Parsifal Slr GuideSAFE
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Overview
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
e1ba289846fdOBSERVED · 2026-10-08Install
Commands as the repository documents them. They are shown, not run.
git clone https://github.com/vitorfs/parsifal.git
pip install -r requirements.txt
Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| openclaw | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: parsifal-slr-guide
description: "Plan and manage systematic literature reviews with Parsifal platform"
metadata:
openclaw:
emoji: "📋"
category: "research"
subcategory: "methodology"
keywords: ["Parsifal", "systematic review", "SLR", "review protocol", "PICO", "research methodology"]
source: "https://github.com/vitorfs/parsifal"
---
# Parsifal Systematic Literature Review Guide
## Overview
Parsifal is a web-based tool for planning and managing systematic literature reviews (SLRs) following established protocols (Kitchenham, PRISMA). It guides researchers through the complete SLR process: defining research questions, setting inclusion/exclusion criteria, planning search strings, and tracking the screening process. Open-source and self-hostable.
## SLR Process with Parsifal
### Phase 1: Planning
#### Define Research Questions
Structure questions using PICO framework:
- **P**opulation: What group/domain?
- **I**ntervention: What technique/method?
- **C**omparison: Compared to what?
- **O**utcome: What results measured?
```
Example:
P: Software development teams
I: AI-assisted code review
C: Manual code review
O: Defect detection rate, review time
Research Questions:
RQ1: Does AI-assisted code review improve defect detection?
RQ2: What is the time savings compared to manual review?
RQ3: What types of defects are best detected by AI tools?
```
#### Set Criteria
```
Inclusion Criteria:
IC1: Studies comparing AI vs manual code review
IC2: Published in peer-reviewed venues (2020-2026)
IC3: Reports quantitative metrics
Exclusion Criteria:
EC1: Grey literature / blog posts
EC2: Studies with fewer than 10 participants
EC3: Non-English publications
```
### Phase 2: Search Strategy
#### Build Search String
```
("artificial intelligence" OR "machine learning" OR "deep learning")
AND
("code review" OR "code inspection" OR "static analysis")
AND
("defect detection" OR "bug finding" OR "software quality")
```
#### Database Mapping
| Database | Adapted Query | Expected Results |
|----------|--------------|-----------------|
| Scopus | TITLE-ABS-KEY(...) | ~500 |
| IEEE Xplore | querytext=... | ~300 |
| ACM DL | [[Abstract: ...]] | ~200 |
| Web of Science | TS=(...) | ~400 |
### Phase 3: Selection
#### Screening Steps
1. **Remove duplicates** — Match by DOI, title similarity
2. **Title screening** — Quick relevance assessment
3. **Abstract screening** — Apply inclusion/exclusion criteria
4. **Full-text review** — Detailed evaluation
#### Quality Assessment
Define quality criteria and scoring:
| Criterion | Score |
|-----------|-------|
| Clear research question stated | 0/0.5/1 |
| Methodology described in detail | 0/0.5/1 |
| Threats to validity discussed | 0/0.5/1 |
| Results statistically analyzed | 0/0.5/1 |
| Study replicable from description | 0/0.5/1 |
### Phase 4: Extraction
#### Data Extraction Form
```
For each included paper, extract:
- Study ID
- Authors, Year, Venue
- Study type (experiment/case study/survey)
- Population size
- AI technique used
- Metrics reported (precision, recall, F1, time)
- Key findings
- Limitations noted
```
### Phase 5: Synthesis
#### Report with PRISMA
```
Identification: 1,400 records
↓ Remove duplicates: -350
Screening: 1,050 titles/abstracts
↓ Exclude irrelevant: -900
Eligibility: 150 full-text assessed
↓ Exclude by criteria: -108
Included: 42 studies in final review
```
## Self-Hosting Parsifal
```bash
git clone https://github.com/vitorfs/parsifal.git
cd parsifal
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver
# Access at http://localhost:8000
```
## SLR Best Practices
1. **Register protocol** before starting (PROSPERO for health, OSF for others)
2. **Two independent reviewers** for screening to reduce bias
3. **Track inter-rater agreement** (Cohen's kappa > 0.8)
4. **Document deviations** from the original protocol
5. **Use PRISMA checklist** for reporting completeness
## References
- [Parsifal](https://github.com/vitorfs/parsifal)
- Kitchenham, B. & Charters, S. (2007). "Guidelines for performing Systematic Literature Reviews in Software Engineering."
- [PRISMA Statement](http://www.prisma-statement.org/)
- [PROSPERO Registry](https://www.crd.york.ac.uk/prospero/)Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
e1ba289846fdfull audit observations/trust-audit/skill/brycewang-stanford__parsifal-slr-guide.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-08 | e1ba289846fd | SAFE | B | 89 | first audit |
Questions
What does the Parsifal Slr Guide skill do?
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Is Parsifal Slr Guide safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Parsifal Slr Guide access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Parsifal Slr Guide work with?
Its documentation mentions openclaw. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (e1ba289846fd), read on 2026-10-08. The repository is watched, and a new audit runs when it changes — this is the first audit.