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name: paper-navigator description: "Find and read academic papers (S2 + arXiv). Disambiguate ambiguous queries, search by keyword + citation graph + recommendations + snippets, judge relevance against an authored rubric, and read with L1/L2/L3 strategy. Trigger phrases: find papers, search papers, related work, citation analysis, recent advances, read this paper, baseline with code. Do NOT use for: survey reports (research-survey), idea generation (research-ideation), Related Work sections (paper-writing)." allowed-tools: "write_file edit_file read_file think_tool execute" metadata: author: EvoScientist version: '3.4.1' tags: [core, research, literature, papers, search, rubric]

Paper Navigator

Find and read academic papers. Route by intent, judge by relevance.

        User
         │
         ▼
   ┌── Router ──┐
   │            │
   ▼            ▼
 POINT      LIST/ITERATIVE
(1 paper)   (Probe + up to 3 paper rounds:
             R2 breadth / R3 deepen / R4 close)

The agent does relevance judgment — no LLM-as-judge is called, no numeric scoring. You author the rubric, you triage each paper, you rank by relevance.

Setup

Script paths in this document are relative to this skill's directory. Run via python scripts/<name>.py.

Dependencies: pip install deepxiv-sdk httpx (also listed in requirements.txt at the skill root). Install into the environment the user is working in.

arXiv access (arxiv_monitor, scholar_search fallback) uses the DeepXiv SDK: pip install deepxiv-sdk, then deepxiv token once to provision a free API token (saved to ~/.env). The skill reads the token from DEEPXIV_API_TOKEN/DEEPXIV_TOKEN in the environment, or from ./.env / ~/.env.

Env var	Used by	Notes
S2_API_KEY	All S2 scripts	Without it: scholar_search falls back to arXiv (via DeepXiv); citation_traverse / recommend / snippet_search are disabled
DEEPXIV_API_TOKEN	arxiv_monitor, scholar_search fallback	Get a free token: deepxiv token (writes ~/.env). Also read from DEEPXIV_TOKEN and ./.env/~/.env. ~10,000 req/day
JINA_API_KEY	fetch_paper	Free tier works without key
GITHUB_TOKEN	github_search, find_code	Higher rate limits
PAPER_NAV_PAPERS_DIR	fetch_paper full text	No default — set or pass --metadata-only

Full env-var list: references/env-vars.md.

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Five Red Lines (always)

1. Track history. Don't re-run a query you already ran. Empty result → change angle, not synonyms.
2. Search a gap, not a vibe. Every query maps to one missing piece of information. No stacked-keyword bags.
3. One query = one concept. Split comparisons (A vs B), multi-property asks, and multi-year spans into separate calls.
4. Never hallucinate. Every fact (title, author, year, citation count, content) comes from a tool result.
5. Quote-or-zero. When you claim a paper meets a criterion, quote a ≤80-char span from its abstract / tldr / snippet. No quote → do not claim the paper meets that criterion. (This guards against hallucination; it does not drive a numeric score.)

---

Router

Branch	User signal	Cadence	Output
POINT	Title quoted, URL, arXiv/DOI/PMID/S2 ID, "read this paper"	1 call	Paper Card
LIST (default)	"find papers about X", "is there a paper that …?", "papers satisfying A and B"	Probe + up to 3 rounds (R2/R3/R4)	Shortlist with per-criterion evidence
ITERATIVE	"survey of X", "30+ papers on Y", called from research-survey / research-ideation	Probe + up to 3 rounds (R2/R3/R4)	Ranked table (hand off to research-survey for the report)

Default to LIST when unsure. Don't add survey / review to LIST queries — it down-ranks the canonical research papers the user wants.

Output format follows the caller: the Output column above is the structured form (skill callers). Direct user calls default to Narrative — see Step 6 "Output mode".

Ambiguous query (project nickname, codename, single capitalized word with zero hits) → run scholar_search exact + web/GitHub search first to resolve identifiers, then re-route.

