≥5 papers with reasonable confidence, return fewer; an empty model-knowledge section is fine and honest.

Display format

Use the same table layout as the other sources, but the URL column may link to a search query (e.g. an arXiv or Google Scholar search) rather than a canonical paper URL, since you don't have a verified link:

### Model Knowledge (N papers, may include uncertain entries)

| #   | Title       | Year | Venue   | Notes |
|-----|-------------|------|---------|-------|
| [1](https://scholar.google.com/scholar?q=Title) | Title here | 2018 | NeurIPS | Foundational; often cited by recent work on X |
| [2](...) | Title here | 2024 | ICLR | (uncertain — verify) |

Replace the "Citations" column with "Notes" because you don't have a reliable citation count from memory.

Example

User: "Find papers on diffusion policies for robotics from 2023 to 2024."

Run (using $SEARCH as defined in the "How to run" section):

python "$SEARCH" \
    --query "diffusion policy robotics" \
    --start-year 2023 --end-year 2024 \
    --max-papers 10

To search only specific sources:

python "$SEARCH" \
    --query "diffusion policy robotics" \
    --start-year 2023 --end-year 2024 \
    --sources arxiv openreview semantic_scholar \
    --max-papers 10

Then read the output and summarize per the rules above.

Important Notes

• Log the final report. After completing the search, write a single markdown file to: ${CLAUDE_PROJECT_DIR}/allinone.md
  • Contents: the full "Display ALL results from every source" tables followed by the "Summary of all searched results" section — in that order, with no truncation.
• Display the full report to the user. Return the complete detailed report inline — every paper, every table, plus the analysis and reasoning. Never collapse the tables into a summary, and never abbreviate results to "save space".
• Never ask for confirmation. All inputs are auto-inferred (see the "Inputs" section). Run the search immediately on the first turn.
• Surface errors verbatim. If a source fails, report the stderr message to the user rather than hiding it or retrying blindly.