structured-workflows is the missing system layer between your intent and a shipped PR. It scans your project, generates context-aware specialist agents, and runs a pipeline that turns a one-sentence intent into a reviewed, tested pull request.
Same codebase. Same issue. Same model. Different system.
Every team has access to the same AI tools. The difference between 10% utilisation and 10× productivity isn't the model — it's whether there's a system between the human and the tool.
Most AI-assisted work today looks like this: a person types a long prompt, gets output, manually reviews it, finds problems, types another prompt, gets more output, manually reviews again. Each interaction starts from zero. There's no accumulated context, no quality gates, no specialist review. The human is the entire workflow.
Requires Claude Code. Currently works best with coding projects. Marketing and legal domains are experimental.
The framework doesn't start with a task. It starts with deep context acquisition. A setup process scans your environment — codebase, content library, document corpus, project history — and generates the specialist agents, skills, rules, and quality gates tailored to your domain. This isn't a generic workflow. It's an expert system built from your actual work.
The system reads your codebase, content library, or document corpus. It identifies patterns, conventions, tools, and standards already in use.
Domain-specific agents are created automatically: security reviewer, brand compliance, legal risk assessor, architecture critic — whatever your domain requires.
Your conventions, quality criteria, and institutional knowledge become encoded rules that every future task is evaluated against. The system knows what "good" looks like here.
This is what separates a system from a tool. After setup, the AI isn't a general-purpose assistant — it's a domain expert that understands your specific codebase, your brand voice, your legal framework, your architectural patterns. Every subsequent task benefits from this accumulated context.
The framework decomposes all structured work into the same skeleton. The human provides intent and approval. The system handles decomposition, specialist review, execution, verification, and delivery.
Each stage is cheaper to fail at than the next one. Catching a bad assumption in the design session costs zero. Catching it during execution costs rework. Catching it after delivery costs reputation. The framework front-loads review and pushes errors left — the same principle behind TDD, design reviews, and every mature engineering process.
The pattern is universal — the implementation is specific. Below are three domain implementations showing how the same five-phase structure adapts to completely different types of work. The verbs change, the agents change, the outputs change — but the architecture is identical.
The primary domain — battle-tested across real projects. In one A/B comparison, structured-workflows produced a PR with -1,319 net lines and CI passing, where ad-hoc prompting added +2,452 net lines and failed CI.
SETUP: /setup scans codebase → generates specialist agents (security reviewer, architecture critic, test strategist), project-specific skills synthesised from your actual conventions, quality gates, and an @import hub wiring everything into Claude Code's context.
Same pipeline, different specialists. The template generates domain-specific agents but hasn't been battle-tested on production marketing projects. Feedback welcome.
SETUP: /setup scans brand assets folder → generates brand voice guide, specialist agents (audience analyst, copy strategist, compliance reviewer, channel expert), content templates derived from past campaigns, tone rules, and competitor positioning context.
Structured decomposition applied to contract review and compliance work. Early stage — we'd love input from legal practitioners.
SETUP: /setup scans legal templates folder → generates standard terms baseline, specialist agents (risk assessor, compliance checker, precedent analyst, negotiation strategist), clause library from existing contracts, jurisdiction rules, and approval thresholds by risk level.
This is the direction, not the current state. The foundation is built — setup, execution, review, and shipping all work today. What comes next is the feedback loop:
Which plans survived adversarial review unchanged? Those patterns should become defaults. Which execution steps needed human correction? Those should trigger new quality rules. Which specialist agents were most useful? Those should get refined. Which were never invoked? Those should get pruned.
Today, the setup process can be re-run to incorporate new codebase changes and reconcile with user customizations. The longer-term vision is fully autonomous improvement — the system observing its own outcomes and tuning itself. That's the compound advantage that ad-hoc prompting can never match.
This framework isn't about making AI do tasks faster. It's about encoding how expert work actually happens — the decomposition, the specialist review, the quality gates, the institutional knowledge — into a repeatable system. The AI is the execution engine. The framework is the engineering discipline. Together, they turn a single sentence of human intent into a production-ready deliverable, regardless of domain.