- 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.
Three research-backed cognitive science skills:
- consciousness-council: 12-archetype Mind Council deliberation for structured multi-perspective analysis
- dhdna-profiler: cognitive fingerprinting across 12 dimensions (DOI: 10.5281/zenodo.18736629)
- what-if-oracle: multi-branch scenario analysis with probability weighting (DOI: 10.5281/zenodo.18736841)
Each includes reference documentation. All MIT licensed.
Source: https://github.com/ashrafkahoush-ux/Claude-consciousness-skills
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 Removed outdated example scripts to streamline the codebase.
- 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
Create an all-out demonstration showing how TimesFM forecasts evolve
as more historical data is added:
- generate_animation_data.py: Runs 25 incremental forecasts (12→36 points)
- interactive_forecast.html: Single-file HTML with Chart.js slider
- Play/Pause animation control
- Shows historical data, forecast, 80%/90% CIs, and actual future data
- Live stats: forecast mean, max, min, CI width
- generate_gif.py: Creates animated GIF for embedding in markdown
- forecast_animation.gif: 25-frame animation (896 KB)
Interactive features:
- Slider to manually step through forecast evolution
- Auto-play with 500ms per frame
- Shows how each additional data point changes the forecast
- Confidence intervals narrow as more data is added
