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/scholar-deep-research

@72ee04b
by Agents365.aiagents365-ai/365-skills57 stars
12

Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.

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Use this Skill: https://skilld.dev/gh/agents365-ai/365-skills/scholar-deep-research

This session only. Nothing lands on disk.

assetstemplatescomparative_analysis.md

≈536 tokens on demand. Your agent reads this file only when SKILL.md points to it.

{{X}} vs {{Y}}: A Comparative Analysis

Question: {{question}} Date: {{date}} Sources consulted: {{sources}}

Executive summary

Verdict: {{one-sentence verdict}}

Confidence: high / medium / low — {{why}}

When the verdict flips: {{edge cases}}

1. What is being compared

1.1 {{X}}

Brief definition, intended use, scope. Who proposes/uses it.

1.2 {{Y}}

Same shape.

1.3 What is not being compared

Explicit exclusions to keep the scope honest.

2. Axes of comparison

Each axis: criterion, evidence from corpus, per-axis verdict.

Axis 1: {{e.g., performance / accuracy}}

Property {{X}} {{Y}}
Headline metric {{value}} [^id] {{value}} [^id]
Best-case ... ...
Worst-case ... ...
Variance across studies low/med/high [^id1][^id2] ...

Per-axis verdict: {{X or Y}} wins on this axis when {{condition}}, but {{caveat}}.

Axis 2: {{e.g., compute cost / sample efficiency}}

Same shape.

Axis 3: {{e.g., robustness / generalization}}

Same shape.

Axis 4: {{e.g., interpretability}}

Same shape.

Axis 5: {{e.g., maturity / community adoption}}

Same shape.

(Add or remove axes to fit the question. Aim for 3-6.)

3. Where they agree

Often overlooked: where do {{X}} and {{Y}} not differ meaningfully? Acknowledging this prevents the report from inventing conflicts.

4. Where they disagree (and why)

For each meaningful disagreement, name:

  • The empirical observation
  • The methodological reason (different datasets, baselines, hyperparameters)
  • The theoretical reason (different framings)

5. Overall recommendation

If the user must pick one today, with no further information:

  • Pick {{X}} when {{conditions}}
  • Pick {{Y}} when {{conditions}}
  • Pick neither (or wait) when {{conditions}}

6. Open questions

What would change the verdict? What study would be most informative?

Appendix A — Methodology

{{search + ranking + dedupe stats}}

Appendix B — Self-critique

{{self_critique.appendix}}

Bibliography

{{rendered from export_bibtex.py}}

Source: SKILL.md on GitHub

Third-party checks

1 warning4mo3 checks · Risk SAFE
  • Gen Agent Trust Hub4mo

    This skill is a robust academic research tool that automates literature reviews across multiple scholarly databases. It uses strict input validation for paper identifiers and handles sensitive API keys through environment variables. While the skill executes a local script from a companion 'paper-fetch' skill to download PDFs, this is a standard design pattern within the author's ecosystem. It also processes external PDF text, which carries a minor risk of indirect prompt injection common to all document-reading agents.

  • Socket4mo

    No alerts

  • Snyk4mo

    Risk: MEDIUM · 1 issue

Provenance

Signed by skilld at 72ee04b. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 11 hours ago.

Activeupdated 3 weeks ago

Capability

homepage
https://github.com/Agents365-ai/365-skills
platforms
[macos, linux, windows]
Other metadata
compatibility
Requires Python 3.9+ with httpx and pypdf (see requirements.txt). Optional: `pip install docling` to enable layout-aware markdown PDF extraction (`extract_pdf.py --engine docling`); auto-used as a fallback for scanned/sparse PDFs. Works offline-first (no MCP required) but enriches with Semantic Scholar / Brave MCP tools when available.
metadata
{
  "openclaw": {
    "requires": {
      "bins": [
        "python3"
      ]
    },
    "emoji": "🔬"
  },
  "hermes": {
    "tags": [
      "research",
      "literature-review",
      "academic",
      "papers",
      "citations",
      "survey"
    ],
    "category": "research"
  },
  "pimo": {
    "tags": [
      "research",
      "literature-review",
      "academic"
    ],
    "category": "research"
  },
  "author": "Agents365-ai",
  "version": "0.17.0"
}

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