Extract SQLite database content to text-readable formats (CSV, JSON, Markdown) for LLM processing and content synthesis. Use when working with SQLite databases (.db, .sqlite, .sqlite3) from course materials, datasets, or applications that need to be converted to text formats for analysis, synthesis, or inclusion in LLM context windows. Routed from db-router skill.

Extract SQLite database content to text-readable formats (CSV, JSON, Markdown) for LLM processing and content synthesis. Use when working with SQLite databases (.db, .sqlite, .sqlite3) from course materials, datasets, or applications that need to be converted to text formats for analysis, synthesis, or inclusion in LLM context windows. Routed from db-router skill.

Extract SQLite database content to text-readable formats (CSV, JSON, Markdown) for LLM processing and content synthesis. Use when working with SQLite databases (.db, .sqlite, .sqlite3) from course materials, datasets, or applications that need to be converted to text formats for analysis, synthesis, or inclusion in LLM context windows. Routed from db-router skill.

Apply when concurrent actors might write to the same file, branch, key, or state object. Eliminate the sharing first; serialize structurally only when one shared writer is a real invariant.

Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'

Creates a scanner configuration to discover services (such as AI agents, MCP servers, and API metadata) from external platforms like AWS Bedrock, Microsoft Copilot, or Google Vertex AI. Use when setting up scanner discovery, configuring a new scanner, connecting to cloud AI platforms, or importing discovered services into Anypoint Exchange.

Secure an MCP server by applying a policy from the catalog. Handles multiple starting points: from an existing API Manager instance, from an MCP server asset in Exchange, or from scratch by publishing the MCP server first. Use when the user wants to secure an MCP server, add rate limiting, apply OAuth2, enforce IP allowlisting, or protect any MCP server with a policy — regardless of where they are in the setup process.

Run Grove tests and diagnose failures. Use when the user asks to "run the tests", "run my test", "debug this test failure", "why is this test failing", "check if tests pass", or wants to execute and troubleshoot code example tests.

Build Google Apps Script automation for Sheets and Workspace. Custom menus, triggers (onEdit / time-driven / form submit), dialogs, sidebars, email batches, PDF export, external API. Use whenever the user wants to automate a Google Sheet, build a Sheets menu / sidebar / dialog, hit a Sheets row from email or a webhook, schedule a Sheets workflow, or asks 'how do I script this in Sheets'.

When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," or "free resource." This skill bridges engineering and marketing — useful for founders and technical marketers.

Use Databricks built-in AI Functions (ai_classify, ai_decide, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).

Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.

Use when changing tools/iac-codegen behavior or its version. Regenerate every listed Terraform resource and verify committed output.

Instructions to manage a local cache of GitHub repositories. This would typically done in cases where the user want to perform research/analysis on a repository. Invoke whenever you need to clone a repo that isn't present locally, bring an existing clone up to date, or remove a repo from the cache. This skill handles only the mechanical filesystem/git operations — not research, analysis, or anything about the repo's contents.

Learn from public breach disclosures — extract the audit question each one implies and check your own stack. Capital One IMDS abuse, LastPass vault exfiltration, Okta Lapsus$, Snowflake credential reuse, MOVEit, SolarWinds, Equifax, Target POS, Codecov, Uber, Twilio, Polymarket's compromised DebugBear script — what would you check now if your boss said 'could that happen to us?' Use when the user mentions 'breach analysis,' 'lessons learned,' 'security postmortem,' 'breach patterns,' 'breach lessons,' 'has this happened to us,' 'apply breach lessons,' 'preempt breaches,' 'security retrospective,' 'real-world security incidents,' or wants to harden against known attacker playbooks.

Route WordPress build and audit requests to the right implementation path for a Studio-backed site. Use when the user wants WordPress work and it is not yet clear whether the task should be handled as site creation, theme work, a custom block, a plugin, or an audit.

Route WordPress build and audit requests to the right implementation path for a Studio-backed site. Use when the user wants WordPress work and it is not yet clear whether the task should be handled as site creation, theme work, a custom block, a plugin, or an audit.

Route WordPress build and audit requests to the right implementation path for a Studio-backed site. Use when the user wants WordPress work and it is not yet clear whether the task should be handled as site creation, theme work, a custom block, a plugin, or an audit.

Automatically infer formal correctness properties from Verilog/SystemVerilog RTL code and generate SystemVerilog Assertions (SVA). Identifies control-flow invariants (mutual exclusion, valid-ready handshakes, pipeline ordering, safety properties), liveness expectations, and temporal properties. Use when working with RTL designs that need formal property generation, when adding assertions to existing RTL, or when users ask to infer properties, generate assertions, or create formal specifications from hardware designs.

Hardware verification tool for checking functional equivalence between two RTL designs (Verilog). Use when users need to: (1) Verify if two RTL versions are functionally equivalent, (2) Compare original vs. refactored RTL code, (3) Validate design changes or optimizations, (4) Identify semantic vs. cosmetic differences, (5) Generate counterexamples for non-equivalent designs. Analyzes interface alignment, state variables, logic differences, and produces detailed equivalence verdicts with plain language explanations. Particularly effective for design verification, code reviews, and regression testing of RTL modifications.