name: paper-search-pro description: "Find academic papers across up to 7 sources (OpenAlex / Semantic Scholar / CrossRef / PubMed / arXiv for English, plus native-Chinese retrieval via NSSD 国家哲社文献中心 + yiigle 中华医学期刊) with adjustable depth — Quick scan (5 min) to Audit prep (3 hr). Use when the user wants to find papers, run a literature search, gather references, scope a research topic, search Chinese-language / 中文原生 literature (中文文献/中文核心/CSSCI/C刊/国内研究/国内文献/中华××期刊/心理学报/经济研究), or filter results by journal tier (中科院分区/一区/几区, Q1, JCR/SJR quartile, 影响因子/impact factor, 期刊分区, 顶刊/top journal, '按分区筛'). Triggers on search verbs ('find papers', 'literature search', 'papers about X'), review types ('scoping review', 'systematic review', 'SR prep', 'literature review', 'lit review', 'help me write a lit review'), Chinese ('找文献', '找论文', '论文搜索', '学术检索', '文献检索', '文献综述', '综述前期', '求文献', '中文文献', '中文核心', 'CSSCI', 'C刊', '国内研究', '找中文的'). Outputs Shadcn HTML report + BibTeX/RIS/CSV + PRISMA-S log. Do NOT use for: concept explanations ('what is X' / 'X 是什么', e.g. '影响因子怎么算'), writing ('帮我写' / 'help me write a paragraph'), single-paper interpretation or PDF download with metadata (use paper-downloader-portable), or when the user already has a literature set (use literature-set-review)." license: Apache-2.0 allowed-tools: Bash, Read, Write, Edit, Glob, Grep, Task metadata: author: Bo version: 2.3.0 vendored-from: futurehouse/paper-qa (Apache 2.0)

paper-search-pro

Multi-source literature search with adjustable depth. Four tiers, five data sources orchestrated by you (the main agent). Python helpers handle deterministic work; LLM classification is delegated to parallel Inline SubAgents — no external API key required.

When to use this skill

User wants to find academic papers / 找文献 / 论文搜索
User is preparing a literature review, systematic review (SR), scoping review, or meta-analysis
User wants to scope research on a topic for a thesis / proposal / coursework / news story
User asks "what research exists on X" / "find me papers about Y"
User uploads a query that suggests literature gathering (PICO, SPIDER, MeSH, RCT, etc.)

When NOT to use

User wants to read a specific paper (use PDF reader / download tool)
User wants to summarize a single known paper (use a summarizer)
User wants to download PDFs given DOIs (use paper-downloader-portable)
User already has a literature set and wants to write a review (use literature-set-review / factor-outcome-review)
User wants concept explanation, not papers ("what is prospect theory" → just answer)

🤖 Called by another agent / headless mode

If you are an agent driving this Skill for your own reasoning (not for a human who wants an HTML report), do NOT hand-run the 14-STEP recipe below. There is a single structured-data channel built for you:

PYTHONPATH=$PSP_HOME python3 -m scripts.agent_search "<query>" > result.json

One command runs the whole deterministic core — multi-strategy retrieve → dedup → heuristic relevance score (computed for every paper) → saturation signal → quota snapshot → per-paper journal metric — and prints one JSON envelope (no HTML, no PRISMA, no LLM classification SubAgent). The human path below is unaffected. That command gives you a deterministic floor, not the finished job — agent mode is not meant to stop at the machine output; references/agent_mode.md is where you layer your own semantic judgement on top to reach human-recipe quality (the command guarantees the floor; you supply the quality).

📖 Read references/agent_mode.md for the full envelope schema, every flag (--verify, --min-relevance, --quartile, --min-impact, …), the relevance formula, error codes / exit codes, and source selection. This is the SSOT for agent callers — everything else in this section is just the pointer to it.

Everything from here down is the human-facing 14-STEP recipe (HTML report + exports). Use it when the consumer is a person.

🔥 Execution discipline (read before running anything)

Four invariants govern every step — ignoring them is the dominant failure mode in real sessions:

A — NEVER cd into the Skill directory. cd $PSP_HOME rebinds ./ to the Skill asset folder, so ./paper-search-results/... lands inside the Skill instead of the user's workspace (and a re-install wipes it). Run every helper from the user's PWD: PYTHONPATH=$PSP_HOME python3 -m scripts.<name> … > "$SEARCH_DIR/...". $PSP_HOME (STEP 0) is the install dir; $SEARCH_DIR (STEP 0) is an absolute path under the user's PWD.
B — Dispatch classifier SubAgents in parallel. STEP 6 puts up to 5 Task blocks in one assistant message; serial dispatch inflates Standard tier from ~10 to ~17 min. The worked example lives in STEP 6 — it is not repeated elsewhere.
C — Announce every skip. If you skip a STEP (budget / empty data / user choice), say what you skipped, why, what's lost, and how to recover (e.g. "re-run at --tier deep"). Skipping is fine; surprising the user is not.
D — Read a step's cited reference when that step is non-trivial for this case. Each STEP names a references/<file>.md carrying edge cases not duplicated here. You won't read all of them every run, nor should you — but skipping the reference for a step you are actually about to run is where boundary knowledge (dict-vs-list shapes, enrich-not-search, DOI casing) gets lost. Read the one in front of you.

Architecture at a glance

You (main agent) drive the workflow per this SKILL.md.
Python helpers do deterministic work — NO LLM inside, NO external API key.

