AI That Works
the Way You Would.
Build workflows where each step is a different specialist — one drafts, another reviews, another checks compliance, another matches your voice. Each tuned to your standards, all visible, all reusable.
One chatbot isn't enough
The models got better. The way we use them stayed single-threaded.
One model does everything
Your chatbot drafts, reviews, and polishes — all in the same voice, with the same strengths and blind spots. No specialisation, no second opinion.
First draft is final draft
There's no review step, no compliance check, no voice matching. What the model produces first is what you get — unless you manually prompt again.
You can't see what happened
Which model ran? What context did it have? What changed between versions? A chat window doesn't show you the process — just the last message.
Every conversation starts from zero
Your standards, your voice, your quality bar — none of it carries over. Every new chat forgets everything you've taught it.
Built for trust, not just speed
Six principles that make your AI pipeline visible, governable, and yours.
Your agents, your machine
Everything runs locally. Your prompts, profiles, and pipeline data never leave your infrastructure unless you choose to share them.
Right model for the right role
Assign different providers to different steps — Claude drafts, GPT-4 reviews, a local model handles sensitive data. Each specialist uses the model that fits.
Pipelines that improve over time
Every workflow is git-versioned. Compare runs, roll back changes, and see exactly what improved. Your quality process has a proper history.
Share your specialists
Publish your agent pipelines to the hub as signed, version-tracked packages. Others can inspect exactly what each step does before they install.
You're still the boss
Gate steps pause the pipeline for human review. You decide when specialists proceed and when they wait for your approval.
See every handoff
Every run is logged — which step ran, which model, what it produced. Background long pipelines, get notified when they finish, and trace any result back to its source.
What makes this different from a chatbot
Claude gives you step 1. Skrptiq gives you all 5.
A chatbot
Skrptiq
One model does every job
Each job gets its own specialist step — drafting, review, SEO, voice check
First output is final output
Steps review each other's work — compliance, quality, and tone checks built in
Generic — same voice for everyone
Persona dials and voice profiles tuned to your standards
Black box — no trace of what happened
Every handoff between steps is logged and traceable
Starts from zero every session
Profiles, templates, and quality settings persist across runs
One blog post. A full quality process.
This is a real pipeline from the hub — not a mockup. Every skrptiq workflow runs like this.
Screenshot: Ideation agent producing ranked topic ideas from seed keywords
Job: find the right topic
The ideation step has one job — generate 10 ranked topic ideas from your niche, audience, and seed keywords. Each idea comes with an angle, search intent, and timeliness assessment. You pick the winner.
Screenshot: Briefing agent output showing structured content brief with SEO targets
Job: structure the brief
A different step takes over. Your selected topic becomes a structured brief — outline, key points, tone guidance, word count targets, SEO keywords, and constraints. It hands off a clear spec to the next step.
Screenshot: Writing agent producing headline variants and polished copy
Job: write and polish
Two more specialists in sequence. One generates headline variants — direct, curiosity, benefit-led — with rationale for each. The next applies surface corrections matched to your grammar strictness and voice profile.
Screenshot: Three parallel review steps running: SEO, compliance, and production checklist
Job: review — three ways at once
Three separate review steps run in parallel, each with a different job. SEO analysis scores on-page quality. Brief compliance checks the output against the original plan. A production checklist validates everything else.
Screenshot: Final output with execution history showing all specialist handoffs
Result: publish-ready, fully traced
Every job in the pipeline is logged — which step ran, which model, what it produced. If a reviewer flagged something, you can see exactly where and why. Trace any sentence back to the specialist that wrote it.
Two modes. One engine.
Workflows
Build the full pipeline. Map out specialist steps, connect providers, set quality gates. Inspect and refine every handoff. This is where you design your process.
Agents
Trigger a workflow from a hotkey or the quick panel. Same pipeline, same quality process — just faster to launch. Agents are workflows optimised for daily use.
Built for how you actually work
Whether you're building your own agent pipeline or governing a team's, the process is yours to shape.
For AI-heavy operators
Build your specialist team
You run multi-step AI processes daily — research, drafting, analysis. Design pipelines with specialist steps, cross-provider review, and persona dials tuned to your standards. Trigger them as agents when you need speed.
For technical leads
Govern your team's AI quality
Package your best pipelines as signed skrpts with gate steps for human approval. Your team starts from a tested, inspectable baseline — not a shared chat prompt with no audit trail.
For solo builders
Stop starting from scratch
Browse 60+ agent packages on the hub. Try any of them with pre-filled examples before committing. Wire your own steps into a pipeline, set your voice profile once, and reuse it tomorrow with different inputs.
Get early access
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