Agent os

Skill percymcn/agent-cookbook/skills/autonomous-ai-agents/agent-os

Production-ready AI agent skills, playbooks, and workflows from the pharma6 automation lab

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npx -y skills add percymcn/agent-cookbook --skill agent-os

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Agent Operating System — Hermes is the command hub on Telegram. You talk to Hermes, Hermes orchestrates subagents (Felix/Claude Code), and results auto-save to memory. Built-in voice intent detection and cron job memory feedback.

SKILL.md

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Agent OS — The Command Hub

Architecture

You (Telegram voice/text)
    ↓
[1] Hermes receives — voice transcribed via faster-whisper / text direct
    ↓
[2] Intent detection — research | deploy | code | browse | cron | memory | question
    ↓
[3] Delegate or execute — delegate_task to subagents, or run cron jobs, or answer directly
    ↓
[4] Auto feedback — every result logs to ~/.hermes/agent-os/memory_log.jsonl
    ↓
[5] Dashboard updates — ~/.hermes/agent-os/dashboard.json always reflects latest state

Core Scripts

Feedback Loop

~/.hermes/scripts/agent-os-feedback.py

Usage:

python3 ~/.hermes/scripts/agent-os-feedback.py \
  --type delegate \
  --task "task description" \
  --result "what happened" \
  [--save-skill "lesson-name"]

Voice Intent Detection

~/.hermes/scripts/voice-intent-pipeline.py

Usage:

echo "research the best AI voice agents" | python3 ~/.hermes/scripts/voice-intent-pipeline.py

Dashboard

~/.hermes/agent-os/dashboard.json — JSON summary: total_recorded, by_type, last_10
~/.hermes/agent-os/memory_log.jsonl — Full append-only log (auto-trimmed to last 200)
~/.hermes/skills/agent-os-learned/*.md — Reusable lessons saved automatically

User Interaction Signals

Purse issues commands with minimal words. The following signals mean execute immediately without confirmation or re-explanation:

Signal	Meaning
"yes"	Go ahead with the plan you just described
"1 2" / "1 2 3" / numbered list	Execute those numbered steps in order
"?"	What's the current state? Report concisely
voice message	Transcribe → classify intent → execute (see Voice Intent Pipeline)

When you receive one of these, do not ask for confirmation. Execute and report results.

Status / Handoff Prompts Are Not Approval

Short questions like "what is left", "where are we", "what now", "what's pending", or "what's left" are status/handoff prompts. They mean: inspect current state first, then report pending items, blockers, and the next highest-leverage action.

Do not treat these as approval to create, run, or modify cron jobs/agents. Only execute a new side-effecting action when the user gives an explicit approval signal ("yes", "do it", numbered choice) or directly asks for that action.

See references/status-handoff-questions.md for the session note and recommended inspection sequence.

When to Use This Skill

User sends a voice message — transcribe → detect intent → delegate
User says "remember this" — save to memory via the feedback script
User replies "yes" or a number list to your plan — execute immediately
Any task completes — call agent-os-feedback.py --type cron --task "..." --result "..."
Checking system state — read dashboard.json for a quick overview

Cron Job Integration

Every cron job script should call the feedback script on completion:

subprocess.run([sys.executable, FEEDBACK_SCRIPT, "--type", "cron", "--task", "...", "--result", "..."])

Intent Classifier Rules

Intent	Triggers	Action
research	"find out", "search", "research", "what is"	Web search + summarize
deploy	"deploy", "push live", "publish"	Deploy to Vercel/Gumroad/etc
code	"build", "fix", "debug", "create"	delegate_task to Claude Code
browse	"check site", "open url"	Browser automation via Felix
cron	"every day", "daily", "remind me"	Create cronjob
memory	"remember", "store"	Save to memory
question	"advice", "should I"	Direct answer from Hermes