  L1 OpenAlex (primary)  → deep top-100 multi-strategy
  L2 PubMed (medical)    → MeSH enricher (mostly; Audit-tier can search independently)
  L2 arXiv (CS/preprint) → T-0~T-4 freshness sentinel
  L3 Semantic Scholar    → influentialCitationCount + abstract fallback
  L3 CrossRef            → funder / license / clinical-trial-number

  Classification         → Inline SubAgents (parallel, file-IPC, 5 per message)
  Output                 → HTML (Shadcn) + MD + BibTeX/RIS/CSV + PRISMA-S log

The 4 tiers — pick first

| Tier | Wall-clock | Papers | When to pick | |------|------------|--------|--------------| | Quick | ~5-8 min | 20-60 | "查一下" / "几篇" / "before tomorrow" / fast scope | | Standard (default) | ~10-17 min | 60-180 | Scope a topic / write background / general lit search | | Deep | ~30-45 min | 180-400 | "thorough" / writing a review article / 综述写作 | | Audit | ~2-3 hr | 400-1000+ | "systematic review" / "PRISMA" / "Cochrane" / "meta-analysis" |

📖 BEFORE picking, read references/tier_decision.md. Tell the user your choice and why. For Audit, show limitations warning + get explicit confirmation before starting.

The recipe

For every literature search, follow these steps in order. Each step references a references/ file for details. Skip files only when the step is obviously trivial for the case at hand — and announce the skip per Rule C.

STEP 0 — Setup ($PSP_HOME + working directory)

📖 BEFORE THIS STEP, read: references/setup.md.

Resolve the Skill install path into $PSP_HOME (every later step uses PYTHONPATH=$PSP_HOME). Prefer explicit injection / agent env var; otherwise scan the known cross-agent install locations. If your harness already exposes this SKILL.md's absolute path, just export PSP_HOME="<that dir>" and skip the scan. 📖 Full rationale, why this can't be a script, and the complete path list: references/runtime_bootstrap.md.

PSP_HOME="${PSP_HOME:-${CLAUDE_SKILL_DIR:-${CODEBUDDY_SKILL_DIR:-}}}"   # explicit / agent-injected
if [ -z "$PSP_HOME" ]; then                                            # else scan known installs
  for base in "$HOME/.claude" "$HOME/.codex" "$HOME/.agents" "$HOME/.config/opencode" \
              "$HOME/.codeium/windsurf" "$HOME/.config/goose" "$HOME/.cline" "$HOME/.roo" \
              "$HOME/.copilot" ./.claude ./.codex ./.agents ./.cursor ./.opencode ./.windsurf; do
    [ -f "$base/skills/paper-search-pro/SKILL.md" ] && PSP_HOME="$base/skills/paper-search-pro" && break
  done
fi
[ -z "$PSP_HOME" ] && { echo "ERROR: paper-search-pro install not found. Set PSP_HOME to the dir containing SKILL.md."; exit 1; }
export PSP_HOME; echo "Using Skill install: $PSP_HOME"

Verify config keys (executed from any cwd, never cd into the Skill dir):

PYTHONPATH=$PSP_HOME python3 -c \
  "from scripts.config import load_config; c = load_config(); print('OK' if c.openalex_api_key and c.ncbi_email else 'MISSING — see references/setup.md')"

If "MISSING", point the user to references/setup.md (5 keys, all free, ~15 min total) and halt.

Set up the working directory variable — every subsequent step uses $SEARCH_DIR:

SEARCH_ID="<topic_slug>_<tier>_$(date +%Y%m%d_%H%M%S)"   # e.g. clt_education_quick_20260522_103045
SEARCH_DIR="$(pwd)/paper-search-results/$SEARCH_ID"
mkdir -p "$SEARCH_DIR/raw" "$SEARCH_DIR/batches" "$SEARCH_DIR/classifications"
echo "Outputs will land in: $SEARCH_DIR"

$SEARCH_DIR is now an absolute path under the user's PWD. Use "$SEARCH_DIR/..." (quoted, with the variable) in every helper command below — not ./paper-search-results/....

STEP 1 — Plan the query (MANDATORY for all tiers)

📖 BEFORE THIS STEP, read: references/query_planner.md.

Detect the report UI language — UI_LANG (zh for Chinese queries, en for everything else) selects which UI language the final HTML report renders in. Paper titles / abstracts / authors / venues are NEVER translated — only the report's UI chrome. Pass --language $UI_LANG to STEP 12b.

UI_LANG=$(PYTHONPATH=$PSP_HOME python3 -m scripts.detect_language "$USER_QUERY")

The detector routes Japanese / Korean / European queries to English (the bundle ships only EN + ZH dictionaries; English is the international academic default). 📖 The exact Unicode rule and why kana is checked before Han live in references/runtime_bootstrap.md.

Determine the search language space (search_language, axis 2 — which literature ocean, distinct from UI_LANG above which is only report chrome). 📖 The parsing SSOT is references/source_routing.md §"Language scope"; resolve the space here, before phrasing the query, because it changes how STEP 3's query is built. This is additive and opt-in — a pure English query resolves to the en space with zero new prompts or behavior (R-19); everything below fires only for Chinese queries or explicit signals.