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POINT branch (known paper)

Input	Command	Output
URL	python scripts/fetch_paper.py --url <URL>	Paper Card + reading notes (see references/reading-strategy.md for L1/L2/L3)
Title quoted	python scripts/match_paper_by_title.py --title "<title>" (add --fallback-search for typos)	Paper Card
Bare ID (arXiv / DOI / S2 / CorpusId)	python scripts/fetch_paper.py --paper-id <ID> --metadata-only	Paper Card

Paper Card:

📄 **<Title>**
Authors: <First Author> et al. | Year: <Y> | Venue: <V>
Citations: <N> | ID: <ArXiv:xxxx.xxxxx> | DOI: <...>
TLDR: <one sentence>

Stop here. Do not chain to citation expansion unless asked.

---

LIST / ITERATIVE branch — 6 steps

Step 1: Parse intent

State in one sentence: the research object (specific technique / concept) and the constraints (domain, task, recency, exclusions). Confirm the router branch. When the user gives only a bare noun (concept / model / algorithm / benchmark name) with no direction, default intent is "trace the lineage" — foundations, evolution, current state — not applications or a generic survey.

Step 2: Author the RUBRIC (via think_tool)

Emit a structured block before any search. It persists across rounds and every later step references it.

RUBRIC for "<user query verbatim>"
Branch: LIST | ITERATIVE
Criteria (2–4, atomic; mark each [core] or [secondary]):
  C1 [core]     <what the paper MUST do/be — one sentence>
  C2 [core]     <...>
  C3 [secondary] <...>
Named entities to preserve verbatim: [<ent1>, <ent2>, ...]
Angle tags (3–5 sub-topic axes): [<tag1>, <tag2>, <tag3>]
Recency signal: [none | recency cue — defined in Step 3]
Disqualifiers: [<auto-reject if abstract shows this>]

Rules:

• Criteria atomic (one condition each), non-redundant. Mark each [core] (must-have) or [secondary] (nice-to-have) — this guides relevance ranking, no weights, no math.
• Named entities = proper-noun / technical-term anchors from the user's query. Every entity appears verbatim in ≥1 query across Probe + R2.
• Angle tags = sub-topic axes (method, task, dataset, evaluation, domain, …). No two queries in the same round share a tag.
• Recency signal = whether the user query carries a recency cue (see Step 3 recency trigger). Drives --year-min and the Step 6 recency tie-break.
• Disqualifiers = "specifically X, not Y" exclusions. Tripping a disqualifier → Irrelevant.

For ITERATIVE, criteria can be lighter (e.g. covers topic + is canonical); disqualifiers may be empty.

Step 3: Search — Probe then Multi-round (Breadth → Deepen → Close)

Probe first to grasp the user's need comprehensively, then run up to 3 paper rounds (R2 breadth / R3 deepen / R4 close). Decide round-by-round whether more rounds are needed (Step 5 gate) — do NOT fix a round count. There is no R5; the cap is 3 paper rounds.

Probe (prerequisite — NOT counted in the 3 rounds, 2 parallel queries): establish a comprehensive understanding of the need and lift named entities / angle gaps.

• Q-broad — canonical phrasing of the topic (angle: general)
• Q-narrow — a specific mechanism / sub-question / method (angle: tagged)

python scripts/scholar_search.py --query "<Q-broad>"  --limit 15 --sort-by relevance --output /tmp/pool.jsonl --append
python scripts/scholar_search.py --query "<Q-narrow>" --limit 15 --sort-by relevance --output /tmp/pool.jsonl --append

--output --append auto-dedupes by paperId across rounds (built into the script), so a paper found by two queries is written once. Read /tmp/pool.jsonl to inspect (Step 4 triage). From Probe titles + tldrs, lift:

• recurring named entities (algorithm / benchmark / dataset / model names),
• angle gaps (Step-2 tags not seen),
• vocabulary from adjacent communities.

R2 — Breadth (default — skipped only via the Step 5 early exit; 2–3 parallel queries). Broad academic queries on the core object + canonical terms lifted from Probe. The main axis is the user's core object + canonical mapping; Probe only supplies high-confidence supplements.

