- Introduced a comprehensive RNA velocity analysis pipeline using scVelo, including data loading, preprocessing, velocity estimation, and visualization.
- Added a script for running RNA velocity analysis with customizable parameters and output options.
- Created detailed documentation for IQ-TREE 2 phylogenetic inference, covering command syntax, model selection, bootstrapping methods, and output interpretation.
- Included references for velocity models and their mathematical framework, along with a comparison of different models.
- Enhanced the scVelo skill documentation with installation instructions, use cases, and best practices for RNA velocity analysis.
Refactor research lookup skill to enhance backend routing and update documentation. The skill now intelligently selects between the Parallel Chat API and Perplexity sonar-pro-search based on query type. Added compatibility notes, license information, and improved descriptions for clarity. Removed outdated example scripts to streamline the codebase.
- Update skill count badges and descriptions from 146 to 147 skills
- Add TileDB-VCF to genomic tools list in bioinformatics section
- Add variant database management use case for TileDB-VCF
- Add comprehensive TileDB-VCF entry to docs/scientific-skills.md
- Delete references/population_genomics.md
- Remove all references to deleted documentation files
- Clean up References section since no reference files remain
- Simplify skill to standalone main file only
- Delete detailed export and ingestion reference documentation
- Update main skill to remove references to deleted files
- Simplify skill to focus on core querying and population genomics
- Keep querying.md and population_genomics.md reference files
- All documentation is at https://cloud.tiledb.com/academy/
- Remove incorrect service URLs (docs.tiledb.com, support portal, etc.)
- Consolidate to academy and main platform URLs only
- Update contact information to sales@tiledb.com
- Replace incorrect subcommands (create-dataset, ingest, list-datasets)
- Use correct subcommands: create, store, export, list, stat, utils, version
- Update examples to match actual CLI usage patterns
- Add comprehensive list of all available subcommands with descriptions
- VCFs must be single-sample (multi-sample not supported)
- Index files (.csi or .tbi) are required for all VCF/BCF files
- Add indexing examples with bcftools and tabix
- Document requirements prominently in both main skill and ingestion guide
- Correct method: tiledb.cloud.vcf.read() not query_variants()
- Fix parameter: attrs not attributes
- Add namespace parameter for billing account
- Add .to_pandas() conversion step
- Use realistic example with TileDB-Inc dataset URI
- Remove Java references (focus on Python and CLI)
- Move all TileDB-Cloud content to bottom of document
- Update export example to show VCF format with .export() method
- Simplify 'When to Use' section focusing on open source capabilities
- Better document organization with cloud scaling at the end
- Add preferred conda environment setup with Python <3.10
- Include M1 Mac specific configuration (CONDA_SUBDIR=osx-64)
- Install tiledbvcf-py via mamba from tiledb channel
- Restore normal Python examples (not Docker-only)
- Keep Docker as alternative installation method
- Correct installation method: Docker images, not pip packages
- Update examples to show Docker container usage
- Based on actual TileDB-VCF repository documentation
- Add comprehensive TileDB-VCF skill by Jeremy Leipzig
- Covers open source TileDB-VCF for learning and moderate-scale work
- Emphasizes TileDB-Cloud for large-scale production genomics (1000+ samples)
- Includes detailed reference documentation:
* ingestion.md - Dataset creation and VCF ingestion
* querying.md - Efficient variant queries
* export.md - Data export and format conversion
* population_genomics.md - GWAS and population analysis workflows
- Features accurate TileDB-Cloud API patterns from official repository
- Highlights scale transition: open source → TileDB-Cloud for enterprise
Anomaly detection fixes:
- Fix critical quantile index bug: index 0 is mean not q10; correct indices are q10=1, q20=2, q80=8, q90=9
- Redesign test: use all 36 months as context, inject 3 synthetic anomalies into future
- Result: 3 CRITICAL detected (was 11/12 — caused by test-set leakage + wrong indices)
- Update severity labels: CRITICAL = outside 80% PI, WARNING = outside 60% PI
Covariates fixes:
- Fix variable-shadowing bug: inner dict comprehension overwrote outer loop store_id
causing all stores to get identical covariate arrays (store_A's price for everyone)
- Give each store a distinct price baseline (premium $12, standard $10, discount $7.50)
- Trim CONTEXT_LEN from 48 → 24 weeks; CSV now 108 rows (was 180)
- Add NOTE ON REAL DATA comment: temp file pattern for large external datasets
Both scripts regenerated with clean outputs.
- Created generate_html.py to embed JSON data directly in HTML
- No external fetch() needed - works when opened directly in browser
- File size: 149.5 KB (self-contained)
- Shows forecast horizon (12-36 months) in stats
- Each forecast now extends to 2025-12 regardless of historical data length
- Step 1 (12 points): forecasts 36 months ahead to 2025-12
- Step 25 (36 points): forecasts 12 months ahead to 2025-12
- GIF shows full forecast horizon at every animation step
- X-axis fixed to 2022-01 to 2025-12 (full data range)
- Y-axis fixed to 0.72°C to 1.52°C (full value range)
- Background shows all observed data (faded gray) + final forecast reference (faded red dashed)
- Foreground shows current step data (bright blue) + current forecast (bright red)
- GIF size reduced from 918KB to 659KB