Read the persisted default config.search_language (auto | en | zh | both) and apply the priority ladder flags > in-query markers > config > auto. CJK presence is the mechanical fact from detect_language above; markers (CSSCI, 中文文献, SSCI, 知网, …) and non-signals (中科院一区, topic-about-China) are your semantic judgment per the §"Language scope" tables.
auto + a Chinese (CJK) query + no language marker + no persisted value → ask ONE question before retrieving (this is the human path's job; the CLI/agent path passes through instead). Two sentences, offer to persist, and don't re-ask later this session:

你用中文提问——文献要英文、中文,还是都要?顺便可以说"以后都这样",我就记成默认、下次不再问。
- "中文" / "都要" → enter that space (STEP 2 discipline routing takes over; report one line there).
- "英文" → v2.2 behavior (Chinese topic planned as an English query), report one line.
- "无所谓 / 都行" → this run uses both (Recall > Precision), not persisted; if the same user answers "无所谓" a second time, add one light offer to set both as default, then never ask again.
- Only an explicit "以后都…" persists to config.search_language (single answers never auto-persist).
If a rank ambiguity (bare "Q1") also fired this run, merge both questions into ONE message — ask language + platform together, never in two rounds (over-asking is a red line).
Once the space is known, phrase the query per references/query_planner.md §"Cross-language query handling": zh keeps Chinese search terms (no translation), en uses the English terms (v2.2 behavior), both builds two sets.

Apply PICO / SPIDER / PEO depending on domain:

Medical/clinical → PICO (Population/Intervention/Comparator/Outcome)
Qualitative → SPIDER
Scoping → PEO (Population/Exposure/Outcome)
Open-ended → just extract 2-4 concept blocks + 2-5 synonyms each

Journal-rank intent recognition (additive — only acts when the query mentions a partition). Before you extract concept blocks, check whether the user's query carries a journal-rank/partition phrase — "中科院一区", "Q1", "JCR Q1", "SJR Q2", "顶刊 / top journal". If so, that phrase is a filter condition, not a search term, and it MUST be stripped from the topic before retrieval. This roots out the failure that motivated the whole feature: "中科院一区情绪调节" used to send "中科院一区" to the search engine as a topic word, so it searched for papers about 中科院一区 instead of papers on 情绪调节 filtered to CAS tier 1. The deterministic parser does both jobs (extract + strip) for you:

PYTHONPATH=$PSP_HOME python3 -c "
from scripts.rank_intent import parse_rank_intent
i = parse_rank_intent('''<original user query>''')
import json; print(json.dumps({
  'platform': i.platform, 'tiers': i.tiers, 'quartiles': i.quartiles,
  'top': i.top, 'ambiguous': i.ambiguous, 'cleaned_query': i.cleaned_query,
  'stripped': i.matched}, ensure_ascii=False))
"

Then act on the parse:

cleaned_query is the real topic — use it (NOT the raw query) for STEP 3 retrieval and the query plan. When the query had no rank phrasing, cleaned_query == query and nothing changes (R-19 default path is untouched).
platform + tiers/quartiles/top are the filter you will apply in STEP 10/11 — remember them; do not filter here.
ambiguous == True (a bare "Q1"/"Q2" with no platform word — the recogniser never guesses a platform): ask the user one short question inline before going further — "按 JCR 还是 SJR 的 Q1 筛?顺便要不要设为以后的默认?" The CLI/headless path cannot ask, so this inline question is specifically the human path's job.
If the query mentions no partition at all, skip this entirely — STEP 1 proceeds exactly as before.

Even Quick tier needs a lightweight version of this step — never skip silently. Output: 1-3 search strategies (concept blocks + year range + work type filter). Write to "$SEARCH_DIR/query_plan.json" so PRISMA-S logger can pick it up later (STEP 13).

STEP 2 — Route supplemental sources within the STEP-1 language space

📖 BEFORE THIS STEP, read: references/source_routing.md.

You make these routing calls by judging the query's domain — the reference's keyword tables are calibration examples, not a match list (mechanical facts — CJK detection, explicit --flags — stay deterministic). Within the language space fixed in STEP 1, route the per-discipline boosters:

English space (en, or the English half of both) — unchanged from v2.2:
- Medical signals (RCT, PRISMA, MeSH, clinical, disease names) → enable PubMed
- CS/preprint signals (preprint, arXiv, NeurIPS, transformer, "最新", 2024+) → enable arXiv
- Cross-domain (e.g. "AI in radiology") → enable both
- Pure social science / humanities → OpenAlex only
- (Judgment call: a core AI/CS query may also raise the primary engine to Semantic Scholar — see source_routing.md §"AI / CS queries → consider Semantic Scholar as primary".)
Chinese space (zh, or the Chinese half of both) — route the Chinese boosters the same way, by discipline:
- Social-science / humanities signal → add NSSD (国家哲社文献中心; carries the CSSCI 收录标识 OpenAlex has ≈0 coverage of)
- Medical signal → add yiigle (中华医学期刊全文数据库); PubMed still covers MEDLINE-indexed 中华 journals, so the two are complementary
- Pure sci-tech with neither → Chinese side runs on OpenAlex only (sci-tech Chinese core journals mostly register DOIs, so OpenAlex covers them well)

Report one line (axis-3 style — a statement, not a question; 22 §6.3). For an English-only run this is the existing PubMed/arXiv notice, unchanged ("I detected medical + CS signals — also searching PubMed and arXiv. Override with --no-pubmed."). For a Chinese space, e.g.:

本次按「中英都要」检索;中文侧检测到社科主题,已加 NSSD(国家哲社文献中心)。想去掉说 --no-nssd,只查一边说"只要英文/中文"。

Coverage honesty rides with the notice: if the zh space has a social-science topic but the user declined NSSD, add that OpenAlex hits ≈0 on CSSCI flagship journals (经济研究 / 管理世界 …), so that layer is missing. User can override any of this with an explicit instruction (a per-query override wins over everything).

