flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
db-core-mutations-optimistic
collection.insert, collection.update (Immer-style draft proxy), collection.delete. createOptimisticAction (onMutate + mutationFn). createPacedMutations with debounceStrategy, throttleStrategy, queueStrategy. createTransaction, getActiveTransaction, ambient transaction context. Transaction lifecycle (pending/persisting/completed/failed). Mutation merging. onInsert/onUpdate/onDelete handlers. PendingMutation type. Transaction.isPersisted.
db-core
TanStack DB core concepts: createCollection with queryCollectionOptions, electricCollectionOptions, powerSyncCollectionOptions, rxdbCollectionOptions, trailbaseCollectionOptions, localOnlyCollectionOptions. Live queries via query builder (from, where, join, select, groupBy, orderBy, limit). Optimistic mutations with draft proxy (collection.insert, collection.update, collection.delete). createOptimisticAction, createTransaction, createPacedMutations. Entry point for all TanStack DB skills.
binding-ffi
Builds and maintains native Rust/C bindings and FFI layers that connect router-side classifiers and signal evaluation to compiled model runtimes. Use when adding or modifying native model bindings, updating FFI interfaces, or changing how the router calls into compiled classifier code.
routing-policy-change
Modifies routing policy after signal extraction, including matched-decision logic, candidate-model selection, and downstream looper behavior. Use when changing decision predicates, thresholds, priorities, model ranking, cost or latency routing, or other post-signal routing policy.
routing-policy-runtime
Maintains matched-decision predicates, looper policy, and candidate-model selection behavior after signal extraction. Use when the task changes decision trees, thresholds, downstream model ranking, or other routing policy that runs after signals are already produced.
training-stack-change
Modifies training-stack workflows, selector or embedding pipelines, evaluation artifacts, or runtime-facing outputs under src/training. Use when changing model selection training, embedding pipelines, evaluation scripts, experiments, or other training outputs that feed runtime behavior.
training-stack-runtime
Modifies training-stack workflows, selector or embedding pipelines, evaluation scripts, and runtime-facing artifact expectations. Use when a primary skill touches src/training, tools/make/models.mk, or training docs for runtime-fed artifacts.
shapely-compute
Computational geometry with Shapely - create geometries, boolean operations, measurements, predicates
gradient-methods
Problem-solving strategies for gradient methods in optimization
continuity-ledger
Create or update continuity ledger for state preservation across clears
session-investigator
Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool call/result correlation, and common failure patterns.
build-dashboard
Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.
bls-query
Queries U.S. Bureau of Labor Statistics data using the BLS MCP server. Use when user asks about CPI, inflation, unemployment, employment, wages, jobs, labor statistics, producer prices, or any economic indicator tracked by BLS. Maps natural language to correct series IDs and tools. Copied from https://github.com/larasrinath/bls_mcp
data-validate
Validate data and analysis before sharing - methodology, accuracy, bias, and data quality checks
csv-wrangling
Standard workflow order, tool selection matrix, and composition patterns for qsv CSV data wrangling
data-quality
Quality dimensions quick reference and remediation decision tree for tabular data assessment
reproducible-analysis
Machine-readable journal format for reproducible data analysis operations
chart-visualization
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 25 available options, extracts parameters based on detailed specifications, and generates a chart image using a Python script.
