semantic-scholar-deep

name: semantic-scholar-deep description: Deep research over the Semantic Scholar Graph API. Covers endpoints missing from allenai's lookup skill — paper references (backward citations), recommendations, batch paper lookup (up to 500 IDs), snippet search, and multi-hop citation graph traversal (BFS forward/backward). Use when the user asks to build a citation graph, expand a literature seed, find related work, run a reference network traversal, explore what a paper cites or what cites it beyond simple lookup, or batch-resolve many DOI/arXiv/S2 IDs. For multi-step research questions, delegate to the deep-paper-researcher subagent to keep the main context clean. Not for single paper-by-ID lookups (use semantic-scholar-lookup) or topical discovery (use web_search_advanced_exa). allowed-tools: Bash(python3:*), Read, Write, Edit, Glob, Grep, Agent

Semantic Scholar — Deep Research

Purpose: fill the gaps that semantic-scholar-lookup (allenai) leaves — references, recommendations, batch, and multi-hop citation-graph traversal.

Contents

• Dispatch Rule — inline vs delegate; model selection
• When to Use — trigger scenarios
• Scripts — ss_client.py + citation_graph.py
• Authentication & Rate Limits
• Progressive Disclosure — deeper references
• Output Hygiene
• Integration — typical pipeline with the subagent

Dispatch Rule (read first)

Two execution modes:

Inline (run the Bash scripts yourself)

Use when the user asks for one specific endpoint:

• "get references of paper X" → ss_client.py references <id>
• "recommendations for paper Y" → ss_client.py recommendations <id>
• "batch-resolve these 30 DOIs" → ss_client.py batch ...
• "find the snippet where X is said" → ss_client.py snippets "..."

Fast, cheap, no orchestration overhead.

Delegate to deep-paper-researcher subagent

Use when the task is multi-step or would otherwise flood the context:

• Literature review on a topic
• Citation graph / network analysis around a seed paper
• Novelty check for an idea
• State-of-the-art survey
• Anything that requires merging Exa discovery + S2 graph + ranking

Mandatory prompt contents. The subagent runs in isolated context with no access to this conversation's system reminders. Include exactly these two things:

1. Today's date — inline as Today is YYYY-MM-DD. Pull from the currentDate system-reminder field, or run date -I via Bash before delegating if it's missing. Never rely on training-data intuitions about the current year.
2. User's request, verbatim — pass the user's original phrasing (topic + any freshness words like "современные / recent / классические / seminal" and any explicit dates like "since 2024"). Translate language if needed but do not paraphrase trigger words into date windows.

Do NOT do any of these:

• Do NOT classify freshness yourself (RECENT/FOUNDATIONAL/MIXED). The subagent does that from the verbatim user request.
• Do NOT invent a date window. If the user said "современные / recent / latest" without a year, the subagent defaults to last 6 months — don't preempt it with "2024-2026".
• Do NOT drop the trigger words. The subagent relies on them to pick the right mode.

Call:

Agent(
  subagent_type="deep-paper-researcher",
  description="<3–5 word task>",
  prompt="Today is 2026-04-22.\n\nUser's request: найди современные 10 статей про AI Code Review на arXiv.\n\n<optional: output format hints, language preference>"
  # model: "opus"  ← add only when the user opts in (see below)
)

The subagent's Freshness Mode section handles classification; keep this layer thin.

Model selection (Sonnet default, Opus on demand)

The subagent's model frontmatter is sonnet — that's the default.

Override to Opus by passing model: "opus" to the Agent tool only if the user explicitly requests deeper reasoning. Triggers (any of):

• English: "deep dive", "thorough", "rigorous", "use Opus", "high quality", "comprehensive", "exhaustive"
• Russian: "глубокий/глубже", "тщательный/тщательно", "подробно", "в режиме Опус/Opus", "максимально качественно", "серьёзный ресерч"

Never auto-upgrade to Opus without a user signal — Sonnet handles the default literature-review workflow fine and costs less.

When to Use

Trigger this skill for:

• Citation graph / network over a seed paper or topic
• Backward references (what does this paper cite?) — not covered by allenai
• Forward citations with pagination beyond 1000 results
• Recommendations — related-paper discovery from a seed
• Batch lookup — resolve 50-500 DOI/arXiv/CorpusId/S2 IDs in one call
• Snippet search — find specific passages across the S2 corpus

Do NOT use for:

• Simple "get paper by ID" or "who cited this" — use semantic-scholar-lookup (faster, no Python)
• Broad topical discovery — use web_search_advanced_exa with category: "research paper" (Exa MCP)
• Consumer-level literature questions — use the deep-paper-researcher subagent, which orchestrates all three tools

Scripts

Located under ${SKILL_DIR}/scripts/.

ss_client.py — raw API client

Subcommands (all output JSON on stdout):

Common flags: --limit, --offset, --fields, --year, --fields-of-study, --venue, --min-citation-count.

citation_graph.py — BFS traversal

python3 ${SKILL_DIR}/scripts/citation_graph.py <paperId> \
    --direction both \
    --depth 2 \
    --max-nodes 200 \
    --per-hop-limit 50 \
    --output graph.json

Directions: forward (citations), backward (references), both. Output schema described in the script docstring — nodes: {paperId → metadata+depth}, edges: [{src, dst, direction}].

Authentication & Rate Limits

• Without API key: ~1 RPS shared, 100 queries/5min bursts. Fine for small graphs.
• With SEMANTIC_SCHOLAR_API_KEY env var: much higher limits.
• Apply: https://www.semanticscholar.org/product/api#api-key
• The client does exponential backoff (1→30s) on HTTP 429/5xx, respects Retry-After.

Progressive Disclosure

• references/endpoints.md — complete field list per endpoint + query examples
• references/workflows.md — lit-review, novelty-check, seed-expansion patterns

Output Hygiene

Scripts emit raw JSON — redirect to files for anything beyond ~20 results. For graphs >50 nodes always pass --output graph.json to avoid flooding the conversation context.

Integration

Typical pipeline inside the deep-paper-researcher subagent:

1. Discovery — mcp__exa__web_search_advanced_exa (neural + multi-source)
2. ID resolution — ss_client.py search / batch to get paperId from titles or DOIs
3. Graph expansion — citation_graph.py with the top 3-5 seeds
4. Synthesis — distill nodes/edges into a ranked report

Optional: Bundled Subagent

A paired subagent definition ships alongside the skill at agents/deep-paper-researcher.md. It orchestrates Exa MCP + allenai semantic-scholar-lookup + this skill's scripts into a token-isolated research agent with:

• Mandatory input validation (today's date anchoring + caller-paraphrased-window detection)
• Freshness Mode classifier (RECENT / FOUNDATIONAL / MIXED)
• Sort-then-tiebreak ranking (never multiplies citations × recency into a single score)
• Compact report format with explicit Anchor date / Mode / Window header

To install for Claude Code (manual, one-time):

cp ~/.agents/skills/semantic-scholar-deep/agents/deep-paper-researcher.md ~/.claude/agents/

(Path may differ on other agents — copy to the agent's subagents directory, then restart the session.)

Prerequisites for full pipeline: Exa MCP connected, allenai/asta-plugins@"Semantic Scholar Lookup" skill installed.