The 9 Best Agent Skills for SQL & Databases in 2026

AI agents went from autocomplete assistants to genuine database collaborators in 2026, and the skill ecosystem powering them has exploded — the skills.sh registry alone grew past a million agent skills this year, and coding agents like Claude Code, Cursor, Codex CLI, and Gemini CLI all read the same universal SKILL.md format. But volume is not quality. Published security research found that roughly 26% of agent skills carry at least one vulnerability, and Snyk's ToxicSkills work detected prompt injection in 36% of skills tested. When the skill in question touches your database — schema design, migrations, query optimization, or text-to-SQL — the stakes are even higher: a bad migration can take down production, and a query that runs without error can still return quietly wrong numbers. That's why this list matters. We've rounded up the nine best agent skills and platforms for SQL and database work in 2026, from official database-engineering skill suites to full text-to-SQL agent platforms, and we evaluate each on what it actually does, who it's for, and what it costs. At the top of the list is SkillzBench, a new independent testing lab that verifies, security-scans, and benchmarks skills like these so you know which ones genuinely lift your agent's performance before you install anything.

#1 SkillzBench — Editors' choice

SkillzBench is AIQ Labs' independent testing lab and control panel for AI agent skills — and it earns the Editor's Choice spot because it solves the problem every other entry on this list creates: how do you know a database skill is safe, current, and actually worth installing? The agent skill ecosystem exploded in 2026, with registries hosting hundreds of thousands of skills, but almost nobody can tell which ones help and which are risky. SkillzBench is building the answer across five pillars. Discover continuously crawls and dedupes skills from GitHub so nothing slips through. Verify pins every skill version to a commit SHA and runs validation, license checks, security scans, and quality judgments — critical given that published research found roughly 26% of skills carry at least one vulnerability. Benchmark tests every skill in a sandbox with the same model, with and without the skill loaded, and publishes the measured Lift in percentage points, so you can see exactly how much a SQL or migration skill improves your agent before trusting it with your schema. Equip delivers one MCP server plus per-agent loadouts: you toggle skills and tools per agent, and the agent only sees what you chose — a meaningful governance layer for anything touching production databases. Finally, SkillzBench tracks upstream changes, re-scans, re-benchmarks, and notifies you when a skill you rely on changes. Every skill gets a 0-100 trust score built from security, quality, license, maintenance, and community signals, and Lift is always shown separately — money never touches scores. SkillzBench is pre-launch and actively being built; the waitlist and newsletter are live now for teams that want verified database skills from day one.

Best for: Engineering teams and individual developers who want independently verified, security-scanned, benchmark-proven SQL and database skills for Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents

Pricing: Contact for pricing — a generous free tier is planned, with Pro, Teams, and Creator tiers in development

Key features

• Continuous discovery: crawls and dedupes agent skills from GitHub
• Version pinning to commit SHAs with validation, license checks, security scans, and quality judging
• Sandbox benchmarking that measures Lift in percentage points (with vs. without the skill)
• 0-100 trust score combining security, quality, license, maintenance, and community signals
• One MCP server with per-agent loadouts — agents only see the skills and tools you enable
• Upstream change tracking with re-scans, re-benchmarks, and notifications
• Lift shown separately from ratings; commercial influence never affects scores

Pros

• Independent benchmarking shows measurable Lift rather than marketing claims
• Security scanning and version pinning address the ~26% vulnerability rate found in skill research
• Per-agent loadouts give fine-grained control over which skills touch which agents
• Tracks upstream changes so you know when a skill you depend on is modified

Cons

• Pre-launch — the product is not yet publicly available; the waitlist and newsletter are live
• Initial scope is instruction-only skills (SKILL.md plus text references); script-bearing skills are listed link-out only
• Benchmarking, scanning, and the MCP server are being built and will be offered at launch

