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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.

Ask your Agent

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

This session only. Nothing lands on disk.

referencesexample_run.md

≈768 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Example run

A complete walk-through showing what the 8-phase workflow looks like end-to-end on a single concrete question. Useful as a sanity check that your own run is hitting the expected checkpoints.

User: Give me a deep research report on CRISPR base editing for Duchenne muscular dystrophy.

Agent:
[Phase 0] Restating: "What is the current state and evidence for CRISPR base editing
          as a therapeutic approach for Duchenne muscular dystrophy (DMD)?"
          Archetype: literature_review (default; confirmed — broad overview requested)
          Clusters:
            - ("base editing" OR "adenine base editor" OR "cytosine base editor" OR ABE OR CBE)
            - ("Duchenne muscular dystrophy" OR DMD OR dystrophin)
            - (in vivo OR AAV OR "muscle delivery")
          → research_state.json initialized

[Phase 1] Running OpenAlex + PubMed + arXiv + Crossref across 3 clusters...
          Round 1: 187 hits, 142 unique. Round 2: 94 hits, 31 new.
          Saturation check: new=11%, max_new_citations=23 → SATURATED
          143 unique papers in state.

[Phase 2] Ranking with default weights (literature review)...
          Top 20 selected. Score components written to state.
          Triage: 10 deep / 10 skim. Prefetch fills 9/10 deep PDFs (1 paywalled,
          surfaced to user via --emit-manifest manifest).

[Phase 3] Fetching full text... 17/20 full, 3 abstract-only (flagged shallow).
          Evidence extraction complete.

[Phase 4] Citation chasing on top 8 seeds, depth 1.
          OpenAlex + S2 backends both run. Added 24 candidates after dedupe,
          6 re-scored into top 20.

[Phase 5] Themes: (a) delivery platforms, (b) editing efficiency, (c) off-target/safety,
          (d) pre-clinical outcomes, (e) clinical translation barriers.
          Tensions: AAV serotype optimality (Theme a) — 3 papers disagree.

[Phase 6] Self-critique flagged 2 single-source claims and a recency gap
          (no 2025 paper in top 20). Re-ran focused search; added 4 papers.

[Phase 7] Rendering literature_review template...
          Report: reports/crispr-base-editing-dmd_20260411.md
          Bibliography: reports/crispr-base-editing-dmd_20260411.bib (84 refs)

Things to notice

  • Phase 1 took two rounds, not one. Saturation isn't a single search — 11% new on round 2 is what passed the threshold.
  • Phase 2 split into deep/skim before fan-out, with a paywall manifest surfaced to the user. The agent did not waste an agent dispatch on the paywalled paper.
  • Phase 4 ran both OpenAlex and S2 by default, then deduped — coverage gaps between the two are real, especially for CS-adjacent biomed.
  • Phase 6 found a recency gap and looped back to search before declaring done. Self-critique is not a checkbox; it's allowed to push the workflow backwards.
  • Final bibliography size (84) > top-N (20): every paper anchored in the report's appendices/methodology — including ones from the citation chase that weren't in the top-20 deep-read pool — gets a bibliography entry.

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.

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  • 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 14 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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