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Trace Citations

skill-zongmin-yu-semantic-scholar-skills-trace-citations · by zongmin-yu

Trace the citation neighborhood around one focal paper into foundations, descendants, bridges, weak edges, and optional second-hop links

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Install

$ agentstack add skill-zongmin-yu-semantic-scholar-skills-trace-citations

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Trace Citations

Map the citation graph around one focal paper into useful buckets. Use this when the human wants lineage, influence, and strong versus weak citation edges around a paper.

Arguments

  • The positional argument is the focal paper query. Quote multi-word titles.
  • --depth 1|2 controls whether to expand a second hop from the strongest first-hop edges.
  • --max-references and --max-citations cap the first-hop fetch sizes.
  • --second-hop-limit caps how many first-hop anchors get expanded at depth two.

Workflow

  1. Run python scripts/run.py ....
  2. Read result.foundations for strong references behind the focal paper.
  3. Read result.direct_descendants for strong citing descendants.
  4. Read result.bridge_nodes for medium-confidence connectors with rich context or intent signal.
  5. Read result.weak_edges for low-signal edges that are probably less useful.
  6. If depth=2, read result.second_hop only after the first-hop picture looks sensible.

Output

  • The script prints the unified JSON envelope described in output_contract.md.
  • The underlying workflow result is CitationTraceResult.to_dict().
  • result.reference_count_examined and result.citation_count_examined show the first-hop search breadth.

When To Escalate

  • The focal paper resolves incorrectly.
  • The API returns very sparse context and intent data, making edge interpretation weak.
  • The first-hop graph is too noisy and needs a tighter focal paper choice before going to depth two.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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