Skill Detail

Govern coding-agent discovery and delivery through a project-local GAAI backlog

Drop a `.gaai/` folder into a project so agents separate scope discovery from isolated delivery, with acceptance criteria, memory, QA gates, and daemonized execution.

Templates & WorkflowsMulti-Framework
Templates & Workflows Multi-Framework Security Reviewed
⭐ 152 GitHub stars
COPY SKILL INSTRUCTIONS (OPTIONAL)
npx skills add agentskillexchange/skills --skill govern-coding-agent-discovery-and-delivery-through-a-project-local-gaai-backlog Copy
Uses the third-party skills CLI, not an ASE-owned installer. Check your agent’s compatibility. This copies instructions; complete the upstream tool setup below separately.
At a glance
Tools required
AI coding tool, git, bash, python3; Claude Code CLI and tmux for autonomous delivery daemon
Install & setup
Copy the repository's .gaai/ folder into the target project or run the documented installer, then read .gaai/core/README.md, bootstrap the project, use /gaai-discover to create scoped stories, and use /gaai-deliver or /gaai-daemon for governed delivery.
Author
Fr-e-d
Publisher
Individual
Last updated
Jul 21, 2026
Quick brief

Use GAAI when a team wants coding agents to turn product intent into scoped stories first, then deliver only from a governed backlog with acceptance criteria and QA checks. The operator runs Discovery to clarify and write backlog stories, then runs Delivery or the daemon to execute stories in isolated agent sessions with planning, implementation, and QA roles. Invoke it instead of normal ad hoc coding-agent prompting when scope drift, forgotten decisions, or unverifiable acceptance criteria are recurring problems. The scope boundary is project-local governance for coding-agent discovery and delivery; it is not a general agent framework, SDK, or hosted workflow platform. Autonomous daemon delivery currently depends on Claude Code CLI, while discovery and governance are documented as portable across coding tools.

How it works

What this skill actually does

Inputs and prerequisites: AI coding tool, git, bash, python3; Claude Code CLI and tmux for autonomous delivery daemon.

Setup notes: Copy the repository’s .gaai/ folder into the target project or run the documented installer, then read .gaai/core/README.md , bootstrap the project, use /gaai-discover to create scoped stories, and use /gaai-deliver or /gaai-daemon for governed delivery.

Source and verification boundary: use https://github.com/Fr-e-d/GAAI-framework as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.

Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.