# awslabs/aidlc-workflows

AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents

Repository: https://github.com/awslabs/aidlc-workflows
Canonical: https://ross.abutalabs.com/products/aidlc-workflows
Homepage: https://awslabs.github.io/aidlc-workflows/
Language: Python
License: MIT-0
License Family: permissive
Last push: 2026-08-26T23:02:32+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 90, longevity 20
- inputs: {"age_days": 293, "days_push": 7, "days_rel": 64, "gap_med": 11.5, "n_releases_24m": 11}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4187, forks 729 (observed 2026-08-28T04:08:38.451163+00:00)

## What it is
AI-DLC Workflows is AWS's implementation of the AI-Driven Development Life Cycle methodology, packaged as adaptive workflow steering rules, skills, agents, and hooks for AI coding agents. It renders a harness-neutral core into distributions for tools like Claude Code, Kiro, Cursor, Cline, GitHub Copilot, and OpenAI Codex.

## Use cases
- structure ai coding agent workflows with quality gates
- run an ai-driven software development lifecycle
- add steering rules to claude code or kiro
- manage requirements to code workflow with ai agents
- standardize team ai-assisted development process
- generate verifiable artifacts from ai coding sessions

## When to choose
- you use AI coding agents (Claude Code, Kiro, Cursor, Copilot, Codex) and want a structured, gated development methodology
- you want consistent, reviewable AI-generated development artifacts across a team
- you need workflow profiles for different work types like MVPs, bugfixes, refactors, or security patches

## When to avoid
- you want a standalone CI/CD or project management tool rather than rules for AI coding agents
- you prefer lightweight ad-hoc prompting without a formal lifecycle methodology
- your coding agent is not among the supported harnesses and you cannot port the rules

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, workflow-automation, developer-tools, code-review, documentation
- domain: developer-tools
- platform: cli, cross-platform, python
- tags: ai-coding-agents, software-development-lifecycle, workflow-rules, steering-files, claude-code, kiro, cursor, github-copilot, methodology, harness-neutral, ai-agents, automation

## Member repositories
- awslabs/aidlc-workflows (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:38.451163+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:22:37.264901+00:00, confidence not recorded.
  - readme: https://github.com/awslabs/aidlc-workflows (fetched 2026-08-28T04:08:38.451163+00:00, sha 4d4af30aa23b)
  - homepage: https://awslabs.github.io/aidlc-workflows/ (fetched 2026-08-29T09:13:01.840812+00:00, sha 9aac3ce301bc)
- Data as of 2026-08-30T08:39:29.467469+00:00.