Aminer Deep Search

AMiner Deep Search

Host-model-driven survey paper collection. You (the model reading this) are the controller: run the tool scripts, read their JSON output, judge relevance yourself, and iterate until the collection target is met.

Scope

• Use for: survey bibliography collection (hundreds of papers), keyword expansion, backward-citation snowballing.
• Do not use for: single-paper lookup or Q&A (route to aminer-free-academic), personalized recommendations (route to aminer-daily-paper).

Pre-flight

1. Check the key without printing it:

[ -z "${AMINER_API_KEY:-}" ] && echo "AMINER_API_KEY missing" || echo "AMINER_API_KEY exists"

If missing, stop and ask the user to set AMINER_API_KEY (console: https://open.aminer.cn/open/board?tab=control). Never print the key.

2. Confirm the topic and the target-size (default 400). If your round plan is estimated to cost ¥5 or more, tell the user the estimate and get confirmation before starting.

Tools

Both scripts live in scripts/ under this skill directory. They print exactly one JSON document to stdout (the tool result); diagnostics and a [cost] line go to stderr. They never score relevance — that is your job.

scripts/aminer_api.py — AMiner API calls

Output shape: search/qa-search/info print [{id, title, year?, venue?, abstract_slice?}]; references additionally includes source_paper_ids (which seeds cited the paper). Seeds themselves are excluded from references output.

scripts/paper_set.py — cross-round state file (no network)

State file defaults to outputs/paper_set.json relative to the working directory.

# Merge kept results (pipe the filtered JSON array in), dedupe by id
python3 scripts/aminer_api.py search --query "..." | python3 scripts/paper_set.py add
# → {"added": N, "duplicates": M, "total": T}

python3 scripts/paper_set.py stats     # totals, expanded_seeds, by_year
python3 scripts/paper_set.py mark-expanded --ids id1 id2   # record snowballed seeds
python3 scripts/paper_set.py export -o outputs/final_papers.json

add also accepts --ids id1 id2 ... for bare IDs. Items carrying source_paper_ids (from references) automatically mark those seeds as expanded.

If you want to filter before adding, read the search output first, then pipe only the kept items:

printf '%s' '[{"id":"...","title":"..."}]' | python3 scripts/paper_set.py add

Round Protocol (core)

Round 0 — plan

• Derive 4–8 seed queries from the topic: synonyms, subfields, method names, datasets/benchmarks, common English abbreviations.
• Estimate rounds and cost (searches ≈ ¥0.01–0.05 each, references ≈ ¥0.10/seed). If the estimate is ≥¥5, confirm with the user first.

Each round (default budget: 12 rounds), five fixed steps

1. Search: run 1–4 search / qa-search calls from the pending query queue. Prefer search (cheaper); use qa-search when the query is a natural-language question.
2. Filter & add: read the stdout results, judge relevance to the topic yourself, and pipe only the kept items into paper_set.py add. Never add papers you consider off-topic.
3. Check: run stats to see the total and this round's increment.
4. Snowball: from this round's relevant additions pick ≤5 strong seeds (highly relevant, ranked high under --order n_citation, not in expanded_seeds) and run references --ids .... Filter the output for relevance, then add it. Run mark-expanded for seeds that yielded nothing addable.
5. Decide: choose the next move —
   • a search returned <5 results or poor quality → replace it with a reformulated query (max 2 variants per direction, then switch to snowballing);
   • references are yielding many relevant papers → keep snowballing from fresh seeds;
   • reached target-size, or results are exhausted, or 2 consecutive rounds added <5 papers → terminate.

Wrap-up

Run export, then report: final paper count, total cost (sum the [cost] stderr lines), and the output path.

Rules

1. Never fabricate paper IDs or titles; only cite data actually returned by the tools.
2. Free first: metadata always comes from the free paper/info (the scripts already do this); never call the paid paper/detail for bulk metadata.
3. Keep the raw tool output out of your final answer; report counts and the exported file path instead.
4. Never print or log AMINER_API_KEY.
5. If AMiner returns fewer papers than the target, report the real count instead of inventing papers.