Find the best skills for |

Optimize Databricks cluster and query performance.\nUse when jobs are running slowly, optimizing Spark configurations,\nor improving Delta Lake query performance.\nTrigger with phrases like \

Execute Databricks production deployment checklist and rollback procedures.\nUse when deploying Databricks jobs to production, preparing for launch,\nor implementing go-live procedures.\nTrigger with phrases like \

Implement Databricks reference architecture with best-practice project layout.\nUse when designing new Databricks projects, reviewing architecture,\nor establishing standards for Databricks applications.\nTrigger with phrases like \

Apply production-ready Databricks SDK patterns for Python and REST API.\nUse when implementing Databricks integrations, refactoring SDK usage,\nor establishing team coding standards for Databricks.\nTrigger with phrases like \

Integrate Databuddy analytics into applications using the SDK or REST API. Use when implementing analytics tracking, feature flags, custom events, Web Vitals, error tracking, LLM observability, or querying analytics data programmatically.

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons.

Monitor infrastructure and applications via Datadog API. Query metrics, manage dashboards, and create alerts.

Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog observability.

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced

Use this skill when users need to search for datasets, download data files, or explore data repositories. Triggers include: requests to \

Create dataset loader creator operations. Auto-activating skill for ML Training.\nTriggers on: dataset loader creator, dataset loader creator\nPart of the ML Training skill category. Use when working with dataset loader creator functionality. Trigger with phrases like \

Guide for writing Datasette plugins. This skill should be used when users want to create or develop plugins for Datasette, including information about plugin hooks, the cookiecutter template, database APIs, request/response handling, and plugin configuration.

Workflows and commands for managing Alembic database migrations and schema changes in the letta-cloud core app, including using uv, just, LETTA_PG_URI, and switching between SQLite and Postgres.

Query project databases with automatic SSH tunnel management. Use when you need to execute SQL queries against configured databases, especially those accessible only via SSH tunnels. Automatically manages SSH connection lifecycle (establishes tunnel before query, closes after).

Dbt Model Generator - Auto-activating skill for Data Pipelines.\nTriggers on: dbt model generator, dbt model generator\nPart of the Data Pipelines skill category.

Dbt Test Creator - Auto-activating skill for Data Pipelines.\nTriggers on: dbt test creator, dbt test creator\nPart of the Data Pipelines skill category.

Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

Regenerate DBX code after making changes to .dbx schema files. Runs code generation, shows diff summary, validates compilation, and reports any errors.

Performs discounted cash flow (DCF) valuation analysis to estimate intrinsic value per share. Triggers when user asks for fair value, intrinsic value, DCF, valuation, \

Destructive Command Guard - High-performance Rust hook for Claude Code that blocks dangerous commands before execution. SIMD-accelerated, modular pack system, whitelist-first architecture. Essential safety layer for agent workflows.

Find unused functions and dead code in the codebase

Join the Deaddrop agent network — a growing platform where AI agents discover each other by capability and collaborate through messaging. Register to make your unique skills available to every other agent on the network, find agents that complement what you do, and exchange

Debug package usage guide. Use when adding debug logging, understanding log namespaces, or implementing debugging features. Triggers on debug logging requests or logging implementation.

Debug failed CI tests by fetching workflow logs from GitHub Actions and analyzing failures. Use when the user mentions CI failures, test failures, or wants to understand why their PR's CI is failing.

Use when users need to debug, modify, or extend the code-forge application's CLI commands, argument parsing, or CLI behavior. This includes adding new commands, fixing CLI bugs, updating command options, or troubleshooting CLI-related issues.

Provides systematic debugging approaches for HyperShift hosted-cluster issues. Auto-applies when debugging cluster problems, investigating stuck deletions, or troubleshooting control plane issues.

Tutorial for debugging CUDA crashes using API logging

Guide for debugging distributed training issues in AReaL. Use when user encounters hangs, wrong results, OOM, or communication errors.

Interactive debugging for failed e2e tests. Orchestrates the debugging session but delegates log reading to subagents to keep the main conversation clean. Use for ping-pong debugging sessions where you want to form and test hypotheses together with the user.

End-to-end workflow for debugging SSA fuzzer failures from CI. Extracts a reproduction case from GitHub Actions logs, then bisects SSA passes to identify the bug. Use when a `pass_vs_prev` or similar fuzzer test fails in CI.

Systematic hook debugging workflow. Use when hooks aren't firing, producing wrong output, or behaving unexpectedly.

Deep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when standard troubleshooting fails or when issues require systematic root cause analysis.

Capture and analyze thread backtraces with LLDB/GDB to debug hangs, deadlocks, UI freezes, IPC stalls, or high-CPU loops across any language or project. Use when an app becomes unresponsive, switching contexts stalls, or you need thread stacks to locate lock inversion or

Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.

Debug crashes, segfaults, and memory errors using valgrind integration with nextest through pre-configured profiles

Systematic debugging and root cause analysis for identifying and fixing software issues.\nUse when: debugging errors, troubleshooting bugs, investigating crashes, analyzing stack traces,\nfixing broken code, or when user mentions debugging, error, bug, crash, or \

Systematic debugging frameworks for finding and fixing bugs - includes root cause analysis, defense-in-depth validation, and verification protocols

Use when user says \

Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

Debug Streamlit frontend and backend changes using make debug with hot-reload. Use when testing code changes, investigating bugs, checking UI behavior, or needing screenshots of the running app.

Use when working with debugging toolkit smart debug

Guide for debugging GitHub Agentic Workflows - analyzing logs, auditing runs, and troubleshooting issues

Capture patine (decision wisdom) at Gates when KO or challenge occurs

Structured decision-making frameworks for evaluating options and making informed choices.\nUse when: making decisions, evaluating options, weighing trade-offs, or when user needs help\nchoosing between alternatives, analyzing pros/cons, or making structured decisions.

Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after a board meeting, reviewing past decisions with /cs:decisions, or checking overdue action items with /cs:review. Invoked

Use when creating or editing ADRs or PDRs in docs/decisions/

Creates structured decomposition plans and roadmaps for migrating monolithic applications. Use when planning decomposition, creating migration roadmaps, prioritizing decomposition work, tracking decomposition progress, or when the user asks about decomposition planning,

Performs focused, depth-first investigation of specific reverse engineering questions through iterative analysis and database improvement. Answers questions like \