Skip to content
This project is under active development and subject to breaking changes. See the changelog for release notes.

DevSquad Copilot

Ship at the pace of innovation with agents that keep delivery grounded in specs, traceable decisions, and production-ready quality.

Shipping at the pace of innovation means treating delivery as a loop, not a pipeline. The framework organizes work in four layers. Specs are sliced thin and revised as you learn; each layer produces artifacts that reduce rework downstream and stay amendable when implementation reveals new understanding.

Intent: Why

Capture the strategic motivation through structured envisioning. Questions surface customer pain points, goals, and business context before any code is written.

Specification: What

Feature and migration specs define scope, acceptance criteria, and constraints for the current slice. Written for stakeholders, these artifacts are the contract downstream work is measured against, and amendable when reality shifts.

Architecture Decisions: How

ADRs record how and why specific technical approaches were selected. Evaluated against ranked priorities, not generic pros/cons, so decisions stay traceable.

Delivery: When

Every increment is traceable and reviewable. When implementation reveals that a spec or ADR no longer matches reality, the framework amends the affected section and continues, instead of silently deviating.

Four concrete mechanisms separate DevSquad from “describe the whole system, then let the AI code it” tools. Each one keeps delivery reviewable and amendable slice by slice, so velocity holds up past the first few iterations.

Thin slices, amendable mid-flight

Specs are written at the slice level and amended when implementation reveals a model shift. The amendment seam is first-class, not a retrofit.

Spec Amendment

Decisions as ADRs, not code comments

Every non-trivial technical choice produces an ADR evaluated against ranked priorities. Reviewers and future agents read the reasoning, not just the code.

Decisions

Impact-scaled ceremony

Low-impact changes move fast. Medium and high changes trigger the checks they need: plan review, ADR, explicit approval. Ceremony matches risk.

Impact Classification

Comprehension checkpoints

After implementation, the framework checks that the developer understands what was generated and why. The loop optimizes for comprehension, not throughput.

Comprehension Checkpoints

A good fit if you...

  • Ship production code that other people depend on.
  • Want AI to accelerate delivery without dropping specs, ADRs, or review.
  • Work in a team where traceability and onboarding matter.
  • Are comfortable reviewing AI output at every impact-classified step.

Not a fit if you...

  • Want unsupervised end-to-end code generation from a prompt.
  • Are prototyping throwaway code where ceremony is pure overhead.
  • Prefer to treat AI as a code autocomplete without structured handoffs.
  • Expect the tool to pick architecture for you without developer input.

Pick the entry point that matches your role. Each path includes the essentials for that audience.

Architects

Understand the framework, decision records, and how technical design is structured.

Framework · Decisions · Agents

Find this framework useful?GitHub stars