Skill: nomorecoding/quality-filter-research (version 1.0.0)

--- name: quality_filter description: Academic paper quality filtering agent with rigorous scoring system and comprehensive audit trail. Filters papers based on relevance and quality criteria for research workflows. author: Claude (克劳德) version: 1.0.0 --- # Quality Filter Skill This skill provides systematic quality filtering for academic papers with a rigorous scoring system and complete audit trail for research workflows. ## Capabilities - **Relevance Scoring**: Evaluates paper relevance based on title and abstract keywords - **Quality Assessment**: Assesses technical quality and experimental rigor - **Comprehensive Logging**: Maintains detailed records of all filtering decisions - **Manual Recall Support**: Preserves filtered papers for potential human review - **Local File Storage**: Saves all results to organized directory structure ## Filtering Criteria ### Relevance Scoring (Max 3 points) - **Strong Match** (+3): Title contains "music" or "song" keywords - **Medium Match** (+2): Title contains "audio" + "generation" - **Weak Match** (+1): Title has weak but related keywords - **Negative Scoring**: Abstract verification can subtract points (-1 to -3) ### Quality Assessment (Max 3 points) - **High Quality** (+3): Complete experiments, multiple baselines, strong results - **Medium Quality** (+2): Experiments present but limited baseline comparison - **Low Quality** (+1): Limited technical contribution or incomplete evaluation ### Pass Threshold - **Minimum Score**: 6/10 points required to pass filtering - **Strong Relevance Override**: Papers with clear "music/song generation" focus may pass with lower scores ## Workflow Integration This skill integrates with the broader research workflow: 1. **Input**: Raw paper list from arXiv search 2. **Processing**: Applies scoring system to each paper 3. **Output**: Categorizes papers as "passed" or "filtered" 4. **Audit Trail**: Maintains complete record for manual recall ## Output Format ### Local File Storage - **Main Log**: `research/{domain}/quality_filtering/quality_filtering_log.md` - **Append Mode**: All results appended to single comprehensive file - **Directory Structure**: Automatically created if missing ### Log Structure Each filtering session includes: - **Session Header**: Date, domain, search parameters - **Scoring Standards**: Detailed criteria used - **Individual Paper Results**: Title, authors, score breakdown, decision - **Summary Statistics**: Pass/fail counts, score distribution - **Manual Recall Section**: List of filtered papers available for human review ## Usage Examples ```bash # Filter music generation papers quality_filter --domain "music_generation" --papers "[paper_list]" --date "2026-02-28" # Filter with custom threshold quality_filter --domain "speech_audio" --threshold 5 --papers "[paper_list]" ``` ## Files Created - `research/{domain}/quality_filtering/quality_filtering_log.md` (append mode) - Directory structure automatically created if missing ## Audit Trail Requirements All filtering decisions must include: - Complete score breakdown (relevance + quality components) - Clear pass/fail rationale - Preservation of filtered papers for manual recall - Timestamp and session context

path: README.md
content: # Quality Filter Skill

Academic paper quality filtering with rigorous scoring system and comprehensive audit trail.

## Directory Structure

```
quality_filter/
├── SKILL.md          # Skill definition and usage
├── README.md         # This file
└── scripts/          # Executable scripts (if any)
```

## Integration

This skill is designed to work within the broader music generation research workflow:

1. **Input**: Papers from arXiv search results
2. **Processing**: Applies systematic scoring based on relevance and quality
3. **Output**: Categorizes papers as passed/filtered with detailed rationale
4. **Audit Trail**: Maintains comprehensive log for manual recall

## Usage in Research Workflow

When processing papers from arXiv search:

```bash
# Example workflow integration
papers = arxiv_search("music generation", date_range)
filtered_papers = quality_filter(
    domain="music_generation",
    papers=papers,
    threshold=6
)
passed_papers = [p for p in filtered_papers if p.score >= 6]
```

## Quality Assurance

- All filtering decisions include complete score breakdown
- Filtered papers are preserved for manual recall
- Comprehensive audit trail maintained in append-mode log file
- Clear pass/fail rationale for each decision

path: _meta.json
content: ownerId: kn7c8ew58zsqxsn7a50925ypk97zzatv
slug: quality-filter
version: 1.0.0
publishedAt: 1769434876997

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)