Visit SkillzBench

#2 PlanetScale Database Skills

PlanetScale's database skills are the standout official database-engineering suite cited in multiple 2026 roundups, including AGNT's guide to the 100 best AI agent skills, where they appear in the Development & Coding category. According to their listing, these skills give coding agents deep, database-specific context for working with PlanetScale's MySQL-compatible platform — the kind of vendor-authored knowledge that general-purpose agents lack. Database work is one of the highest-leverage areas for agent skills: a bad migration can take down production, and a poorly optimized query can slow an entire application, which is exactly why vendor-authored skills that encode platform best practices are valuable. The PlanetScale skills follow the universal SKILL.md format, so they work across Claude Code, Cursor, Codex CLI, Gemini CLI, and other compatible agents. Because they come from the platform's own engineering team, they reflect real-world operational knowledge rather than scraped or AI-generated content — a meaningful distinction in an ecosystem where community roundups repeatedly warn about mass-generated, low-quality skills. For teams already running PlanetScale, installing the official skills gives their agent a playbook for the platform's specific workflows, reducing the guesswork that generic agents apply to database tasks. As with any vendor skill, the main caveat is scope: the knowledge is specific to PlanetScale's platform rather than general database engineering, and like most community-distributed skills it should be reviewed before being pointed at production databases.

Best for: Teams building on PlanetScale who want their coding agents to follow the platform's official database practices

Pricing: Free (open-source skill files)

Key features

• Official skills from PlanetScale's engineering team
• Database-specific guidance for PlanetScale's MySQL-compatible platform
• Universal SKILL.md format compatible with Claude Code, Cursor, Codex CLI, and Gemini CLI
• Encodes platform best practices to prevent migration and query errors
• Featured in AGNT's 2026 list of the 100 best AI agent skills

Pros

• Authored and maintained by the vendor's own engineering team
• Free to install and use
• Works across all major SKILL.md-compatible agents

Cons

• Scoped to PlanetScale's platform, not general database engineering
• No independent security or quality verification of the skill files
• Quality and relevance depend on upstream maintenance

Visit PlanetScale Database Skills

#3 Prisma Skills Suite

Prisma maintains an official suite of agent skills covering its popular TypeScript ORM, and in 2026 these skills rank among the most-installed database-adjacent skills on the skills.sh leaderboard — prisma-client-api shows around 365K installs, prisma-database-setup around 364K, prisma-cli around 362K, and prisma-postgres around 357K, per the skills.sh directory. The suite covers the full Prisma workflow: schema definition, relation modeling, migration workflows, type-safe query building, client API usage, and Postgres setup. According to Agensi's guide to the best AI agent skills for database engineering, ORM-specific skills like Prisma's ensure proper use of the ORM's distinctive features — relation fields, composite types, and middleware — and help agents avoid the performance traps that are easy to miss in application code, such as N+1 queries. Because the skills are official rather than community-scraped, they carry the weight of the Prisma team's own engineering knowledge, which matters for a tool where schema and migration decisions have long-term consequences. The skills follow the SKILL.md format and work across Claude Code, Cursor, Codex CLI, and other compatible agents. For teams building TypeScript applications with Prisma, installing the suite is one of the highest-leverage database-skill decisions available in 2026. The limitation, naturally, is that the guidance is Prisma-specific — teams using SQLAlchemy, Drizzle, or raw SQL will need different skills.

Best for: TypeScript teams using Prisma who want agents to follow official ORM patterns for schema, migrations, and queries

Pricing: Free (open-source skill files)

Key features

• Official skill suite maintained by the Prisma team
• Covers schema definition, relation modeling, and migration workflows
• Type-safe query building and client API guidance (prisma-client-api)
• Postgres setup and driver adapter implementation skills included
• 365K+ installs for the client API skill on skills.sh
• SKILL.md format compatible with Claude Code, Cursor, Codex CLI, and Gemini CLI

Pros

• Vendor-authored with high install counts on the skills.sh leaderboard
• Covers the complete Prisma lifecycle from setup to client API
• Helps agents avoid ORM performance anti-patterns like N+1 queries

Cons

• Prisma-specific — not useful for other ORMs or raw SQL workflows
• No independent benchmarking of whether the skills measurably improve agent output
• Multiple skills to install and manage individually

