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/scholar-deep-research

@72ee04b

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.

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

This session only. Nothing lands on disk.

assetstemplatesscoping_review.md

≈539 tokens on demand. Your agent reads this file only when SKILL.md points to it.

{{title}}: A Scoping Review

Question: What has been studied (and how) in {{topic}}? Date: {{date}} Sources consulted: {{sources}} Papers in corpus: {{total_papers}}


1. Background

Why scope this field? Who is the audience? What does the user need to plan next?

2. Scope question

Refined scope statement (broader than a PICO; narrow enough to be tractable).

3. Methods

Brief — scoping reviews use broad inclusion. Note minimum exclusion criteria only.

Source Cluster Hits Included
OpenAlex {{...}} {{n}} {{n}}

4. Coverage map

A matrix view of the field. Rows = subtopics, columns = methods (or populations, or settings). Cell = paper count.

Subtopic ↓ / Method → Method A Method B Method C Method D
Subtopic 1 n=12 n=4 — n=1
Subtopic 2 n=2 n=18 n=6 —
Subtopic 3 — — n=3 n=14

The empty cells are the gap.

5. Methods inventory

What methods has the field used? Brief description of each, with a representative paper.

  • Method A: {{description}}. Representative: [^id]
  • Method B: {{description}}. Representative: [^id]

6. Population / setting inventory

Same shape — what populations, models, or settings have been studied?

7. Subtopic narratives

One short paragraph per subtopic, with anchor pointers to the most representative work — not a full review of each.

7.1 {{Subtopic 1}}

{{paragraph with anchors}}

7.2 {{Subtopic 2}}

{{paragraph with anchors}}

8. Research gap

Synthesize the empty cells into a research gap statement. This is the deliverable.

  • Gap 1: {{description}}
  • Gap 2: {{description}}
  • Gap 3: {{description}}

9. Recommendations for future work

  • Most tractable next study: {{...}}
  • Highest-impact next study: {{...}}
  • Methodological recommendation: {{...}}

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

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

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  • Snyk4mo

    Risk: MEDIUM · 1 issue

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
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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