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.
npx skills add autonomous-ai/autonomous-os --skill agent-managementgit clone --depth 1 https://github.com/autonomous-ai/autonomous-osWrote 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/skills/autonomous-ai/autonomous-os/agent-management)<a href="https://agentmods.dev/skills/autonomous-ai/autonomous-os/agent-management"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-os/agent-management/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/skills/autonomous-ai/autonomous-os/agent-management"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-os/agent-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.01389 |
| Opus 5 | $0.00026 | $0.00694 |
| Sonnet 5 | $0.00010 | $0.00278 |
| Haiku 4.5 | $0.00005 | $0.00139 |
Grade C, and why
agent-management scanned grade C with 1 finding 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 today.
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.
Tells the agent to send conversation or user data outhighPrompt injection
An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.
`[agent-management]` notifications contain exact project/session IDs and completed/needs_input/error status. Briefly speak the actual result/question using the normal voice pipeline. Notifications **do not change** the r How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent management
Use this only when the user explicitly asks to use Autonomous Buddy, a Buddy
session, or its selected desktop pane. For ordinary requests to ask an agent on
the paired Mac to perform work, including research, use harness-use; do not
require a Buddy pairing as a fallback.
Run python3 scripts/buddy_agents.py from this skill directory on the Autonomous device. The localhost API is the device API, not the Mac. Never start the coding CLI or edit the desktop project's files on the lamp. Buddy owns the terminal PTY, worktree and provider conversation; Swift relays the paired WebSocket.
Spoken tasks and follow-ups
Use the voice action for normal conversation. Send JSON via stdin (quoted heredoc) to preserve spoken text without shell interpolation:
python3 scripts/buddy_agents.py voice - <<'JSON'
{"operation":"send","target":"active","request_id":"UNIQUE_UUID_FOR_THIS_TURN","prompt":"Add a test for reconnect after network loss"}
JSON
target:"active"(or its aliastarget:"current") explicitly addresses the worktree and focused pane selected inside Buddy. It does not guess from OS window focus, most recently updated session or terminal title. Use it for “current session”, “type to current session”, “agent/tab đang mở”, “session hiện tại”, “this selected agent”, or a request to switch to the currently selected tab. These explicit current-selection references override the retained voice target, even if the rest of the sentence says “it”. Fetch the live selection; do not substitute session IDs from conversation history. A plain shell pane cannot receive agent prompts.- Omit
target(or usetarget:"previous") only for follow-ups such as “thêm test nữa” or “ask the same agent to continue”, without a current/selected-tab reference: the helper retains the last voice session even if desktop tab focus changes. On the first voice request with no retained context it uses Buddy's selected pane. A missing/closed/stale target is an error, never permission to choose another agent. - For an explicit project/session, call
listand use the returnedproject_idandsession_id. Named selectorsprojectandworktreematch exact returned names, IDs, branch names or paths; ambiguous matches return an error. Ask only which target is meant, then use those exact selectors. Do not invent Mac paths or IDs. - To create a session, use
new_session:trueplusprovider:"codex"or"claude", and the requested target worktree. Example:{"operation":"send","target":"active","new_session":true,"provider":"codex","request_id":"UUID","prompt":"Fix reconnect"}creates in the selected worktree, including a feature worktree. Only create when the user asks to start a task/session; never create merely because a follow-up target is unavailable. If the provider is unspecified, ask which available agent to use. operation:"select"retains an explicitly chosen session without sending a prompt.operation:"status"returns the target's session/events.operation:"stop"stops that target; it does not roll back edits.conversation_iddefaults tovoicefor the device's single spoken conversation. For a separate chat channel use its stable conversation identifier; do not share a voice target across unrelated chats or invent a new conversation ID on every turn. If a different speaker's target is uncertain, select explicitly.
What ships with it
4 files beside SKILL.md 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.
- today Changed · +5 lines · -18 tokens per session f94cf169c88c
- yesterday First seen · 44 lines · 70 tokens per session scan C ed30dd2fff68
agent-management is a skill published in the GitHub repository autonomous-ai/autonomous-os (317 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 1,389 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent to send conversation or user data out). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-08.
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