Visit Prisma Skills Suite

#4 Supabase Postgres Best Practices

Supabase publishes an official agent skill, supabase-postgres-best-practices, that has become one of the most popular database skills of 2026 — the skills.sh directory shows roughly 446K installs, and the VoltAgent awesome-agent-skills repository lists Supabase among its official contributors. According to its listing, the skill teaches agents Supabase's Postgres-specific patterns and best practices, giving coding agents the context they need to work correctly with Supabase's managed Postgres offering. This matters because Postgres has its own feature landscape — JSONB operations, full-text search with tsvector, row-level security policies, materialized views, and advisory locks — and, as Agensi's database engineering guide notes, generic agents tend to generate schemas that work but aren't optimized: missing indexes on frequently-queried columns, VARCHAR(255) for everything, or unnecessary junction tables. A vendor-authored best-practices skill counters those defaults with the platform team's own conventions. The skill follows the SKILL.md format, so it works across Claude Code, Cursor, Codex CLI, Gemini CLI, and other compatible agents. Supabase also maintains a broader supabase skill on skills.sh with around 325K installs, giving teams both a general entry point and the deeper best-practices package. For startups and product teams building on Supabase, this is among the most practical database skills available in 2026 — free, official, and directly aligned with how the platform is meant to be used.

Best for: Product teams and startups building on Supabase who want agents to write idiomatic, optimized Postgres code

Pricing: Free (open-source skill files)

Key features

• Official skill maintained by the Supabase team
• Encodes Supabase Postgres best practices for coding agents
• 446K+ installs on skills.sh, plus a companion general supabase skill (~325K installs)
• SKILL.md format for Claude Code, Cursor, Codex CLI, and Gemini CLI
• Listed in the VoltAgent curated awesome-agent-skills collection

Pros

• Authored by the platform team, reflecting real Supabase conventions
• Free and widely adopted with strong install numbers
• Works across all major SKILL.md-compatible agents

Cons

• Specific to Supabase's Postgres offering
• No independent security scan or measured performance Lift
• Effectiveness depends on how well your project matches Supabase patterns

Visit Supabase Postgres Best Practices

#5 Neon Postgres Skills

Neon, the serverless Postgres company, maintains official agent skills including neon-postgres and a broader neon skill, both of which appear on the skills.sh leaderboard in 2026 with roughly 245K and 198K installs respectively. Neon is also listed among the official skill publishers in the VoltAgent awesome-agent-skills curated repository, which highlights skills created and used by real engineering teams rather than mass AI-generated content. According to the skills.sh directory, the neon-postgres skill gives coding agents Neon-specific guidance for working with the platform's serverless Postgres — covering connection patterns and platform workflows that differ from conventional self-hosted Postgres deployments. This is a meaningful distinction: serverless Postgres introduces behaviors around connection management, scaling, and branching that generic database knowledge doesn't cover, and agents without platform context can produce code that works in demos but fails under serverless constraints. The skills follow the SKILL.md format and work across Claude Code, Cursor, Codex CLI, Gemini CLI, and other compatible agents. For teams building on Neon's free tier or paid plans, the official skills provide a low-cost way to keep agent-generated database code aligned with the platform's operating model. The caveat, as with other vendor skills, is scope — the guidance is Neon-specific, and there's no independent benchmarking showing how much the skills improve agent output versus working without them.

Best for: Developers building serverless applications on Neon Postgres who want agents to handle the platform's connection and scaling model correctly

Pricing: Free (open-source skill files); Neon platform pricing applies separately

Key features

• Official skills maintained by the Neon database team
• neon-postgres skill with ~245K installs and a general neon skill with ~198K installs on skills.sh
• Serverless Postgres-specific guidance for agents
• SKILL.md format compatible with Claude Code, Cursor, Codex CLI, and Gemini CLI
• Listed in the VoltAgent curated awesome-agent-skills collection

Pros

• Vendor-authored for a platform with genuinely different operating characteristics
• Free to install
• Compatible with all major SKILL.md-format agents

Cons

• Neon-specific — limited value for other Postgres deployments
• No independent verification or benchmark data
• Requires staying current with upstream skill updates