On the --flag shorthands above (--no-nssd, --no-pubmed, --source …): on this human path they are natural-language override notation — a compact way to write what the user can say ("去掉 NSSD" / "只查 OpenAlex"), which you (the LLM) interpret. They are not executable CLI flags — no script parses them here. The only real, script-parsed flags live on the agent/headless path (agent_search), and there the Chinese-source control is opt-in: --with-nssd / --with-yiigle (there is no --no-nssd / --source there). See references/agent_mode.md.

STEP 3 — Retrieve from OpenAlex (deep)

📖 BEFORE THIS STEP, read: references/openalex_helper_cheatsheet.md.

Always run OpenAlex first. (OpenAlex is the default primary source; only if primary_source is set in config.yaml or the OpenAlex quota is exhausted, see Primary source selection & quota fallback in references/source_routing.md for the additive SS-fallback flow — default behavior is unchanged.) For Standard+ tiers, use multi-strategy deep crawl:

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.openalex_helper double-sort "<query>" \
    --n 50 --year-min 2018 \
    > "$SEARCH_DIR/raw/openalex.json"

For Quick tier, single-strategy is fine:

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.openalex_helper search "<query>" \
    --limit 30 --year-min 2018 \
    > "$SEARCH_DIR/raw/openalex.json"

The full subcommand + flag reference (search / double-sort / seminal / reviews / journal-list / citation-network, all verified against argparse) is in references/openalex_helper_cheatsheet.md — read it before reaching for anything beyond the two commands above. For Deep+Audit, also call topic-specific subcommands (e.g. seminal, reviews, journal-list), append outputs to $SEARCH_DIR/raw/openalex_*.json, and federate them all together in STEP 5.

STEP 4 — Run L2 boosters (if enabled by STEP 2)

📖 BEFORE THIS STEP, read: references/pubmed_helper_cheatsheet.md and references/arxiv_helper_cheatsheet.md.

PubMed — default mode is enrich, NOT search:

Standard / Deep tier: enrich OA-found papers with MeSH terms (mutates the openalex.json file in place):

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.pubmed_helper enrich \
    --input-file "$SEARCH_DIR/raw/openalex.json" \
    --output-file "$SEARCH_DIR/raw/openalex.json"

Audit tier with explicit MeSH query: independent MeSH search (produces a new file to federate later):

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.pubmed_helper search-mesh "Diabetes Mellitus, Type 2" \
    --year-min 2020 --limit 30 --pub-type "Randomized Controlled Trial" \
    > "$SEARCH_DIR/raw/pubmed.json"

Generic pubmed_helper search is a fallback when no MeSH term is known — prefer enrich or search-mesh whenever possible.

arXiv — only if query contains freshness signals (preprint, 最新, 2024+):

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.arxiv_helper freshness "<query>" \
    --days 4 --limit 30 \
    > "$SEARCH_DIR/raw/arxiv.json"

Subcommand reference:

arxiv_helper freshness <query> --days N --limit M [--all-cats]
arxiv_helper search <query> --limit M --sort submitted|relevance|lastUpdated [--all-cats]
arxiv_helper get <arxiv_id>

NSSD / yiigle — Chinese boosters (ONLY when STEP 1-2 put this run in the zh space, and only the one(s) the discipline routing selected):

Each is an independent primary source for the Chinese space (same role as ss_helper --search) and emits the same UnifiedPaperEntity shape as openalex.json, so STEP 5 federates them identically. Keep the query in Chinese (do NOT translate — query_planner §Cross-language) and write to raw/nssd.json / raw/yiigle.json:

# NSSD — Chinese social-sciences & humanities (adds the CSSCI-tier layer OpenAlex lacks)
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.nssd_helper --search "<中文检索式>" \
    --n 50 --year-min 2018 \
    > "$SEARCH_DIR/raw/nssd.json"

# yiigle — Chinese medical (中华医学期刊全文数据库; native-Chinese titles + abstracts)
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.yiigle_helper --search "<中文检索式>" \
    --n 50 --year-min 2018 \
    > "$SEARCH_DIR/raw/yiigle.json"

Both degrade gracefully to [] on any network / HTTP failure (they never raise) — an empty file just federates to nothing. Both take only 题录 + 摘要 (compliance: no full-text download, no caching) and print their source attribution to stderr. --year-min filters client-side; drop it to keep all years.

config search_language: en but a Chinese query arrived (hard boundary 2): do NOT enable Chinese boosters, but say one line — never a silent translation (22 §6.4):

按你的默认(只查英文),我把中文主题规划成英文检索式了。想要中文文献这次说一声即可,想改默认说"以后…"。

User names 知网 / CNKI / 万方 / 维普 (marker hit + compliance): these are closed subscription databases PSP does not scrape. Say one line, offer the substitute, don't re-argue (22 §6.5):

PSP 不接知网/万方(合规原因,不做封闭库抓取)。中文侧用 OpenAlex 中文底座 + NSSD(社科,含 CSSCI 标识)/yiigle(医学)覆盖;如需知网全文,结果里的题录可去知网人工检索。继续吗?

STEP 5 — Federate (dedup + merge)

📖 BEFORE THIS STEP, read: references/source_routing.md §"Field priority table".

