agiflow-ai-plugin
v0.2.0AgiFlow AI plugin. Includes 10 skills and 1 MCP server.
By AgiFlowLicense: MIT1.1k GitHub starsUpdated 13 hours ago
Directory evidence
- Runtimes
- Codex
- Parsed components
- 11 skill or MCP entries
- Source updated
- Sep 23, 2026
- Manifest status
- Canonical path parsed
The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →
Install agiflow-ai-plugin for Codex
codex plugin marketplace add hashgraph-online/awesome-codex-plugins
codex plugin marketplace upgrade awesome-codex-plugins
codex plugin add agiflow-ai-plugin@awesome-codex-pluginsPaste and run these commands in a terminal with Codex. They add and refresh the awesome-codex-plugins catalog, then install this plugin.
Compatibility: the page URL and API slug “agiflow-ai-plugin” remain stable.
- Codex:
agiflow-ai-plugin@agent-plugin-marketplace→agiflow-ai-plugin@awesome-codex-plugins
The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.
Get the source manually
git clone https://github.com/hashgraph-online/awesome-codex-pluginsClone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/AgiFlow/ai-plugin/.
Plugin files
├── .codex-plugin/plugin.json├── skills/backlog-grooming/SKILL.md├── skills/daily-standup/SKILL.md├── skills/getting-started/SKILL.md├── skills/orchestrate/SKILL.md├── skills/project-plan/SKILL.md├── skills/refine-task/SKILL.md├── skills/review-work/SKILL.md├── skills/run-task/SKILL.md├── skills/run-work/SKILL.md├── skills/triage/SKILL.md└── .mcp.json
Included Skills10
Review Agiflow Planning tasks for readiness, prioritize approved work, group related tasks into work units, and promote ready tasks to Todo. Use when grooming a backlog, organizing planned tasks, creating work units, or deciding what should be executed next.
Produce a concise read-only Agiflow status summary covering completed work, work in progress, blockers, and recommended next priorities. Use for daily standups, morning checks, stakeholder updates, or a quick project pulse.
Assess an authenticated Agiflow workspace and recommend the right next project-management workflow. Use when a user is new to Agiflow, asks what to do next, needs orientation, or is unsure whether to plan, refine, groom, triage, or review daily status.
Pipeline orchestration: dispatch the highest-priority ready tasks/work units to agents, manage capacity, and coordinate the Todo to Done flow. Invoked as /agiflow:orchestrate. Uses list_tasks, list_active_tasks_by_org, list_members, update_task, get_work_unit_progress.
Turn a product goal or feature request into a clear Agiflow project plan with small, testable tasks in Planning status. Use when starting a project, decomposing a feature, clarifying requirements, or converting an idea into an actionable backlog.
Refine an existing Agiflow task into an unambiguous, testable specification without expanding its intended outcome. Use when a task is vague, lacks acceptance criteria, has unclear scope or dependencies, or is not ready for backlog grooming.
Quality gate: verify each acceptance criterion of a completed task/work unit, run quality checks, and create follow-up tasks for gaps. Use before merging or to audit delivered work. Invoked as /agiflow:review-work <work-unit-or-task>. Uses get_work_unit, get_task, update_task, create_task, create_task_comment.
Execute a single Todo task through In Progress to Review, meeting every acceptance criterion with tests and vibe-lint checks. Refuses Planning-status tasks. Invoked as /agiflow:run-task <task>. Uses get_task, update_task, create_task_comment.
Execute a work unit end-to-end: sequence tasks by dependency, implement, test between tasks, commit, and track progress. Use to deliver a complete feature in one session. Invoked as /agiflow:run-work <work-unit>. Uses get_work_unit, list_tasks, update_task, get_work_unit_progress.
Diagnose stalled, blocked, overloaded, or unhealthy Agiflow projects and recommend specific corrective actions. Use for project health checks, blocked work, conflicting priorities, obsolete tasks, overloaded assignees, or an unmanageable backlog.
MCP servers1
- url
- https://agiflow.io/api/v1/mcp
Plugin manifests1
{
"name": "agiflow-ai-plugin",
"version": "0.2.0",
"description": "AgiFlow AI plugin",
"repository": "https://github.com/AgiFlow/ai-plugin",
"author": {
"name": "AgiFlow"
},
"license": "MIT",
"keywords": [
"ai-plugin",
"codex",
"claude-code",
"cursor",
"gemini",
"mcp"
],
"skills": "./skills/",
"interface": {
"displayName": "AgiFlow",
"shortDescription": "Use AgiFlow tools in Codex",
"longDescription": "Access AgiFlow tools, skills, and MCP-backed workflows directly from Codex.",
"developerName": "AgiFlow",
"category": "Productivity",
"capabilities": [
"Read",
"Write"
],
"defaultPrompt": [
"Show me the key Agiflow workflows",
"Find open Agiflow issues",
"Help me use the Agiflow plugin"
],
"brandColor": "#F9BD2B",
"composerIcon": "./assets/icon.png",
"logo": "./assets/logo.svg"
},
"mcpServers": "./.mcp.json"
}For maintainers
If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.
[agiflow-ai-plugin on Agent Plugins Marketplace](https://pluginsmp.com/plugins/agiflow-ai-plugin)