Scholar Deep Research

End-to-end academic research workflow that turns a question into a cited, structured report. Built for depth: multi-source federation, transparent ranking, citation chasing, and a mandatory self-critique pass before the report ships.

When to use

Explicit triggers: "literature review", "research report", "state of the art", "survey the field", "what's known about X", "deep research on Y", "systematic review", "scoping review", "compare papers on Z".

Proactive triggers (use without being asked):

• User asks a factual question whose honest answer is "it depends on the literature"
• User frames a research plan and needs the background section
• User is drafting a paper intro/related-work and hasn't yet scoped prior work
• User proposes a method and asks whether it's novel

Do not use when: a single known paper answers the question, the user wants a tutorial (not a survey), or they're debugging code.

Guiding principles

1. Scripts over vibes. Every search, dedupe, rank, and export step runs through a script in scripts/. The same input should produce the same output. Do not improvise ranking or counting by eye.
2. Sources are federated, not singular. OpenAlex is the primary backbone (free, 240M+ works, no key). arXiv (CS/ML/physics preprints), Crossref (DOI metadata), PubMed (biomedical), DBLP (CS conferences/journals), bioRxiv (life-sci preprints via Europe PMC), and Exa (open-web, requires EXA_API_KEY) fill gaps. Semantic Scholar is also script-driven — build_citation_graph.py --source s2|both is the spine path for Phase 4, with better CS / arXiv / cross-disciplinary coverage than OpenAlex; the two graphs disagree more than you'd expect. The asta MCP tools (mcp__asta__*) and Brave Search are skin — used opportunistically for relevance ranking or non-academic context, never on the critical path. If MCP times out, research continues.
3. State is persistent. Everything goes through research_state.json. Queries ran, papers seen, decisions made, phase progress. Research becomes resumable and auditable.
4. Citations are anchors, not decorations. Every non-trivial claim in the draft carries [^id] where id matches a paper in state. Unanchored claims are treated as hallucinations and fail the gate.
5. Saturation, not exhaustion, is the stop signal. A phase ends when a new round of search adds <20% novel papers AND no new paper has >100 citations.
6. Self-critique is a phase, not a checkbox. Phase 6 reads the draft with adversarial intent. Its output goes into the report appendix.

The 8-phase workflow (Phase 0..7)

Phase 0: Scope       → decompose question, pick archetype, init state
Phase 1: Discovery   → multi-source search, dedupe
Phase 2: Triage      → rank, select top-N for deep read
Phase 3: Deep read   → extract evidence per paper
Phase 4: Chasing     → citation graph (forward + backward)
Phase 5: Synthesis   → cluster by theme, map tensions
Phase 6: Self-critique → adversarial review, gap finding
Phase 7: Report      → render archetype template, export bibliography

Each phase writes to research_state.json before advancing. If the user pauses or a session crashes, the next run reads the state and picks up from the last completed phase.

Phase 0 — Scope

Before searching anything, decompose the question.

1. Restate the question in one sentence. Surface ambiguities.
2. PICO-style decomposition (or equivalent for non-biomedical fields):
   • Population / Problem — what system, species, setting, or phenomenon?
   • Intervention / Independent var — what method, factor, or manipulation?
   • Comparison — against what baseline or alternative?
   • Outcome — what is being measured or claimed?
3. Pick an archetype that matches user intent (see references/report_templates.md):
   • literature_review — what is known about X (default)
   • systematic_review — rigorous PRISMA-lite, comparison of many studies on one narrow question
   • scoping_review — what has been studied and how (breadth over depth)
   • comparative_analysis — X vs Y, head-to-head
   • grant_background — narrative background + gap for a proposal
4. Draft keyword clusters — 3-5 Boolean clusters covering synonyms, acronyms, and variant spellings. Include a "negative" cluster (terms to exclude).
5. Initialize state:

   python scripts/research_state.py --state research_state.json init \
     --question "<restated question>" \
     --archetype literature_review
   

   (--state is top-level and applies to every subcommand; init itself takes --question, --archetype, and optional --force.)

