agiflow-ai-plugin
Access Agiflow tools directly from Gemini CLI.
Open source Open in the app JSON README (API)
About
Access Agiflow tools directly from Gemini CLI.
Details
- Kind
- Plugins
- Topic
- AI, RAG & memory
- Publisher
- agiflow
- Origin
- gemini
- Category
- ferramentas
- Version
- 0.2.0
- Stars
- 3
- Last push
- 2026-07-10T08:43:34Z
- Repository state
- ativo
- License
- MIT
- Added
- 2026-08-30 14:13:39
- Updated
- 2026-08-30 14:13:39
- Origin id
agiflow/ai-plugin
README
# AgiFlow AI Plugin Official AgiFlow plugin for AI clients. Drive AgiFlow project management, including planning, grooming, execution, and review, directly from your AI tool. Works with **ChatGPT**, **Claude Code**, **Codex**, **Cursor**, **Antigravity**, and **Gemini CLI**. ## Installation 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. ### Claude Code ```bash git clone <your-remote>/agiflow-ai-plugin claude --plugin-dir ./agiflow-ai-plugin ``` The bundled `.mcp.json` wires the AgiFlow MCP server automatically. Use `/mcp` inside Claude Code to check the connection. ### Antigravity (Google) Place the plugin folder in one of Antigravity's plugin locations, then restart: ```bash # 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. ### Cursor Add manually in **Cursor Settings → MCP / Plugins**, pointing at this folder. Cursor's stable surface is MCP config. The bundled `.mcp.json` provides it. ### Codex Add the AgiFlow plugin marketplace, then install the plugin from that marketplace: ```bash codex plugin marketplace add AgiFlow/ai-plugin codex plugin add agiflow-ai-plugin@agiflow ``` For local development, point Codex at this checkout as a marketplace root: ```bash codex plugin marketplace add ./agiflow-ai-plugin codex plugin add agiflow-ai-plugin@agiflow ``` ### Gemini CLI ```bash gemini extensions install <your-remote>/agiflow-ai-plugin ``` The bundled `gemini-extension.json` connects the AgiFlow MCP server via `mcp-remote`. ### ChatGPT OpenAI Platform plugins are submitted from the production AgiFlow MCP server. This public repository provides the reusable Agent Skills used by that plugin, but does not contain Platform dashboard IDs, submission evidence, reviewer credentials, or other private operational data. The ChatGPT-safe workflow set is: - `getting-started` - `project-plan` - `refine-task` - `backlog-grooming` - `daily-standup` - `triage` The coding-agent workflows `orchestrate`, `run-task`, `run-work`, and `review-work` remain available to coding clients but are not part of the ChatGPT submission bundle. ## How to develop ```bash 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.md` for per-client manifest notes. ## Features 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 or epics and track progress - **Workflows**: acquire and 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 ### Bundled skills 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 do not 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`](references/agiflow-agents.md). ## Example usage ``` > 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? ``` ## Self-hosted 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`): ```bash 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`. ## Documentation - AgiFlow: https://agiflow.io - Plugin client compatibility: [`references/plugin-types.md`](references/plugin-types.md) ## License MIT