Marketplace
Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
- 143.8K
- listings
- 1
- installs
- 0
- reviews
- 38.7K
- publishers
A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
warehouse
Plan and review read-only data warehouse analysis with explicit scope, privacy, provenance, and validation checks.
data-quality-auditor
Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or
portaljs-check-data-quality
Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase r
hubspot-contact-dedup
Deduplicate HubSpot contacts at production scale — surviving import storms, wrong-winner merges, fuzzy-match blind spots, association orphans, rate-limit exhaustion, and silent merge failures on confl
snowflake-data-quality-sentinel
Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows. Use when evaluations, definitions, schedules, notifications, anomaly state, or monitoring
hubspot-contact-dedup
Deduplicate HubSpot contacts at production scale — surviving import storms, wrong-winner merges, fuzzy-match blind spots, association orphans, rate-limit exhaustion, and silent merge failures on confl
podium-contact-dedup
Deduplicate Podium contacts in production and survive the data-quality failures — phone-format inconsistency producing four contacts for one phone, merge-api ordering that silently discards the richer
snowflake-data-quality-sentinel
Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows. Use when evaluations, definitions, schedules, notifications, anomaly state, or monitoring
data-validator
Validate data quality in CSV, JSON, and database exports by checking for missing values, type mismatches, duplicates, outliers, and schema violations. Use when building ETL pipelines, auditing data im
great-expectations
Great Expectations is a Python framework for data quality testing and validation. Learn to define expectations, create validation suites, build data docs, and integrate with data pipelines for automat
label-studio
Open-source data labeling and annotation platform for ML projects. Supports text, image, audio, video, and time-series data. Features configurable labeling interfaces, ML-assisted labeling, team colla
soda
You are an expert in Soda, the data quality platform for testing, monitoring, and profiling data. You help developers write data quality checks in YAML that validate freshness, completeness, uniquenes
Find
Search or browse by kind. Every card shows who made it, how many people installed it and what they think.
Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
Rate and publish
Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.
Prefer the terminal? osr stack apply registry://starter installs the starter template.