When in doubt about archetype, ask the user. The choice shapes everything downstream.

Phase 1 — Discovery

Run searches across all available sources, in parallel where the source can take it. OpenAlex is primary; the others fill gaps.

Where parallelism actually pays off. The right place to fan out is Phase 3 (one agent per paper to read PDFs concurrently — see references/agent_prompts/phase3_deep_read.md). At Phase 1 the bottleneck is the upstream API, not local compute, and parallel fan-out across the same source mostly buys 429s and sticky cooldowns. The skill's bias should be: parallel between different sources, serial within one source. Concretely:

• Parallel-friendly: OpenAlex (polite-pool, very tolerant), Crossref (polite-pool), Exa (paid quota), bioRxiv (Europe PMC).
• Self-serialised (file-locked, automatic): arXiv (≥3s/req), PubMed (≥0.34s/req without NCBI_API_KEY, ≥0.10s with), DBLP (1s buffer to avoid SSL EOF flakes).

The serialised sources use a per-source file lock under ${SCHOLAR_CACHE_DIR:-.scholar_cache}/rate/<source>.lock, so even N parallel search_arxiv.py invocations from the same agent will queue automatically and sleep the right gap — no agent-side coordination required, but parallel calls don't speed those sources up either, just don't error.

# Primary (no API key, always available)
python scripts/search_openalex.py --query "<cluster 1>" --limit 50 --state research_state.json
python scripts/search_openalex.py --query "<cluster 2>" --limit 50 --state research_state.json

# Domain-specific (use when relevant)
python scripts/search_arxiv.py    --query "<cluster>" --limit 50 --state research_state.json  # CS/ML/physics preprints
python scripts/search_dblp.py     --query "<cluster>" --limit 50 --state research_state.json  # CS gold-standard bibliography (no abstracts)
python scripts/search_pubmed.py   --query "<cluster>" --limit 50 --state research_state.json  # biomedical (PubMed)
python scripts/search_biorxiv.py  --query "<cluster>" --limit 50 --state research_state.json  # life-sci preprints (bioRxiv + medRxiv via Europe PMC)
python scripts/search_crossref.py --query "<cluster>" --limit 50 --state research_state.json  # DOI-backed metadata

# Open-web coverage (optional, requires EXA_API_KEY) — finds material the
# scholarly APIs miss: lab sites, institutional PDFs, conference mirrors,
# preprints parked outside arXiv, NGO/government reports.
python scripts/search_exa.py --query "<cluster>" --limit 50 --state research_state.json

# Dedupe across sources (DOI-first, title-similarity fallback)
python scripts/dedupe_papers.py --state research_state.json

MCP enrichment (optional, run if available): call mcp__asta__search_papers_by_relevance and mcp__asta__snippet_search and feed results via scripts/research_state.py ingest. If the MCP call errors or times out, do not retry — move on.

Iterate. Read the state file. Are there keyword gaps? Are there authors appearing 3+ times whose other work you haven't pulled? Run another round. Stop when saturation hits — every source, not just the last one queried:

python scripts/research_state.py saturation --state research_state.json
# Returns { "per_source": {...}, "overall_saturated": true/false, ... }

overall_saturated is true only when every queried source has run at least --min-rounds (default 2) rounds AND each is individually below the new-paper percentage and new-citation thresholds. A source that has been queried only once cannot be declared saturated, which rules out the failure mode where a single quiet source falsely ends discovery. Use --source openalex to check one source in isolation.