R3 — Targeted deepening (optional, when Step 5 gate says CONTINUE, 2–3 parallel queries). Driven by the gap→strategy map below — fill the specific gap Step 4 triage exposed.

R4 — Closing (optional, 2–3 parallel queries). Fill whatever key gap remains after R3: a missing representative work, a strong baseline, a counter-example, or an uncovered sub-direction.

Gap→strategy map (R3/R4 are driven by the Step-4 triage gap, not vibes):

Gap surfaced by Step 4 triage	R3/R4 strategy
Foundational work drowned by recent papers	--year-max, search the original mechanism / early terminology
User wants SOTA / frontier, or R2 skewed old	--year-min last 2 years
Thin single-source evidence	swap terminology / team / benchmark for multi-source corroboration
Incomplete A-vs-B comparison	separately fill A, B, and an upper-topic query (no survey/review terms)
Contradictory findings	verification query, prefer authoritative venue / high-cite / direct experiment

Per-query rules:

• 3–6 words preferred (English academic terms); <3 over-recalls, >6 dilutes ranking.
• Use academic terms (mechanism, benchmark); no how it works phrasing.
• Do NOT add survey / review / tutorial terms — they bias results toward review papers and crowd out the research papers the user wants. Only use them when the user explicitly asks for a survey/review.
• Bare entity names; no paper / pdf / arxiv / original.
• Split comparisons / multi-property; if short of 2–3 queries, fill with upper-topic or representative method.
• Time intent goes into --year-min/max params, never year words in the query.
• Forbidden: "…", (..), OR, AND, |, site:, filetype:.
• No two queries in one round may share >60% of content tokens (after stop-words).

Recency trigger (query layer). When the user query contains a recency signal (最新 / 近年 / 近期 / 近两年 / 前沿 / SOTA / latest / recent / state-of-the-art), set --year-min to last 2 years from R2 onward — Jan 1 of the year before the current system year (e.g. system date 2026-08 → --year-min 2025). Do not extend to 3–4 years.

Without S2_API_KEY: swap scholar_search for arxiv_monitor --keywords "<variant>" --match-mode flexible --days 3650.

Citation expansion (ITERATIVE, or LIST after ≥3 All-core/Partial seeds):

python scripts/citation_traverse.py --paper-id <SEED> --direction co-citation --limit 15 --output /tmp/pool.jsonl --append
python scripts/citation_traverse.py --paper-id <SEED> --direction forward --limit 20 --min-citations 20 --year-min 2022 --output /tmp/pool.jsonl --append
python scripts/recommend.py --positive <SEED1>,<SEED2> --limit 15 --output /tmp/pool.jsonl --append

Step 4: Triage — All-core / Partial / Irrelevant

After every round, classify each new paper and stamp a per-criterion mask (✓ / ~ / ✗) over the RUBRIC — no numeric scoring. The mask is what makes conjunctive queries ("papers satisfying A and B") terminate correctly: only a paper with ✓ on every [core] criterion is All-core. Emit a think_tool block:

TRIAGE round=<n>  query="<q>"
  All-core   (k): <paperId> "<title-≤60>" Y=<year> · [C1✓ C2✓ (C3~)]   every [core] ✓
                 C1: "<≤80-char quote>"
                 C2: "<≤80-char quote>"
  Partial    (k): <paperId> "<title>" Y=<year> · [C1✓ C2✗]              some [core] ✓
                 C1: "<≤80-char quote>"
  Irrelevant (k): <paperId> "<title>"                                   no [core] ✓, or trips disqualifier — drop

Tier	Mask	Quotes
All-core	every [core] criterion ✓ (no ✗ on any [core])	one ≤80-char quote per [core] criterion
Partial	at least one [core] ✓, but some [core] ✗/~; or only [secondary] support	one quote per ✓ [core] criterion
Irrelevant	no [core] ✓, or trips a disqualifier	none — drop from later rounds

✓ = abstract/tldr clearly supports. ~ = partial / inferable. ✗ = no support or contradicts. [secondary] criteria don't set the tier but still get a mask symbol.

