quality-assessment
Evaluate ENCODE experiment quality using standard metrics and audit flags. Use when the user asks about data quality, wants to filter for high-quality experiments, needs to interpret quality metrics (FRiP, NSC, RSC, NRF, IDR, TSS enrichment, fragment size), wants to understand ENCODE audit warnings, needs to compare quality across experiments, or is deciding whether data is usable for their analysis. Also use when the user mentions QC, quality control, or data filtering.
npx skills add majiayu000/claude-skill-registry --skill quality-assessment-ammawla-encode-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Assess ENCODE Data Quality
How it works
The skill retrieves experiment details and audit status, interprets ENCODE audit flags (ERROR, NOT_COMPLIANT, WARNING, INTERNAL_ACTION), and evaluates core quality metrics for each assay (ChIP-seq, ATAC-seq, RNA-seq, WGBS, Hi-C, CUT&RUN/CUT&Tag) using threshold guidance and recommended read depth where provided. It consolidates multiple orthogonal metrics (e.g., FRiP, NSC, RSC, NRF, IDR, TSS enrichment, fragment size, mapping rate, duplication, coverage) to help determine if an experiment meets quality standards for analysis. It explains each audit category and flag, and provides context on how metrics relate to data usability. It also notes read-depth requirements and interpretations specific to each assay class and highlights caveats around platform effects and antibody validation where applicable.
When to use it
- User asks about data quality, QC metrics, or whether an experiment is reliable
- User wants to filter experiments by quality (FRiP, NSC, RSC, NRF, IDR, TSS enrichment)
- User asks "is this experiment good enough?" or "should I use this data?"
- User needs to interpret ENCODE audit flags (ERROR, NOT_COMPLIANT, WARNING)
- User wants to compare quality across multiple experiments
- User is selecting high-quality experiments for a meta-analysis or aggregation
What it can touch
- Interprets audit flags and QC metrics embedded in experiment metadata
- References core QC thresholds and read-depth recommendations per assay type
Caveats
- Quality assessment is not single-mmetric; requires integrating multiple measures within context
- Thresholds and recommendations reference ENCODE guidelines and literature cited in the material; real data may require context-specific interpretation
# Assess ENCODE Data Quality ## When to Use - User asks about data quality, QC metrics, or whether an experiment is reliable - User wants to filter experiments by quality (FRiP, NSC, RSC, NRF, IDR, TSS enrichment) - User asks "is this experiment good enough?" or "should I use this data?" - User needs to interpret ENCODE audit flags (ERROR, NOT_COMPLIANT, WARNING) - User wants to compare quality across multiple experiments - User is selecting high-quality experiments for a meta-analysis or aggregation Help the user evaluate whether ENCODE experiments meet quality standards for their analysis. Quality assessment is not a single-metric exercise — it requires integrating multiple orthogonal measures in the context of the specific assay, biological system, and analytical goals. ## Literature Foundation | # | Reference | Key Contribution | |---|-----------|-----------------| | 1 | Landt et al. 2012, Genome Res, DOI:10.1101/gr.136184.111 (~3,500 cit) | ENCODE/modENCODE ChIP-seq guidelines; defined NSC, RSC, NRF, FRiP thresholds | | 2 | ENCODE Project Consortium 2020, Nature, DOI:10.1038/s41586-020-2493-4 (~1,656 cit) | ENCODE Phase 3; expanded quality standards to new assays, defined cCRE
- When to Use
- Literature Foundation
- Step 1: Retrieve Experiment Details and Audit Status
- Step 2: Interpret ENCODE Audit Flags
- Step 3: Evaluate ChIP-seq Quality (Landt et al. 2012)
- Core Metrics
- Read Depth Requirements
- IDR Analysis (Li et al. 2011)
- Antibody Validation
- Step 4: Evaluate ATAC-seq Quality (Buenrostro et al. 2013; Ou et al. 2018)
- Fragment Size Interpretation (Buenrostro et al. 2013)
- Step 5: Evaluate RNA-seq Quality (Conesa et al. 2016)
- Read Depth Requirements (Conesa et al. 2016)
- Strand Specificity
What does the quality-assessment skill do?
Evaluate ENCODE experiment quality using standard metrics and audit flags. Use when the user asks about data quality, wants to filter for high-quality experiments, needs to interpret quality metrics (FRiP, NSC, RSC, NRF, IDR, TSS enrichment, fragment size), wants to understand ENCODE audit warnings, needs to compare quality across experiments, or is deciding whether data is usable for their analysis. Also use when the user mentions QC, quality control, or data filtering.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill quality-assessment-ammawla-encode-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.