Budget caps and broad-topic escape hatches. Phase 1 has two hard caps to prevent runaway agents: SCHOLAR_PHASE1_MAX_ROUNDS (default 10 rounds per source) and SCHOLAR_PHASE1_MAX_REQUESTS_PER_SOURCE (default 20 ingests per source). Hitting either returns phase1_budget_exhausted with a next: hint. For genuinely broad topics that cross subfields (e.g. CS-ML topics with multiple keyword clusters), the saturation thresholds can also fail to converge under the defaults — relax them with SCHOLAR_SATURATION_NEW_PCT (default 20.0), SCHOLAR_SATURATION_MAX_CITATIONS (default 100), and SCHOLAR_SATURATION_NEW_AUTHORS_PCT / SCHOLAR_SATURATION_NEW_VENUES_PCT. These env vars are honored both by python scripts/research_state.py saturation and by the G2 gate, so raising them lets the gate accept "good enough" coverage on topics where the default is unreachable.

Phase 2 — Triage

Rank the deduplicated corpus and pick the top-N for deep reading.

python scripts/rank_papers.py \
  --state research_state.json \
  --question "<phase 0 question>" \
  --alpha 0.4 --beta 0.3 --gamma 0.2 --delta 0.1 \
  --top 20

The formula is transparent — the script prints it and writes the components to state so the report can cite its own methodology:

score = α·relevance + β·log10(citations+1)/3 + γ·recency_decay(half-life=5yr) + δ·venue_prior

Defaults target a literature review. For a scoping review prefer higher α (relevance) and lower β (citations). For a systematic review of a narrow question, lower α and higher β.

Write the top-N selection to state:

python scripts/research_state.py select --state research_state.json --top 20

Triage the selection into deep / skim / defer tiers before advancing. Phase 3 fan-out is the most expensive stage of the workflow; not every selected paper deserves a full agent dispatch:

python scripts/skim_papers.py --state research_state.json \
  --deep-ratio 0.5 --skim-ratio 0.5

Defaults split the top-N evenly: top half → deep (agent dispatch in Phase 3), bottom half → skim (abstract-derived evidence stub auto-filled, depth=shallow). For tighter budgets, use --deep-ratio 0.3 --skim-ratio 0.5 — the remaining 20% gets tier=defer and is removed from selected_ids (still queryable as candidates for citation chase).

The script emits data.deep_tier_preview listing the deep-tier papers by triage_score. Show this to the user before advancing so they can hand-override before agents fan out (re-run with different ratios, or manually re-rank in state). Triage is required before G3 passes — the gate's triage_applied check rejects the advance otherwise.

Optional but recommended — prefetch deep-tier PDFs before agent fan-out:

python scripts/prefetch_pdfs.py --state research_state.json \
  --tier deep --concurrency 4

Fetches every deep-tier paper's PDF into ${SCHOLAR_CACHE_DIR:-.scholar_cache}/pdfs/<id-hash>/ via paper-fetch (with Unpaywall fallback), in parallel waves, and writes pdf_path / pdf_status / pdf_source / pdf_bytes per paper. Phase 3 agents then read the local file directly instead of each running its own download — Agent context stays focused on reading + reasoning, not on retrying paywalls.

Failures land as pdf_status='failed' with a pdf_failure_code (paper_fetch_error, no_open_access_pdf, pdf_download_failed, …); papers without a DOI get pdf_status='no_doi'. Phase 3 agents check pdf_path first and only fall back to extract_pdf.py --doi if the prefetched path is missing. Re-running prefetch is cheap: papers with an existing pdf_path on disk are skipped (pdf_status='cached').

Human-in-loop for paywalled PDFs. When automatic fetch fails (paywall, OA chain exhausted, no DOI), surface a hand-fetch list to the user via --emit-manifest (read-only):

python scripts/prefetch_pdfs.py --state research_state.json --emit-manifest
# Returns { needs_user_download: [{id, doi, title, drop_at, alt_urls}, ...] }

The user downloads each PDF (institutional VPN, ResearchGate, etc.) and drops it at the listed drop_at path (any *.pdf filename in that subdir works). On the next normal prefetch_pdfs.py run, dropped files are auto-absorbed as pdf_source='user_provided' without re-fetching.