Rules:

1. Dedup across rounds by paperId first, then normalised title. Keep the stronger mask.
2. Disqualifier check beats all other matches → Irrelevant.
3. Re-diagnose gaps: note any [core] criterion with 0 ✓ across All-core+Partial, and any angle tag with 0 All-core/Partial → that's the next refine target (feeds the Step 3 gap→strategy map).
4. No fabrication: missing abstract → stamp ~, do not infer from training data.

The per-criterion quotes collected here are exactly what the Step 6 Rank-1 bar and the structured LIST template cite — do not skip them.

Snippet upgrade for borderline papers (abstract silent on a [core] criterion): batch-fetch real body text:

python scripts/snippet_search.py --query "<criterion phrase>" \
  --paper-ids "CorpusId:1,CorpusId:2,..." --limit 50

Step 5: Saturation Gate

After Probe and each round, decide CONTINUE vs STOP by whether key gaps remain — not by counting papers.

Early exit after Probe (single-recommendation / conjunctive queries only, K=1–2): for "is there a paper that …?", "recommend a paper", "what's the canonical X", or conjunctive "papers satisfying A and B" queries, if an All-core paper already covers every [core] criterion, STOP without running further rounds. This preserves the fast path where one probe hit settles a POINT-like query (≈2 queries), which matters under keyless S2 rate limits. For broader question shapes ("find papers about …" K=3–5, or 30+-paper ITERATIVE), do not use this early exit — apply the full STOP conditions below.

STOP when ALL hold:

• ≥1 All-core paper exists — a single paper with ✓ on every [core] criterion. This is required so conjunctive queries like "papers satisfying A and B" can't pass on two different papers that each cover only one side, AND
• every angle tag has ≥1 All-core/Partial paper, AND
• no key claim rests on a single source,
• OR further rounds stop surfacing anything new (empty recall / all duplicates).

CONTINUE to the next round (R3 / R4) otherwise, driven by the gap→strategy map:

• 0 All-core papers → fill the [core] criterion still ✗.
• An angle tag has 0 All-core/Partial → open that angle.
• A key claim rests on a single source → multi-source corroboration.

Re-decompose (rubric is wrong) if R2 returns 0 All-core AND 0 Partial across the board: report the strongest Partial candidate(s) + ask the user to relax a criterion.

Round caps: LIST and ITERATIVE up to 3 paper rounds (R2/R3/R4). POINT is a single fetch (no multi-round). If still not saturated at the cap, go to Step 6 and report which criteria / angle tags were not covered.

The gate is mechanical about gaps — do not skip rounds because "the results look right"; do not run extra rounds once the STOP conditions hold. The single-All-core-suffices shortcut applies only to the K=1–2 early exit above, not to broader queries.

Step 6: Rank and Output

Gather: every All-core and Partial paper from across all rounds (dedup by paperId). Drop Irrelevant.

Rank by relevance — the model judges, no numeric score. Order the gathered papers by how directly each answers the user's question: All-core before Partial; a paper satisfying every [core] criterion ranks above one satisfying only some. This is a judgment call, not a formula — do not compute a weighted_total.

Recency-aware tie-break. When the RUBRIC flagged a recency signal (cues defined in Step 3), break ties / near-ties in favor of the more recent paper (year DESC), applied after relevance. Recency rides behind relevance, never ahead of it.

K (soft ceiling — prevents pool-dumping):

Question shape	K
"Exactly N papers"	N
"Is there a paper that …?" / "Recommend a paper"	1–2 (bold top-1)
"Find papers about …"	3–5
"Survey of …" / ITERATIVE	≤ 10 (soft cap) + 1–2 surveys if the user explicitly asked for them

K is a soft guide, not a formula. For broad "survey / categorize the field" queries the dominant failure mode is dumping the whole accumulated pool (often 30–50 papers, most off-criterion) into the output, burying the few on-criterion papers. Rank by relevance, keep the top K, and move the rest to an "Also relevant (not ranked)" list — never pad the ranked list with weak papers to reach a count.