Combine all retrieval results into a single deduped KG. Default output is a dict keyed by canonical_key — that's what rcs_parser expects later, so do NOT pass --as-list:

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.federated_kg_resolver \
    --input-files "$SEARCH_DIR/raw/openalex.json" \
                  "$SEARCH_DIR/raw/pubmed.json" \
                  "$SEARCH_DIR/raw/arxiv.json" \
    --output "$SEARCH_DIR/kg.json"

Pass only the input files you actually produced — skip ones that were not enabled by STEP 2. If STEP 4 ran the Chinese boosters, add "$SEARCH_DIR/raw/nssd.json" / "$SEARCH_DIR/raw/yiigle.json" to the same --input-files list — they carry the identical entity shape and federate exactly like the others (CJK-safe dedup is handled by the Phase 0 canonical-key fix, so distinct Chinese titles don't collapse). For an English-only run those files don't exist, so the call is byte-identical to v2.2 (R-19). This handles DOI normalization (arXiv X→x case), version stripping, E5b guard (same title+year but different DOIs are kept separate), and field-priority merge.

--as-list exists but is only for consumers that want a sorted list (by citation_count); do not use it in this pipeline.

STEP 6 — Classify in parallel batches (LLM happens here — main agent + SubAgents)

📖 BEFORE THIS STEP, read: references/classifier_subagent_prompt.md and references/rcs_rubric.md.

Split the KG into batches of 10 papers each. Write to "$SEARCH_DIR/batches/batch_NNN.jsonl".

Before dispatch, expand $PSP_HOME/references/rcs_rubric.md into the actual absolute path (e.g. /Users/alice/.claude/skills/paper-search-pro/references/rcs_rubric.md) and substitute it for {rubric_path} in the classifier prompt template. Each SubAgent runs in its own shell where $PSP_HOME is not exported — passing the literal $PSP_HOME token would leave the SubAgent unable to find the rubric, which silently degrades scoring quality. See references/classifier_subagent_prompt.md for the full placeholder table.

🔥 PARALLELISM IS MANDATORY (Rule B):

You MUST dispatch up to 5 classifier SubAgents in a single assistant message using multiple Task tool_use blocks. Serial dispatch (one Task per message, waiting for each result) is the single biggest performance failure observed — it inflates Standard tier from ~10 min to ~17 min.

✅ CORRECT — in ONE assistant message:

Task tool_use #1  → subagent_type="general-purpose", prompt="<classifier prompt for batch_001.jsonl>"
Task tool_use #2  → subagent_type="general-purpose", prompt="<classifier prompt for batch_002.jsonl>"
Task tool_use #3  → subagent_type="general-purpose", prompt="<classifier prompt for batch_003.jsonl>"
Task tool_use #4  → subagent_type="general-purpose", prompt="<classifier prompt for batch_004.jsonl>"
Task tool_use #5  → subagent_type="general-purpose", prompt="<classifier prompt for batch_005.jsonl>"

All five tool_use blocks live in the same <assistant> message. The harness fires them in parallel; you receive five tool_result blocks back together.

❌ WRONG — five separate messages (this is what serial dispatch looks like):

Message N:    Task tool_use #1 ─→ wait for result
Message N+1:  Task tool_use #2 ─→ wait for result   ← SERIAL, makes Standard run 70% slower
Message N+2:  Task tool_use #3 ─→ wait for result
...

If you have more than 5 batches, send 5-at-a-time across multiple messages — each message still contains 5 parallel Task blocks.

Each SubAgent reads its batch file, applies the RCS rubric, and writes "$SEARCH_DIR/classifications/batch_NNN_result.json". Then merge classifications into the KG:

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.rcs_parser \
    --input-dir "$SEARCH_DIR/classifications/" \
    --kg "$SEARCH_DIR/kg.json" \
    --output "$SEARCH_DIR/kg_classified.json"

STEP 7 — Compute saturation curve (MANDATORY for all tiers)

📖 BEFORE THIS STEP, read: references/stop_decision.md.

This step is NOT optional, even for Quick. The curve.json drives both STEP 8 stop decision and STEP 12 HTML chart rendering. If you skip it, the report shows an empty curve and PRISMA-S transparency suffers.

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.discovery_curve \
    --kg "$SEARCH_DIR/kg_classified.json" \
    --output "$SEARCH_DIR/curve.json"

The curve has saturation_estimate (0-1) + ci_low + ci_high. Optional --prior-snapshots lets you chain curves across iterations; --papers-evaluated overrides the auto-count.

STEP 8 — Decide next action (MANDATORY)

📖 BEFORE THIS STEP, read: references/stop_decision.md.

This step is NOT optional. Make the decision explicitly — based on curve.json + tier budget + intent — and state the reasoning to the user. Do not skip based on intuition.

Decision tree:

saturation < 0.6 AND budget remaining AND tier in {standard, deep, audit} → expand citations (STEP 9)
saturation > 0.85 OR budget exhausted → stop, write report (STEP 10+)
ambiguous → tell user the numbers and ask

STEP 9 — Expand citations (if applicable)

📖 BEFORE THIS STEP, read: references/citation_chasing.md.

For top-rcs papers (rcs >= 7), get the citation network:

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.openalex_helper citation-network <openalex_id> \
    --refs-limit 25 --cited-by-limit 25 \
    >> "$SEARCH_DIR/raw/citations.json"

Then loop back to STEP 5 (federate the new papers into the KG, then re-classify only the new entries in STEP 6).

STEP 10 — Enrich top-N papers (L3, optional but recommended)

📖 BEFORE THIS STEP, read: references/ss_helper_cheatsheet.md and references/crossref_helper_cheatsheet.md.