Skip prefetch entirely when paper-fetch is not installed AND you don't want Unpaywall traffic — Phase 3 agents will then download per-paper inside their own contexts (slower, noisier, but functionally identical).

Phase 3 — Deep read (parallel agent fan-out)

Phase 3 splits by tier:

• tier=skim — apply_triage() already wrote an abstract-derived evidence stub with depth=shallow. No further action needed.
• tier=deep — dispatch one agent per paper, in parallel waves of 8–10. Each agent reads the PDF, writes structured evidence back to state, and returns one JSON line. The host's main context never sees the full PDF text.

The agent prompt template lives at references/agent_prompts/phase3_deep_read.md. Load it once, instantiate per paper, and dispatch all N tool_use calls in a single message so they fan out concurrently. Per-agent contract:

• Input: paper_id, doi, pdf_url, abstract, question, state_path
• Action: extract_pdf.py --doi <doi> --output <tmp> → read text → write evidence --depth full
• Output: one line {"paper_id": "...", "status": "ok"|"evidence_unavailable", ...}

The state CLI is exclusive-locked, so N agents writing concurrent evidence calls are serialized automatically — no coordination needed.

# After all wave(s) complete, verify deep-tier coverage:
python scripts/research_state.py advance --state research_state.json \
  --to 4 --check-only

If deep_tier_full_evidence is failing, dispatch a follow-up wave for the missing ids only. If a paper's full text is genuinely unreachable (paywall, exhausted OA chain), the agent should write a depth=shallow record with method starting evidence_unavailable: per the prompt's failure-mode section — that record satisfies depth_marks_valid without inflating the deep-tier coverage count.

Manual fallback (no agents available). Hosts that cannot dispatch parallel agents (some non-CC platforms) can run Phase 3 sequentially in the main session: for each tier=deep paper, extract_pdf.py --doi <doi> then research_state.py evidence --id <pid> --depth full .... Slower and burns more context, but the gate logic is identical.

Phase 4 — Citation chasing

Take the top 5-10 highest-ranked papers and expand the graph.

# Preview the request count first — this is the most expensive command
python scripts/build_citation_graph.py \
  --state research_state.json \
  --seed-top 8 --direction both --depth 1 --dry-run

# Run with an idempotency key so a retry after a network blip is free
python scripts/build_citation_graph.py \
  --state research_state.json \
  --seed-top 8 --direction both --depth 1 \
  --idempotency-key "chase-$(date -u +%Y%m%dT%H%M)"

The script pulls backward references (what did this paper cite?) and forward citations (who cited this paper?), deduplicates against existing state, and writes new candidate papers with discovered_via: citation_chase. Run rank + deep read again on any new high-scoring additions.

Dual backend. --source openalex|s2|both (default both). OpenAlex covers most fields well; Semantic Scholar (S2) has better CS / arXiv / cross-disciplinary coverage. The two graphs disagree more than you'd expect — running both then deduping by id surfaces real coverage gaps. S2 needs a DOI / arXiv id / PMID on each seed (it doesn't accept OpenAlex ids); seeds without one skip the S2 backend. S2_API_KEY env var raises the S2 quota; without it the public quota of ~1 req/s applies.

Idempotency. When --idempotency-key <k> is set, the first successful run writes {response, signature} to .scholar_cache/<hash>.json. A retried run with the same key replays the cached response without re-hitting OpenAlex or re-mutating state. Reusing the same key with different arguments returns idempotency_key_mismatch rather than silently serving stale data. Cache directory: SCHOLAR_CACHE_DIR env var, default .scholar_cache/.

Special case — a highly cited paper has never been challenged. If rank says a paper is top-3 by citations but no critiques appear in the corpus, search explicitly for "<first author> <year>" critique OR limitations OR reanalysis OR failed replication. This is the confirmation-bias backstop.

