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Found 78 Skills
World-class database schema design - data modeling, migrations, relationships, and the battle scars from scaling databases that store billions of rowsUse when "database schema, data model, migration, prisma schema, drizzle schema, create table, add column, foreign key, primary key, uuid, auto increment, soft delete, normalization, denormalization, one to many, many to many, junction table, polymorphic, enum type, index strategy, database, schema, migration, data-model, prisma, drizzle, typeorm, postgresql, mysql, sqlite" mentioned.
UiPath Process Mining via `uip pm` — build and operate a process app end-to-end from a CSV / event log: templates, data mapping, upload, ingest, the dbt (Snowflake) transformation layer, publish, and query it (metrics, percentiles, RCA). Covers `uipath.custom`, the `Cases.sql` optional-column gotcha, Case-linked data-model tables (add-table + re-ingest), the apply-not-reingest fix loop, fixing a wrong mapping in place via `apps data-mapping get|update` (no app rebuild), and editing the app model via `apps model fields` — a field's data kind / calculated fields, including the numeric→duration mismatch that locks dashboards open (DNA-46960). For Orchestrator/Data Fabric/Integration Service→uipath-platform. For `.flow`/Maestro→uipath-maestro-flow. For IXP→uipath-ixp.
Task generation skill based on the speckit workflow, used to generate actionable, dependency-ordered tasks.md from available design documents. Use this skill when you need to generate detailed task lists for feature development based on design documents such as spec.md, plan.md, data-model.md, contracts/, etc. Trigger words include "speckit tasks", "generate tasks", "task planning", "feature task decomposition", "create tasks.md", etc.
Validates planning artifacts (spec.md, plan.md, data-model.md, contracts/) for quality, completeness, and consistency. Checks for mandatory sections, cross-artifact consistency, and constitution compliance. Always run after /speckit.plan completes.
Verify implementation parity against approved design specs with severity-based decisions and fix guidance. Use when implemented UI must be compared against approved specs before release or sign-off; do not use for backend data-model or deployment pipeline decisions.
Author, explain, diagnose, and validate Splunk search-time field extractions and mappings to Common Information Model (CIM) datasets from representative events, configuration, and search evidence. Use for automatic key-value extraction, regex or delimiter extraction, props.conf EXTRACT and REPORT/transforms.conf rules, SPL extraction commands, aliases, calculated fields, lookups, event types, tags, value normalization, CIM field mapping, and missing or incorrect normalization; do not use for deployment execution, ingestion transport, app installation, knowledge-object governance, data-model acceleration, or unrelated search/dashboard repair.
Draw a software architecture, system, data-model or call-flow diagram as D2 text laid out by TALA (D2's whiteboard-style engine, open source since D2 0.9), look at it in the terminal before anyone opens an image, and check that the picture says what it claims — `d2 --layout=tala x.d2 x.txt` renders the layout as Unicode box drawing, `--ascii-mode standard` as plain ASCII for a README, and `assets/d2-facts.mjs` reads the drawn boxes and arrows back out of the render and holds them to a fact sheet. Loop: write `.d2` → `d2 fmt --check` / `validate` → facts → terminal render → PNG → fix the text. Use when asked for a D2 diagram, an architecture diagram kept as editable text in the repo, a TALA layout, a diagram that shows in a terminal or a code block, or when the repo already has `.d2` files. Not for animations or figures with a moving walk (`explanatory-animation`, `explain-with-anim`).
Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt
MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.
Audit and improve SQL quality systematically — catch performance smells in raw SQL, ORM-generated queries, schema/modeling decisions, migrations, or PR diffs, explain the database-level impact, give a corrected version, and make the tradeoff explicit. Generic by design: no project, domain, ORM, or language config — any context it needs (is this table transactional? is the scan intentional? what volume is expected?) is raised during analysis, never assumed. Reach for it whenever someone writes, reviews, or optimizes a query or data-access code — "review this query", "why is this slow", "check my migration", "is this index right", or when you see N+1, SELECT *, a cartesian/row explosion, three-plus joins, a missing date filter on a growing table, OFFSET pagination, LIKE '%term%', NOT IN with nullable columns, an unindexed ORDER BY, an unbounded list, or a long transaction — even when they never say the word "SQL". Use it both to validate new code and designs and to audit existing ones.
Use this skill when you need to create or modify a LookML Explore. This includes defining the Explore, joins, access grants, and basic configuration.
Converts legacy SQL to modular dbt models. Use when migrating SQL to dbt for: (1) Converting stored procedures, views, or raw SQL files to dbt models (2) Task mentions "migrate", "convert", "legacy SQL", "transform to dbt", or "modernize" (3) Breaking monolithic queries into modular layers (discovers project conventions first) (4) Porting existing data pipelines or ETL to dbt patterns Checks for existing models/sources, builds and validates layer by layer.