Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/AgiFlow/ai-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/plugins/agiflow/ai-plugin/gemini-extension)<a href="https://agentmods.dev/plugins/agiflow/ai-plugin/gemini-extension"><img src="https://agentmods.dev/badge/plugins/agiflow/ai-plugin/gemini-extension/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/plugins/agiflow/ai-plugin/gemini-extension"><img src="https://agentmods.dev/badge/plugins/agiflow/ai-plugin/gemini-extension.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
agiflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
{
"name": "agiflow",
"version": "0.3.1",
"description": "Access Agiflow tools directly from Gemini CLI.",
"mcpServers": {
"agiflow-ai-plugin": {
"command": "sh",
"args": [
"-c",
"exec npx mcp-remote@latest \"${AGIFLOW_AI_PLUGIN_MCP_URL:-https://agiflow.io/api/v1/mcp-app}\" --header \"x-agiflow-mcp-consumer:plugin\""
]
}
},
"contextFileName": "GEMINI.md",
"settings": [
{
"name": "Agiflow MCP URL",
"description": "Agiflow MCP endpoint URL.",
"envVar": "AGIFLOW_AI_PLUGIN_MCP_URL"
}
]
}
What ships with it
4 files beside gemini-extension.json in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 23 lines scan A 856548b8e489
agiflow is a plugin published in the GitHub repository AgiFlow/ai-plugin (4 stars, last pushed 6d ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-11.
Other plugins, from other repositories
before-you-build
Pre-build product risk review for founders, product teams, and AI-assisted builders before implementation starts.
obw
Obsidian Workspace — project-scoped vault productivity (capture / notes / PM). Skills own folder layout and templates; /obw:pm splits a spec into blocking tickets; /issue is a Claude Mod pane that lists a view of the project's dashboard, draws Mermaid as text diagrams, and can open a card in the browser through viz.…
feature-to-pr-factory
Turn one feature into dependency-ordered GitHub or GitLab issues, then build each ticket into its own reviewed, tested PR or MR with agents.
mission-control
SDLC management for Operations, Asgard, and CAMPPS: prepared issue drafts, live schema sync, board schemas, labels, project fields, metrics, and card validation.
feishu-calendar
Manage Feishu (Lark) calendar events — create, read, update, delete events and manage attendees via the Feishu Open Platform Calendar API v4.
gse
GSE-One — AI engineering companion for structured SDLC management. 24 commands, adaptive risk analysis, unified backlog, knowledge transfer, worktree isolation.