Phase 5 — Synthesis

No scripts here — this is where the agent earns its keep. Cluster and structure:

1. Thematic clustering. Group the top-N into 3-6 themes that map onto the report outline. Themes should be orthogonal: a paper can be primary to one, secondary to at most one other.
2. Tension map. Where do papers disagree? For each disagreement, note: which papers, on what, and whether the disagreement is empirical (different data), methodological (different tools), or theoretical (different framings).
3. Timeline. When relevant, a chronological arc: seminal paper → consolidation → refinement → current frontier.
4. Venn / gap. What has been studied well, partially, and not at all? The gap is the pivot for Phase 7.

Phase 6 — Self-critique

This is not optional. Load assets/prompts/self_critique.md and run the full checklist against your draft (still unpublished). The checklist covers:

• Single-source claims (any claim backed by only one paper?)
• Citation/recency skew (is the latest-2-years window covered?)
• Venue bias (is the corpus dominated by one journal/venue?)
• Author bias (does one lab dominate the citations?)
• Untested high-citation papers (anyone cite a paper without reading a critique?)
• Contradictions buried (any tension in Phase 5 that got glossed over?)
• Archetype fit (does the structure match the chosen archetype?)
• Unanchored claims (any statement without a [^id] anchor?)

Write findings to research_state.json under self_critique and fix blockers before Phase 7. Findings go into the report appendix verbatim — the reader deserves to see what the research process doubted itself about.

Phase 7 — Report

Render an archetype scaffold from state, then fill the agent-prose slots and validate anchors:

# Generate the scaffold — fills header, themes, tensions, methodology
# appendix, self-critique appendix, and bibliography anchor index from
# state. Leaves `<!-- AGENT: ... -->` placeholders for prose.
python scripts/render_report.py --state research_state.json
# → reports/<slug>_<YYYYMMDD>.md by default; pass --output PATH to override.

# After filling in the prose, lint every [^id] anchor against
# state.papers. Catches typo'd anchors before the report ships.
python scripts/render_report.py --state research_state.json \
  --lint reports/<slug>_<YYYYMMDD>.md

# Export bibliography in the user's preferred format
python scripts/export_bibtex.py --state research_state.json --format bibtex --output refs.bib
python scripts/export_bibtex.py --state research_state.json --format csl-json --output refs.json

The scaffold's body uses [^id] anchors (the paper id from state). The bibliography section at the bottom carries one definition per selected paper. The lint mode flags unknown_anchors_used (typos) and undefined_in_text (anchors with no footnote definition); both are blockers. unused_definitions is a soft signal — selected papers that ended up not cited inline.

Save path convention: reports/<slug>_<YYYYMMDD>.md. The skill does not write outside the working directory unless the user specifies a path.

HTML / PDF delivery. The pipeline ships markdown by design — it's the contract render_report.py --lint validates and what downstream agents can re-process. For a polished human-facing artifact, hand reports/<slug>_<YYYYMMDD>.md + .bib to the host coding agent with a prompt like "render this as a polished HTML page suitable for sharing with my PI: serif body, ~70ch column, sidebar TOC, Phase 6 critique as a <details> block, citation hovers showing the bib entry". Visual styling sits outside the skill's correctness contract.

Report archetype selection

Archetype	When to use	Primary output shape
literature_review	User wants to know what's established about a topic	Thematic sections + synthesis + gap
systematic_review	Narrow question, many studies, need rigorous comparison	PRISMA-lite flow + extraction table + pooled findings
scoping_review	Broad topic, "what has been studied?"	Coverage map + methods inventory + research gap
comparative_analysis	"A vs B" — methods, models, approaches	Axes of comparison + per-axis verdict + recommendation
grant_background	Narrative for a proposal introduction	Problem significance + what's known + what's missing + why our approach

Templates live in assets/templates/<archetype>.md. Load only the one you need.

Scripts reference