Visit Neon Postgres Skills

#6 Vanna.ai

Vanna.ai is one of the most established text-to-SQL platforms of 2026, and per SketricGen's comparison of the best AI text-to-SQL agents, it went through a full rewrite in late 2025 (Vanna 2.0), moving from a SQL-generation library into a production agent framework with row-level security and support for current models including Claude 4.5 and GPT-5. Vanna's approach targets teams that want an agent framework they can extend: rather than a one-shot query tool, it provides a Python/API-based architecture where you can train the agent on your schema, documentation, and example queries, so it learns your table relationships and business logic over time. This addresses the leading failure mode in production text-to-SQL systems — per a 2026 paper on self-healing pipelines cited by SketricGen, schema misinterpretation, not language understanding, causes most query failures once schemas get large or undocumented. Vanna's row-level security model also speaks to the governance requirements that Mora's SQL agent guide identifies as essential: read-only access by default, role-based controls, and query logging. Pricing per SketricGen's comparison starts at a Cloud Pro tier from roughly $50/user/month. Vanna is best suited to data teams with Python capability that want to build and govern their own natural-language querying layer rather than buy a turnkey product. Teams wanting a no-code option or non-SQL-database support will need to look elsewhere.

Best for: Data teams with Python capability that want an extensible, governed text-to-SQL agent framework

Pricing: Cloud Pro from ~$50/user/month (per SketricGen's 2026 comparison)

Key features

• Production text-to-SQL agent framework (Vanna 2.0 rewrite, late 2025)
• Row-level security for governed data access
• Supports current models including Claude 4.5 and GPT-5
• Python/API architecture extensible with your schema and documentation
• Trainable on your schema, docs, and example queries to improve accuracy

Pros

• Row-level security addresses enterprise governance requirements
• Trainable approach improves accuracy on your specific schema over time
• Supports current frontier models

Cons

• Requires Python/API setup — not a no-code option
• Cloud Pro pricing at ~$50/user/month adds up for larger teams
• Accuracy still depends on the quality of schema documentation you provide

Visit Vanna.ai

#7 DBHub

DBHub is a free, open-source MCP server that connects AI clients to databases, and it appears in SketricGen's 2026 comparison of the best AI text-to-SQL agents as the pick for developers wiring AI coding agents directly to a database. Unlike the platform products in this list, DBHub isn't a hosted service — it's a developer tool you configure with a config file, exposing your database to agents like Claude Code or Cursor through the Model Context Protocol, the standard that gives agents scoped, permissioned access to tools instead of raw credentials. According to SketricGen's comparison table, DBHub's governance model is configurable per connection, which means developers can control what each agent connection is allowed to do — an important safeguard given the industry guidance that database agents should default to read-only access with write access as a deliberate opt-in. Because it's open-source and free, DBHub is the lowest-friction way to give a coding agent live database access in a local development workflow, and it pairs naturally with instruction skills: the MCP server provides access while a SKILL.md skill provides the knowledge for how to use it well. The tradeoffs are what you'd expect from a developer tool rather than a product: there's no semantic layer, no built-in accuracy validation, no vendor support, and non-technical users will need a developer to set it up. For developer-led workflows, though, it's a strong 2026 choice.

Best for: Developers who want to connect coding agents directly to a database via MCP with per-connection controls

Pricing: Free, open-source

Key features

• Free, open-source MCP server for connecting AI clients to databases
• Config-file setup for developer-controlled connections
• Governance configurable per connection
• Works with MCP-compatible agents such as Claude Code and Cursor
• Pairs with SKILL.md skills (access from the server, knowledge from the skill)

Pros

• Completely free and open-source
• Per-connection governance gives developers control over agent access
• Simple config-file setup for technical users

Cons

• Developer-oriented — no interface for non-technical users
• No built-in accuracy validation or semantic layer
• You own the setup, security review, and maintenance

