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    One source of truth for every AI capability your org ships.

    You build the AI solutions. But without a governed layer to manage connectors, vet Skills, and distribute capabilities, every project starts from zero. Skilder gives you the foundation to scale AI delivery across teams, frameworks, and use cases.

    "We had five teams maintaining five versions of the same summarizer. Now there's one vetted Skill in the library, and every framework pulls from the same source of truth."

    Staff AI Engineer, Series B SaaS

    The friction that slows you down

    Every team rebuilds the same thing

    Three teams wrote a contract summarizer this quarter. Nobody knew the others existed. Without a shared skill library, duplication is the default and maintenance is a nightmare.

    Unvetted skills break in production

    The prompt worked in your notebook. It failed at scale. Without a vetting workflow, every new capability is a production risk someone else discovers the hard way.

    No visibility across what's running

    You shipped 40 Skills across 12 agents. Which ones are actually used? Which ones fail silently? Without telemetry at the skill level, you're flying blind.

    ● In Practice

    Migrated 12 agents in 3 days. No rewrites.

    Context

    A platform team at a logistics company runs 12 production agents across LangChain, CrewAI, and custom stacks. They need to migrate everything to a new LLM provider.

    Challenge

    Each agent has its own hardcoded prompts, connector configs, and retry logic. Updating one means 12 separate code changes, tests, and deploys. Migration timeline: 3 sprints.

    With Skilder

    They moved prompts and connectors into Skills with versioned HATs. Each agent now references Skill IDs instead of hardcoded logic. The team updated the underlying LLM connector in one place and rolled it out gradually with feature flags.

    Result

    Migration completed in 3 days

    Zero agent rewrites. Rollback capability on every Skill. The team now treats AI infrastructure like any other service dependency.

    How it works for you

    01

    Publish Skills to a shared library

    Author a Skill with its instructions, connectors, and guardrails. Submit it through your org's vetting workflow. Once approved, it's available for every team and every framework to use.

    02

    Distribute to any framework

    Reference any approved Skill by ID from LangChain, CrewAI, n8n, or your own stack. No copy-pasting prompts. No hardcoding connectors. One source, many consumers.

    03

    Monitor, version, improve

    See which Skills are used where, how they perform, and when they fail. Update a Skill once and every consumer gets the new version. Full telemetry, full control.

    Built for your AI stack

    Skill vetting and approval workflowFramework-agnostic Skill APIConnector governanceVersion pinning and rollbackUsage telemetry per Skill

    Ready to get started?

    Free to start. No credit card required.