Official AgiFlow plugin for AI coding clients. Drive AgiFlow project management — planning, grooming, execution, and review — directly from your AI coding tool.
Works with Claude Code, Codex, Cursor, Antigravity, and Gemini CLI.
This repo is a self-contained, multi-client plugin bundle. Until it is published to each client's marketplace, load it as a local plugin directory.
git clone <your-remote>/agiflow-ai-plugin
claude --plugin-dir ./agiflow-ai-pluginThe bundled .mcp.json wires the AgiFlow MCP server automatically. Use /mcp inside Claude Code to
check the connection.
Place the plugin folder in one of Antigravity's plugin locations, then restart:
# Workspace-level (this project only)
mkdir -p .agents/plugins && cp -R /path/to/agiflow-ai-plugin .agents/plugins/
# Global (all workspaces)
mkdir -p ~/.gemini/config/plugins && cp -R /path/to/agiflow-ai-plugin ~/.gemini/config/plugins/Antigravity reads the root plugin.json marker, the skills/, and mcp_config.json automatically.
Add manually in Cursor Settings → MCP / Plugins, pointing at this folder. Cursor's stable surface
is MCP config — the bundled .mcp.json provides it.
Load the plugin directory so Codex picks up .codex-plugin/plugin.json:
codex --plugin-dir ./agiflow-ai-pluginOnce published to a marketplace: codex plugin marketplace add <your-remote>/agiflow-ai-plugin.
gemini extensions install <your-remote>/agiflow-ai-pluginThe bundled gemini-extension.json connects the AgiFlow MCP server via mcp-remote.
git clone <your-remote>/agiflow-ai-plugin
claude --plugin-dir ./agiflow-ai-plugin- Add new workflow instructions under
skills/<name>/SKILL.md. - Keep shared guidance in
references/(e.g.references/agiflow-agents.md). - See
references/plugin-types.mdfor per-client manifest notes.
This plugin connects to the AgiFlow MCP server (https://agiflow.io/api/v1/mcp) and exposes AgiFlow
tools across these categories:
- Projects — create, inspect, and update projects and their statuses
- Tasks — create, list, get, update, reorder, and batch-create tasks
- Work units — group tasks into deliverable features/epics and track progress
- Workflows — acquire/release locks and coordinate multi-agent runs
- Members — list and assign agent members to work
- Comments — document decisions and progress on tasks
- Vault — read and set scoped configuration entries
The plugin ships 10 workflow skills that mirror AgiFlow's scrum pipeline. Your AI client loads them on demand when your request matches their description — you generally don't invoke them by name:
| Skill | Phase | Use it to… |
|---|---|---|
getting-started |
orient | get coached on where to start and which workflow fits |
project-plan |
Planning | break requirements into vertical-slice tasks (Planning status) |
refine-task |
Planning | turn a vague task into an autonomous-ready spec |
backlog-grooming |
Planning → Todo | verify, prioritize, and promote tasks into work units |
run-work |
Todo → Done | execute a whole work unit end-to-end in one session |
run-task |
Todo → Done | execute a single task through to Review |
review-work |
Review | verify acceptance criteria and file follow-ups |
triage |
diagnose | classify project issues by severity and recommend actions |
daily-standup |
report | a read-only pulse of done / in-progress / blocked / next |
orchestrate |
dispatch | route the highest-priority ready work to agents |
Shared guidelines (status model, transitions, tags, work-unit sizing) live in
references/agiflow-agents.md.
> Plan a feature: add per-user notification preferences
> Groom the backlog and promote the ready tasks to Todo
> Run task DXX-2
> Execute the checkout work unit end-to-end
> Review the auth work unit against its acceptance criteria
> Give me a daily standup for this project
> Why is this project stuck?
> What should an agent pick up next?
For a self-hosted AgiFlow instance, point the MCP wiring at your endpoint via the
AGIFLOW_AI_PLUGIN_MCP_URL environment variable (consumed by gemini-extension.json):
export AGIFLOW_AI_PLUGIN_MCP_URL="https://mcp.your-agiflow-instance.com/api/v1/mcp"For other clients, edit the server URL in .mcp.json, mcp.json, and mcp_config.json.
- AgiFlow: https://agiflow.io
- Plugin client compatibility:
references/plugin-types.md
MIT