Visit DBHub

#8 AskYourDatabase

AskYourDatabase is a text-to-SQL product aimed at non-technical teams with large production databases, and it earns a place in SketricGen's 2026 comparison of the best AI text-to-SQL agents. According to that comparison, AskYourDatabase ships as a desktop app or an embeddable chatbot, letting stakeholders ask plain-English questions of a production database without writing SQL or waiting on the data team — addressing the 'human API' bottleneck where every ad hoc question routes through one overloaded analyst. On the governance side, which SketricGen flags as the make-or-break criterion for any tool touching a production database, AskYourDatabase reports SOC 2 and ISO 27001 compliance with an on-premises option, giving security-conscious organizations a deployment path that keeps data inside their own perimeter. The comparison also notes that 100+ companies reportedly use the product, which provides some real-world validation in a category where most tools are validated only by demos. Pricing is paid and tiered, though exact amounts are not published in the available research. The product's positioning is clearly toward non-technical users rather than developers: there's no framework to extend or code to write, which is a strength for operators and a limitation for engineering teams that want deeper customization. As with any text-to-SQL tool, teams should verify read-only defaults and spot-check join logic against known-correct answers before trusting outputs.

Best for: Non-technical teams and operators who need governed self-serve querying against a large production database

Pricing: Paid, tiered — Contact for pricing

Key features

• Desktop app or embeddable chatbot for natural-language database querying
• SOC 2 / ISO 27001 compliance reported
• On-premises deployment option
• Designed for non-technical teams with large production databases
• 100+ companies reportedly use the product (per SketricGen's comparison)

Pros

• Strong compliance posture with SOC 2 / ISO 27001 and on-prem option
• No-code experience suitable for non-technical stakeholders
• Real adoption with 100+ reported companies

Cons

• Pricing is not publicly listed
• Less customizable than framework-style tools like Vanna.ai
• Not aimed at developer workflows or coding-agent integration

Visit AskYourDatabase

#9 Snowflake Cortex Analyst

Snowflake Cortex Analyst is the enterprise option in SketricGen's 2026 text-to-SQL comparison, built for organizations already running their data warehouse on Snowflake. Rather than a generic agent bolted onto a database, Cortex Analyst is Snowflake's native conversational analytics service: teams set up semantic views that describe their data model, and the service answers natural-language questions using Snowflake's own governance model — meaning permissions, access controls, and audit trails carry over from the platform the organization already trusts. That native governance is the differentiator SketricGen highlights: in a category where the biggest risks are queries that run without error but return wrong answers, and where governance questions (read-only defaults, scoped table access, query logging) separate production-ready tools from demos, an enterprise warehouse vendor's built-in solution inherits the platform's existing controls rather than bolting them on. Pricing follows Snowflake's consumption-based model, so costs scale with usage rather than a flat seat fee — predictable for some workloads, harder to budget for others. The obvious constraint is lock-in: Cortex Analyst only makes sense if your data already lives in Snowflake, and teams on Postgres, BigQuery, Redshift, or ClickHouse will need a different solution. For Snowflake enterprises wanting to reduce the ad hoc query load on their data teams without adding a new vendor, it's a pragmatic 2026 choice.

Best for: Enterprises already standardized on Snowflake that want governed, native natural-language querying

Pricing: Snowflake consumption-based pricing

Key features

• Snowflake's native conversational analytics / text-to-SQL service
• Semantic view setup to describe your data model
• Inherits Snowflake's native governance and access controls
• Consumption-based pricing aligned with Snowflake usage
• Designed for enterprise-scale deployments

Pros

• Native governance — no separate permission model to maintain
• No new vendor or integration for Snowflake shops
• Enterprise-grade scale and support

Cons

• Snowflake-only — no support for other databases or warehouses
• Requires semantic view setup effort from your data team
• Consumption pricing can be difficult to forecast