For papers with rcs >= 6, enrich with SS (influentialCitationCount + abstract fallback + tldr) and CrossRef (funder/license/clinical-trial-number). Both helpers consume a JSON list — the KG is currently dict-shaped. Convert first, enrich, then federate back; or supply a paper_list.json produced by data_materialization in STEP 12.

For Quick tier, skipping STEP 10 is acceptable — but announce the skip per Rule C ("Skipped L3 enrichment → no influentialCitationCount or funder fields; re-run at --tier standard to include this").

# Semantic Scholar — adds influentialCitationCount + abstract fallback + tldr
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.ss_helper \
    --input-file "$SEARCH_DIR/paper_list.json" \
    --mode enrich \
    --output-file "$SEARCH_DIR/paper_list.json"

# CrossRef — adds funder + license + refs + clinical_trial_number in one fetch
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.crossref_helper \
    --input-file "$SEARCH_DIR/paper_list.json" \
    --mode all \
    --output-file "$SEARCH_DIR/paper_list.json"

This adds ~135-170s for 100 papers — only do it on top-N, not the full set.

Optional (additive) — journal partitions (中科院 / JCR / SJR). The multi-platform partition layer labels every paper with all three platforms and, when a tier was requested, filters on one. Like everything else in this step it is opt-in and off by default — skip it and the report is byte-for-byte unchanged (R-19). 📖 Read references/journal_metrics.md first (it is the SSOT for sources, the ISSN join, attribution, and R-04 naming).

First use needs a one-time fetch (init-once; data is pulled at runtime into ~/.paper-search-pro/ranks/ and never bundled in the repo). If you have not fetched before, run it once (and tell the user it is a one-time step):

PYTHONPATH=$PSP_HOME python3 -m scripts.journal_rank fetch          # all three
# or a single platform: ... journal_rank fetch --platform cas
PYTHONPATH=$PSP_HOME python3 -m scripts.journal_rank info           # what's cached

Annotate (label all three platforms — do this once per result set):
```
PYTHONPATH=$PSP_HOME python3 -c "
from scripts import journal_rank, rank_filter
# ... load your papers as UnifiedPaperEntity list, then:
lk = journal_rank.load()                         # RankLookup | None (None → graceful degrade)
n  = rank_filter.annotate_papers(papers, lk)     # fills paper.journal_rank (三家全标)
"
```
journal_rank.load() returns None when nothing is cached — then this layer silently degrades (no partitions; the OpenAlex open-impact figure from the block above is still the influence placeholder) and you tell the user they can journal_rank fetch to enable partitions.
Filter (only when a tier was requested — see STEP 11 for the full flow): call rank_filter.filter_by_rank(papers, platform, tiers=…, quartiles=…, top=…). It returns (kept, filtered_out, no_platform_data) — the third bucket (journals not on the chosen platform) is reported, never silently dropped.
R-04 naming is enforced for you in the serialised dict: only JCR exposes an impact_factor (the real IF); 中科院"区" and SJR quartile are 分区/quartile.

STEP 11 — Write the executive summary

📖 BEFORE THIS STEP, read: references/summary_writer.md.

Write a ~300-word executive summary in your own words based on the classified papers:

The field's main consensus
Key methods / theoretical frameworks
Notable disagreements or open questions
Top 3-5 most influential papers (by influential_citation_count when available)
(Optional) journal tier of the leading papers, if you attached SJR metrics in STEP 10 — phrase as "SJR分区 / 期刊影响力", never "影响因子 / JCR" (R-04). Skip this bullet entirely when no metrics were attached.

Save to "$SEARCH_DIR/summary.md".

Partition default / ask / filter / report / switch flow (additive — only when partitions are in play). When you annotated the multi-platform journal_rank in STEP 10, follow this flow; it is entirely opt-in and changes nothing on the default no-partition path (R-19):

Factory default standard = JCR. The persistent default lives in config rank.default_platform (out of the box: jcr; the user can set it to cas/sjr). The default platform only labels every paper — it does not filter unless the user actually asked for a tier.
No partition mentioned → do not filter. Just show all three platforms' labels per paper (STEP 10 annotate already did this) and let the user read / refine. Never invent a tier filter the user didn't ask for.
A tier was requested (from STEP 1 intent or the user this round) → filter this once. Use the STEP 1 parse: platform + tiers/quartiles/top. A per-request tier filter is transient — never auto-persist it to config. The persistent default is only ever changed when the user explicitly says "以后都用 X".
Ambiguous bare "Q1" with no platform and no persistent default → ask one short question (you should already have asked in STEP 1; if not, ask now): "按 JCR 还是 SJR?顺带设默认吗?" Small confirmations are welcome, but do not over-ask.
Always report what this run did. After filtering, tell the user in one line: "本次按 {platform} 筛(留 N / 滤 M", plus a light offer: "可换中科院/JCR/SJR 或换档位、可设为以后的默认。" Include the per-platform attribution (journal_rank.ATTRIBUTION[platform]).
Switching standard or tier = RE-FILTER the already-annotated pool, NOT a re-search. When the user then says "换成中科院二区" or "看看 SJR Q1", do not re-run the search. annotate_papers already stamped all three platforms onto the same candidate pool, so a switch is a pure in-memory re-filter — call rank_filter.filter_by_rank(papers, new_platform, tiers=new_tiers, …) again and it returns instantly. Only when the re-filter leaves too few survivors do you go back to STEP 3 and deepen the search (retrieve more, re-annotate, re-filter). This "切换=重筛不重搜" rule is what makes partition exploration cheap.
Persisting the default (only on an explicit "以后都用 X"): set rank.default_platform in ~/.paper-search-pro/config.yaml. Tier档位 is never persisted — only the platform default is.
R-04 naming in the summary bullet too: 中科院"区" and SJR quartile are 分区 / quartile; only JCR IF(2024) is an 影响因子 / Impact Factor. The OpenAlex 2yr-mean-citedness figure is "期刊影响力" (open), never a JIF.