Rank-1 quality bar. For single-recommendation queries ("is there a paper that …?", "recommend a paper", "what's the canonical X") the bolded top-1 must be an All-core paper — clearly satisfying every [core] criterion with a quote. Rank 1 carries disproportionate weight in user perception; fronting a Partial paper at top-1 reads as a confident wrong answer. If no paper clears the bar, lead with "No fully-matching paper found" and present the strongest near-miss honestly with its gaps.

If no All-core paper survives after the round cap, report "no fully-matching paper found", list strongest Partial candidates + their gaps, stop.

Output mode (caller inference). Pick the format by what the request demands, not by an explicit flag:

• Structured (formats below) — when the request demands machine-consumable output: a ranked list/table, per-criterion evidence, or hand-off to a downstream skill. Default for skill callers (research-survey, research-ideation, paper-writing, experiment-pipeline).
• Narrative (see "Narrative output" below) — the default for direct user calls: natural-language questions with no structured-output demand.

Structured output formats (skill callers):

LIST (shortlist with evidence):

**Top matches:**
- **<paperId>** "<Title>" — <Authors> et al., <Year>, <Venue>, cited by <N>. <URL>
  - C1 [core]: "<quote>"
  - C2 [core]: "<quote>"
  - C3 [secondary]: "<quote>"

**May also be relevant:**
- <paperId> "<Title>" — <Authors> et al., <Year>, cited by <N>. <URL> (Partial: only C3)

ITERATIVE (ranked table):

| # | Title | Authors | Year | Venue | Cited by | Link |
|---|-------|---------|------|-------|----------|------|
| 1 | …    | … et al. | 2024 | NeurIPS | 1234 | <URL> |

POINT: Paper Card (above).

Narrative output (direct user callers).

Deliver structured knowledge, not a search trace. Strip process words before output (Probe, R2/R3/R4, All-core/Partial, "rounds done") unless the user explicitly asks for a trace.

• Information-first, not list-first. Unless the user only wants a paper list, do not collapse the answer into "title + one-line contribution". Build the cognitive structure the user needs (timeline / topic grouping / comparison / mechanism breakdown / evidence verification / reading path), then place papers into it as evidence nodes.
• One main form + 2–3 auxiliary forms. The main form carries the answer's logic (timeline, topic grouping, comparison, mechanism breakdown, evidence grading, reading path, mini-survey); 2–3 auxiliary forms (paper card, table, evidence grading, annotated bibliography, reader payoff) aid readability. Do not stack every form.
• Intent → form (condensed): latest/SOTA → status-judgment + table; origin/foundation → timeline + source-paper analysis; A-vs-B → conclusion + dimension comparison; mechanism → mechanism breakdown + evidence interleaving; benchmark/data → verification + evidence table; landscape → topic grouping + reading path.
• Citation rules: a core paper shows Title (Venue Year) on first mention (never author-only like Zhang et al.); every cited paper carries its returned [N]. Tables MAY contain [N] markers. At the end of the answer, list every cited paper by number in IEEE style (with citation count appended): [N] A. Author et al., "Title," Venue, Year, cited by N. [Online]. Available: URL. IEEE rules: author names as Initial. Surname (e.g. A. Vaswani); join multiple authors with commas and and; ≥3 authors → A. Firstauthor et al.; title in double quotes; then Venue, Year; append cited by N (the tool's citationCount); end with [Online]. Available: URL for the link. Omit a field only if the tool genuinely did not return it (never fabricate); authors and citation count must appear whenever returned.
• Length: single-point 600–1200 words; comparison/retrieval 1200–2500; landscape/timeline 2000–3500. Do not sacrifice evidence structure for brevity.
• Quote-or-zero still applies — every claim a paper is used to support is still backed by a ≤80-char quote (Red Line 5, anti-hallucination); the narrative just renders it as [N] instead of showing the raw quote.

Pre-output checklist (mandatory). Before emitting the answer, verify each box.