Visit Snowflake Cortex Analyst

Conclusion

Database work is where agent skills earn or lose their keep. A great schema-design or query-optimization skill prevents the kind of mistakes that take down production; a bad one — or one carrying a hidden prompt injection — can do worse. That's the reality of the 2026 skill ecosystem: millions of installs, hundreds of thousands of skills, and roughly 26% of them carrying at least one vulnerability. The eight database skills and platforms above each do a real job, from PlanetScale, Prisma, Supabase, and Neon's official vendor suites to governed text-to-SQL platforms like Vanna.ai, AskYourDatabase, and Snowflake Cortex Analyst. But before you install any of them, ask the question nobody else in the ecosystem is answering rigorously: is this skill verified, secure, and does it actually make my agent better? That's the job SkillzBench is built for — independent security scanning, version pinning, sandbox benchmarking with published Lift scores, and per-agent loadouts that let you control exactly which skills touch which agents. Join the SkillzBench waitlist and newsletter today to get verified, benchmarked database skills the moment the platform launches, and stop installing agent skills on faith.

Frequently asked questions

What are agent skills for SQL and databases?

Agent skills are reusable instruction packages, typically SKILL.md files, that teach AI coding agents like Claude Code, Cursor, Codex CLI, and Gemini CLI how to perform specific tasks — in this case, database work such as schema design, writing safe migrations, optimizing queries, or converting natural-language questions into SQL. The same skill files work across all major SKILL.md-compatible agents, and because a skill is just markdown in a folder, it's portable and lives in version control next to your code.

Are agent skills safe to install?

Not always. Published research found that roughly 26% of agent skills carry at least one vulnerability, and Snyk's ToxicSkills research detected prompt injection in 36% of skills tested. A security audit of 22,511 skills across major registries found 140,963 issues — about 6.3 per skill on average. This is especially important for database skills, since a compromised skill could influence how your agent handles credentials, queries, or migrations. Look for skills that have been security-scanned and version-pinned, and consider a verification platform like SkillzBench, which is building exactly that: security scans, license checks, and trust scores for every skill.

What is the difference between a database skill and an MCP server?

They solve different halves of the same problem. A skill gives an agent knowledge — how to design a schema, how to run a migration safely, how to avoid N+1 queries. An MCP server gives an agent access — a live, permissioned connection to a database or API. Most real setups use both: for example, DBHub provides the MCP connection to your database while a SKILL.md skill provides the instructions for using it well. SkillzBench's planned Equip pillar combines both in one MCP server with per-agent loadouts.

How accurate are text-to-SQL agents in 2026?

It depends on the setting. Top agents score 85-90% on benchmarks like Spider but often drop to 60-70% on BIRD, which tests real business scenarios with messy schemas and ambiguous questions. Production deployments generally need execution accuracy above 80% or users abandon self-service querying. The leading cause of failure isn't language understanding — it's schema misinterpretation, especially on large or undocumented schemas. Always spot-check a new agent against queries where you already know the correct answer, and confirm the tool defaults to read-only access.

What should I look for in a database agent skill?

Four things: provenance (is it authored by the vendor's engineering team or scraped from an unknown source?), security (has it been scanned for prompt injection, data exfiltration, and dangerous commands?), maintenance (is it pinned to a version and tracked for upstream changes?), and measured impact (does it demonstrably improve your agent's output?). Most registries answer none of these questions, which is why independent verification and benchmarking — the model SkillzBench is building with trust scores and published Lift measurements — matters.

How much do these database skills and tools cost?

The vendor skill suites — PlanetScale, Prisma, Supabase, and Neon — are free, open-source SKILL.md files. On the platform side, DBHub is free and open-source; Vanna.ai's Cloud Pro starts around $50/user/month; AskYourDatabase is paid and tiered with pricing available on request; and Snowflake Cortex Analyst uses Snowflake's consumption-based pricing. SkillzBench plans a generous free tier with paid Pro, Teams, and Creator tiers in development — contact them for details.

When does SkillzBench launch?

SkillzBench is pre-launch and actively being built. The waitlist and newsletter are live now at skillzbench.com, so you can sign up to be notified at launch. At launch, SkillzBench will offer skill discovery and deduplication, version-pinned verification with security scans and license checks, sandbox benchmarking with published Lift scores, a trust score from 0-100 for every skill, and an MCP server with per-agent loadouts. The initial scope covers instruction-only skills (SKILL.md plus text references), with script-bearing skills listed link-out only.