STEP 12 — Render the report

📖 BEFORE THIS STEP, read: references/output_files.md.

# 12a. Materialize data for the renderer (also writes sibling chart_data / paper_list / metadata / prisma_log)
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.data_materialization \
    --kg "$SEARCH_DIR/kg_classified.json" \
    --summary "$SEARCH_DIR/summary.md" \
    --query "<original query>" \
    --tier "<quick|standard|deep|audit>" \
    --search-id "$SEARCH_ID" \
    --snapshots "$SEARCH_DIR/curve.json" \
    --output "$SEARCH_DIR/report_data.json"

# 12b. Render HTML (Shadcn webartifacts — only renderer; no size cap)
#      --language $UI_LANG selects EN vs ZH UI; the bundle ships with both
#      dictionaries inlined, $UI_LANG just picks which one mounts. Resolution
#      order inside the renderer is: explicit --language > metadata.language > en.
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.html_renderer_webartifacts \
    --data "$SEARCH_DIR/report_data.json" \
    --output "$SEARCH_DIR/report.html" \
    --query "<original query>" \
    --language "$UI_LANG"

# 12c. MD report (uses materialized-dir for speed)
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.md_report \
    --materialized-dir "$SEARCH_DIR" \
    --query "<original query>" \
    --tier "<quick|standard|deep|audit>" \
    --output "$SEARCH_DIR/report.md"

# 12d. Exports (BibTeX / RIS / CSV / papers.json — only rcs >= 5 by default)
PYTHONPATH=$PSP_HOME \
  python3 -m scripts.generate_exports \
    --kg "$SEARCH_DIR/kg_classified.json" \
    --output-dir "$SEARCH_DIR/" \
    --min-rcs 5

data_materialization accepts --wall-clock-seconds if you tracked elapsed time yourself; otherwise the helper computes it from session timestamps when available.

STEP 13 — Write PRISMA-S log

📖 BEFORE THIS STEP, read: references/prisma_s_checklist.md.

PYTHONPATH=$PSP_HOME \
  python3 -m scripts.prisma_s_logger \
    --search-id "$SEARCH_ID" \
    --kg "$SEARCH_DIR/kg_classified.json" \
    --user-query "<original query>" \
    --tier "<quick|standard|deep|audit>" \
    --query-plan "$SEARCH_DIR/query_plan.json" \
    --snapshots "$SEARCH_DIR/curve.json" \
    --output "$SEARCH_DIR/execution_log.json"

This captures the 16 PRISMA-S items for transparency / audit.

STEP 14 — Open the report + report to user

First, auto-open the HTML report in the user's default browser (do NOT wait for the user to ask). Platform-aware Bash:

# macOS — most common dev setup
open "$SEARCH_DIR/report.html"
# Linux fallback — xdg-open "$SEARCH_DIR/report.html"
# Windows fallback — start "" "$SEARCH_DIR/report.html"

Use open on macOS by default. If it fails (rare — only bare Linux containers), fall through to xdg-open then start. Do NOT skip this step — the user just waited 5-30 minutes for the report; they should see it the moment it's ready.

Then tell the user:

"Opened report in your default browser." (1 line confirmation)
Where the report is on disk (absolute path: $SEARCH_DIR/report.html) — so the user can find it later
Top findings (3-5 sentences from your executive summary)
Any caveats — including any steps you skipped per Rule C (e.g. "PubMed wasn't queried because no medical signals were detected", "Skipped STEP 10 L3 enrichment because Quick tier; re-run at standard to include funder/license fields")

Output convention

📖 See references/output_files.md for the full directory layout. All paths below are relative to the user's working directory (PWD) — never the Skill asset directory.

$(pwd)/paper-search-results/<search_id>/
├── report.html              # Main deliverable (Shadcn style)
├── report.md                # Markdown copy
├── papers.csv               # Spreadsheet export
├── papers.bib               # Citation manager import (BibTeX)
├── papers.ris               # Alternative citation format
├── papers.json              # Full structured data
├── kg_classified.json       # Internal KG with RCS scores
├── summary.md               # Your executive summary
├── execution_log.json       # PRISMA-S 16-item log
├── report_data.json         # Renderer bundle
├── chart_data.json          # Sibling: chart series
├── paper_list.json          # Sibling: per-paper list
├── metadata.json            # Sibling: run metadata
├── prisma_log.json          # Sibling: PRISMA log JSON view
├── curve.json               # Saturation snapshot
├── query_plan.json          # STEP 1 output
├── raw/                     # Raw per-source dumps (openalex.json, pubmed.json, arxiv.json, citations.json)
├── batches/                 # batch_NNN.jsonl files
└── classifications/         # batch_NNN_result.json files