• Pool gathered from every round's triage, deduped by paperId, Irrelevant excluded.
• Ranked by relevance (judgment, not a numeric score) — All-core before Partial.
• Recency tie-break applied when a recency signal is present (year DESC after relevance).
• Rank-1 clears the bar for single-recommendation queries (clearly satisfies every [core] criterion with a quote) — or you've reported "No fully-matching paper found".
• Every cited paper has ≥1 supporting quote for the claim it's used for (quote-or-zero, Red Line 5 — anti-hallucination).
• Output ≤ K (soft cap); surplus relevant papers sit in "Also relevant (not ranked)", not the ranked list.
• Narrative mode only — every cited paper appears in the end-of-answer numbered reference list in IEEE style with authors + citation count ([N] A. Author et al., "Title," Venue, Year, cited by N. [Online]. Available: URL), and the body strips process words (Probe / R2 / All-core/Partial).

If any box is unchecked, return to Step 6 — do not output.

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Tool Cheat Sheet

Need	Script	Notes
Keyword search	scholar_search.py	S2 → arXiv fallback on missing key / 429
Title → record	match_paper_by_title.py	S2 exact-match; --fallback-search for typos
Citation graph	citation_traverse.py	--direction forward/backward/co-citation; --min-citations; --year-min/max; --smart-sort; --enrich
Similar papers	recommend.py	seed-based; --per-seed for diverse seeds
Author papers	author_search.py	--sort-by year/citations
New arXiv	arxiv_monitor.py	--categories cs.CL or --keywords "x,y" --match-mode flexible
Trending	trending.py	citation velocity
Body-text snippets	snippet_search.py	--paper-ids c1,c2,c3 --limit 50 (1 call, not N)
Fetch full text	fetch_paper.py	Saves to $PAPER_NAV_PAPERS_DIR/<id>.md; stdout truncated to 2000 chars
Code repo (known paper)	find_code.py --arxiv-id <ID>	Official repo lookup
Code repo (unpublished)	github_search.py	When no arXiv ID exists
HF leaderboard / SOTA	sota.py	sorted by downloads
HF datasets	dataset_search.py	Query short-name (imdb, sst2), not task description
Saturation gate (optional)	saturation.py	JSONL log of per-round yields; estimate returns STOP/CONTINUE

All discovery scripts: --limit N, --json, --output FILE, --append; accept S2 / arXiv / DOI / CorpusId IDs. --output --append auto-dedupes by paperId across rounds (within-batch + cross-file), so the pool stays clean.

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Rate limits

API	Without key	With key
Semantic Scholar	~1 req / 3s, no parallel	100 req/min, parallel OK
arXiv	1 req / 3s (courtesy)	N/A
GitHub	10 req/min	5,000 req/hr
HuggingFace	500 req / 300s	Higher with HF_TOKEN

Global S2 pacer + circuit breaker (5 failures → 60s cooldown). Retries: 3s / 6s / 12s / 24s / 48s.

Without S2_API_KEY: use scholar_search (arXiv fallback) + arxiv_monitor. Skip citation_traverse / recommend / snippet_search — they're S2-only; do not retry.

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References

File	Read when
references/env-vars.md	Setting environment variables
references/search-principles.md	Per-query rules, gap diagnosis, rate-limit recovery
references/iterative-collection.md	ITERATIVE corpus collection (30+ papers): phase mapping, citation expansion, escape hatches
references/disambiguation.md	Query is a project nickname / codename
references/reading-strategy.md	L1 / L2 / L3 reading framework
references/api-reference.md	S2 / arXiv / Jina / HF / GitHub endpoint details
references/arxiv-categories.md	arXiv category codes
references/output-formats.md	Baseline / Disambiguation / Reading-Notes / Citation-Graph templates

References are self-contained. Don't chain between them — return here to re-route.

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Hand off to

Goal	Skill
Survey report	research-survey
Idea generation	research-ideation
Related Work section	paper-writing
Baseline + experiment	experiment-pipeline