agentinel
GitHubAgentinel 是本地资源监控 CLI,用于分析 AI 代理进程的资源占用、泄漏及僵尸会话。触发于用户询问内存/CPU/磁盘消耗或清理建议时。提供只读扫描与建议,不执行删除操作。
Trigger Scenarios
Install
npx skills add 0x0funky/Agentinel --skill agentinel -g -y
SKILL.md
Frontmatter
{
"name": "agentinel",
"description": "Inspect this machine's live resource usage via the Agentinel CLI — find runaway \/ zombie \/ leaking AI-agent processes (Claude Code, Codex, Gemini, OpenCode, MCP & dev servers, Ollama…), duplicate sessions, heavy memory users, and reclaimable disk caches. Use when the user asks what's eating RAM\/CPU\/disk, whether they have forgotten or leaking agent sessions, which agents are still running, or wants a cleanup recommendation. Read-only — it never kills or deletes."
}
Agentinel — local resource sentinel (CLI)
Agentinel ships a zero-overhead CLI (~12 MB, no WebView) that snapshots every process on the machine, classifies AI-agent CLIs, and flags leaks / zombies / duplicate sessions / heavy disk. Use it to answer "what's going on with my machine right now" without guessing.
When to use
Trigger when the user asks things like:
- "What's eating my RAM / CPU?" · "為什麼這麼吃記憶體?" · "什么最吃内存?"
- "Do I have any zombie or forgotten agent sessions?" · "有沒有忘了關的 agent?"
- "Which Claude / Codex sessions are still running?"
- "Is anything leaking?" · "What can I clean up on disk?" · "磁碟可以清什麼?"
How to run
Run from the repo root (or anywhere agent_monitor/ is importable). The wrapper
./agentinel auto-finds a sibling .venv; otherwise use python -m agent_monitor.
Prefer --json for parsing, fall back to the text table for display.
agentinel scan --json # whole-system snapshot (parse this)
agentinel scan --agents-only --json # AI agents only
agentinel scan --top 50 # text table, top 50
agentinel advise # local AI CLI writes markdown advice
agentinel advise --engine codex # pick the engine (claude|codex|gemini|opencode)
agentinel doctor # which AI CLIs are installed + fallback order
# (equivalently: python -m agent_monitor <subcommand> …)
What scan --json returns
{ system, processes[], findings[] }:
- system:
ram_used_gb,ram_total_gb,ram_pct,cpu_pct,swap_used_gb,swap_total_gb - processes[]:
pid,label,category,project,mem_mb,cpu,age_hr,idle_min,parent_name,parent_alive,ports[],children,cmdline - findings[]: rule-engine anomalies —
{ severity, title, detail, pids, action }
How to respond
- Summarize the 1–2 things that matter most (biggest RAM users, suspected leaks, idle zombies, duplicate sessions in the same project).
- Cite concrete evidence: memory size/growth,
idle_min,parent_alive=false, boundports, child count. - Recommend keep / end / watch for each — but do not kill or delete anything.
Safety
scan/advise/doctorare read-only. There is no CLI kill or cleanup — do not invent one. Destructive actions (kill / suspend / disk cleanup) live only in the GUI's confirm-to-run flow (python agentinel.py). If the user wants to act, point them there, or ask for explicit confirmation before running anything destructive.
Version History
- 3b9fc86 Current 2026-07-05 15:01


