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
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).
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:
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, , …), 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.
--min-impact
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
📖 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:-${CODEBUDDY_SKILL_DIR:-}}}"# explicit / agent-injectedif [ -z "$PSP_HOME" ]; then# else scan known installsfor 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="$base/skills/paper-search-pro" && breakdonefi
[ -z "$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.
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.
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:
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:
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.:
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:
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):
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:
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):
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):
📖 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:
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:
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.
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)
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").
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 "$SEARCH_DIR/" \
--min-rcs 5
data_materialization accepts --wall-clock-seconds if you tracked elapsed time yourself; otherwise the helper computes it from session timestamps when available.
STEP 13 — Write PRISMA-S log
📖 BEFORE THIS STEP, read: references/prisma_s_checklist.md.
This captures the 16 PRISMA-S items for transparency / audit.
STEP 14 — Open the report + report to user
First, auto-open the HTML report in the user's default browser (do NOT wait for the user to ask). Platform-aware Bash:
# macOS — most common dev setup
open "$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
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.
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.