Error handling

📖 See references/error_handling.md. Common cases:

| Error | What to do | |-------|-----------| | Config missing keys | Direct user to references/setup.md, halt | | Rate limit (SS 429 / NCBI 429) | Helper auto-retries; if persistent, drop that enricher | | OpenAlex 404 on DOI | Use title search fallback (helper handles) | | L2 booster returns 0 papers | Skip silently, note in PRISMA-S log via STEP 13 | | SubAgent classifier returns invalid JSON | rcs_parser.py has 5-layer fallback (regex parse) | | HTML output size | No size cap or fallback — html_renderer_webartifacts always produces the full Shadcn bundle. Typical 250-paper report is ~1.7 MB; pathological 1000+ paper Audit may reach 5-10 MB. All modern browsers handle 10+ MB HTML cleanly. |

References (progressive disclosure — read the one for the step you're on)

You won't read all of these every run, and shouldn't. Read a step's reference when you reach that step and it's non-trivial for the case (Rule D). core = read for its step; cond = only when its trigger fires.

| File | Load | Read when | |------|------|---------| | tier_decision.md | core | choosing the tier (before STEP 0) | | setup.md | core | STEP 0 — config + 5-key acquisition | | runtime_bootstrap.md | cond | STEP 0/1 — only if $PSP_HOME env injection failed, or you need the full install-path list / language-routing rationale | | query_planner.md | core | STEP 1 — PICO / SPIDER / PEO frameworks | | source_routing.md | core | STEP 1 language scope (§"Language scope" SSOT) + STEP 2 routing + STEP 5 field-priority merge | | openalex_helper_cheatsheet.md | core | STEP 3 + STEP 9 — subcommands, params, gotchas | | pubmed_helper_cheatsheet.md | cond | STEP 4 — only if PubMed enabled | | arxiv_helper_cheatsheet.md | cond | STEP 4 — only if arXiv enabled | | classifier_subagent_prompt.md, rcs_rubric.md | core | STEP 6 — SubAgent prompt + RCS 0-10 rubric | | stop_decision.md | core | STEP 7 + STEP 8 | | citation_chasing.md | cond | STEP 9 — only if expanding citations | | ss_helper_cheatsheet.md, crossref_helper_cheatsheet.md | cond | STEP 10 — only if enriching top-N | | summary_writer.md | core | STEP 11 | | journal_metrics.md (SSOT) | cond | STEP 1 / STEP 10-11 — only if the user wants journal partitions (中科院 / JCR / SJR) or SJR metrics; ISSN join, attribution, R-04 naming | | output_files.md | core | STEP 12 — output dir layout (PWD-relative) | | prisma_s_checklist.md | core | STEP 13 | | agent_mode.md (SSOT) | cond | only when another agent / headless calls this Skill — agent_search envelope + flags | | error_handling.md | cond | any unexpected error |

Examples

Example 1: Quick scan (5-8 min)

User: "find 5-6 high-impact papers on prospect theory in decision making, classics + a couple recent ones"

You: Pick Quick tier (signals: "5-6", "high-impact", short query). Run STEP 0-2 lightweight. In STEP 3 use openalex_helper seminal for classics + openalex_helper search for recent (year >= 2020). No L2 boosters in STEP 4 (pure social science). In STEP 6 classify 20-30 papers via 2 parallel SubAgents in one message. STEP 7 + 8 still run (curve renders in the report). Announce skip of STEP 9 + STEP 10 per Rule C. Render report.

Example 2: Standard ZH (10-17 min)

User: "用 paper-search-pro 帮我找一些关于工作记忆训练干预的文献老板让我看我对这块完全不懂要给老年人群体的最好谢谢🙏"

You: Pick Standard tier (default; signals: "找一些", "老板让我看"). Detect medical signal ("干预" + "老年") in STEP 2 → enable PubMed enricher. Query plan: PICO (P=elderly, I=working memory training, O=cognitive outcomes). OpenAlex double-sort top-100 in STEP 3. PubMed enrich of openalex.json in STEP 4. Federate in STEP 5 (dict output). Classify 60-180 papers via 4 batches × 5 SubAgents — all 5 Tasks in one message (Rule B). STEP 7 curve, STEP 8 expand if saturation < 0.6. Render report.

Example 3: Deep × Lit review writing (30-45 min)

User: "I'm writing a proper literature review article on attachment and human-robot interaction in elderly care contexts. Need real depth..."

You: Pick Deep tier ("proper literature review article" + "real depth"). Cross-domain (psychology + CS) in STEP 2 → enable arXiv freshness sentinel. SPIDER plan in STEP 1. OpenAlex double-sort top-200 + reviews subcommand in STEP 3. Classify 200+ papers via 8 batches in STEP 6 — dispatch 5 parallel Tasks per message, two waves. STEP 9 expand citations 2 hops. STEP 10 enrich top-50 with SS + CrossRef. Render report with PRISMA-S log.

Example 4: Audit × SR-prep (2-3 hr)

User: "Need help — preparing a systematic review on dietary interventions for IBS in adults. Inclusion criteria: RCTs, adult populations (≥18), low-FODMAP or fiber-based interventions, English-language, published 2010-present."

You: Pick Audit tier ("systematic review" + PICO + IC). Show limitations warning first ("This is not a PRISMA replacement — it's SR-prep assist. Cochrane Library + Embase still needed for full SR rigor."). Get user confirmation. STEP 4 use pubmed_helper search-mesh "Irritable Bowel Syndrome" --pub-type "Randomized Controlled Trial" for independent MeSH search. STEP 3 also call openalex_helper journal-list --preset Cochrane. STEP 10 add CrossRef enrichment for funder + clinical-trial-number. Render with PRISMA flow chart in STEP 12.

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