Results for “sendlane”

Run the standard single-cell RNA-seq analysis pipeline with Scanpy on AnnData — QC filtering, normalization, dimensionality reduction (PCA, UMAP, t-SNE), Leiden/Louvain clustering, marker/differential expression, PAGA trajectories, and plotting. Use when analyzing scRNA-seq data through clustering, cell-type annotation, DE, or pseudotime workflows; for building or reading the .h5ad data structure itself (layers, obs/var, concatenation, backed mode) prefer alterlab-anndata instead, and for RNA velocity from spliced/unspliced counts prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.

Run RNA velocity analysis with scVelo on single-cell RNA-seq data — estimate cell-state transitions from spliced/unspliced mRNA dynamics, infer trajectory direction, compute latent time, and identify driver genes. Use when adding directionality to trajectories or studying differentiation dynamics from spliced/unspliced layers (velocyto/STARsolo output); for the general QC, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for .h5ad data-structure I/O and layer wrangling prefer alterlab-anndata instead. Part of the AlterLab Academic Skills suite.

SENTINEL v2.0 — Quality Trader Convergence Scanner. Inverted pipeline: find ELITE/RELIABLE traders, see where they converge. When 5+ quality traders hold the same asset in the same direction, enter.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

Build streaming data pipelines with Redpanda Connect using declarative YAML configs, Bloblang mappings, and component discovery. Covers running, linting, and dry-running pipelines.

Provides a structured pre-launch checklist, staged rollout plan, monitoring setup, and rollback strategy for safe production deployments.

Configure, deploy, and manage Senpi Trading Runtime (OpenClaw plugin @senpi-ai/runtime) for automated on-chain position tracking with DSL trailing stop-loss protection. Use when a user needs to create or modify runtime YAML files, configure DSL (Dynamic Stop-Loss) exit engine parameters (phases, tiers, time-based cuts), set up the position_tracker scanner to monitor a wallet's positions on Hyperliquid, install/list/delete runtimes via CLI, or inspect DSL-tracked positions. The runtime does NOT create strategy wallets; create/get the strategy wallet via Senpi MCP first, then link that existing wallet in runtime YAML. Triggers on mentions of senpi, Senpi runtime, DSL exit, stop-loss tiers, position tracker, trailing stop, openclaw senpi, dsl_preset, or strategy YAML configuration."

Automates the collection of suspicious files from endpoints and email gateways, submission to sandbox and multi-engine scanners, and generation of verdicts with IOCs for SIEM integration.

Land and deploy workflow. Merges the PR, waits for CI and deploy, verifies production health via canary checks. Takes over after /ship creates the PR. Use when: "merge", "land", "deploy", "merge and verify", "land it", "ship it to production". (gstack)

Land and deploy workflow. (gstack)