{{title}}: A Systematic Review

Question (PICO):

Population: {{P}}
Intervention: {{I}}
Comparator: {{C}}
Outcome: {{O}}

Date: {{date}} Protocol: This review followed PRISMA-lite guidance. Search and screening were not pre-registered.

1. Background and rationale

Why does this question matter? What is the prior state of evidence?

2. Methods

2.1 Search strategy

Source	Query	Date searched	Hits	Included
OpenAlex	`{{cluster A}}`	{{date}}	{{n}}	{{n}}
PubMed	`{{cluster A}}`	{{date}}	{{n}}	{{n}}
arXiv	`{{cluster B}}`	{{date}}	{{n}}	{{n}}
Crossref	`{{cluster C}}`	{{date}}	{{n}}	{{n}}
Citation chase	seeds={{n}}, depth=1	{{date}}	{{n}}	{{n}}

2.2 Inclusion criteria

{{criterion 1}}
{{criterion 2}}
{{criterion 3}}

2.3 Exclusion criteria

{{criterion 1}}
{{criterion 2}}

2.4 Risk-of-bias assessment

For each included study we noted: sample size, pre-registration status, blinding, conflicts of interest, retraction status, and replication status. See extraction table.

3. PRISMA-lite flow

Records identified: {{total_hits}}
        │
        ▼
After dedupe: {{after_dedupe}}
        │
        ▼
Screened (title/abstract): {{after_screen}}
        │
        ▼
Full-text assessed: {{full_text}}
        │
        ├── Excluded ({{n}}):
        │     - {{reason 1}}: n={{n}}
        │     - {{reason 2}}: n={{n}}
        ▼
Included in synthesis: {{included}}

4. Extraction table

Study	Year	n	Population	Intervention	Comparator	Outcome	Effect	Risk of bias
[^id1]	{{y}}	{{n}}	...	...	...	...	...	low/med/high
[^id2]	...	...	...	...	...	...	...	...

5. Synthesis

5.1 Primary outcome

Narrative synthesis. If outcomes are numerical and homogeneous, a meta-analytic note can go here (the skill does not run meta-analyses — flag this for the user).

5.2 Secondary outcomes

5.3 Subgroup observations

6. Quality of evidence

Use a GRADE-style summary if applicable, otherwise narrative:

High: {{summary}}
Moderate: {{summary}}
Low / very low: {{summary}}

7. Conclusions

What does the body of evidence support, with what confidence?

8. Limitations of this review

{{limitation 1}}
{{limitation 2}}
{{from self-critique appendix}}

Appendix A — Methodology details

(See SKILL.md Phase 0-7 description; same content as literature_review template.)

Appendix B — Self-critique

Bibliography

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
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Snyk4mo
Risk: MEDIUM · 1 issue

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" }

/scholar-deep-research

assetstemplatessystematic_review.md