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 binary-husky/AlphaAutoResearch --skill regular_taskgit clone --depth 1 https://github.com/binary-husky/AlphaAutoResearchWrote 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/binary-husky/alphaautoresearch/regular_task)<a href="https://agentmods.dev/skills/binary-husky/alphaautoresearch/regular_task"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/regular_task/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/binary-husky/alphaautoresearch/regular_task"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/regular_task.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 | $0.00000 | $0.01989 |
| Opus 5.5 | $0.00000 | $0.00796 |
| Sonnet 5.5 | $0.00000 | $0.00398 |
| Haiku 4.5 | $0.00000 | $0.00199 |
Grade A, and why
regular_task scanned grade A 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 2mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
r = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
- Do Not Terminate Without Careful Consideration
- Run important commands in tmux sessions, and monitor them with
tmux_wait.pyto detect errors early and save time. - Always use
python ./tmux_wait.pyinstead ofsleepto wait before checking tmux content, so that you can detect errors early and avoid wasting time. - tmux session names must use prefix
ajet_worker_*
Service Monitoring Skill
---
name: monitor-with-tmux
description: Monitor training progress by reading tmux content at exponential backoff intervals (30s, 1min, 2min, 4min, 8min, 16min), analyze logs when anomalies occur, and provide fix suggestions
license: Complete terms in LICENSE.txt
---
# Monitor with Tmux
Monitor in tmux, detect anomalies, analyze errors, provide fix suggestions.
## Step Zero
Create a sleep script for tmux monitoring:
1. Create `./tmux_wait.py`
```python
import argparse
import subprocess
import time
SHELLS = {"bash", "zsh", "sh", "fish", "csh", "tcsh", "ksh", "dash", "ash"}
def smart_sleep(session: str, seconds: float, check_every: float = 2.0) -> bool:
end_time = time.time() + seconds
while time.time() < end_time:
try:
r = subprocess.run(
["tmux", "list-panes", "-F", "#{pane_current_command}", "-t", session],
capture_output=True, text=True, timeout=5
)
if r.returncode != 0:
return False
cmds = [l.strip().lower() for l in r.stdout.splitlines() if l.strip()]
if not any(c not in SHELLS for c in cmds):
return False
except Exception:
return False
time.sleep(min(check_every, end_time - time.time()))
return True
def print_tmux_window(session: str, lines: int = 100):
try:
r = subprocess.run(
["tmux", "capture-pane", "-p", "-t", session],
capture_output=True, text=True, timeout=5
)
if r.returncode == 0:
output_lines = r.stdout.splitlines()
print("\n\n--- tmux pane output (last {} lines) ---".format(lines))
print("\n".join(output_lines[-lines:]))
print("--- tmux pane output ends ---\n\n")
except Exception as e:
print(f"Failed to capture tmux pane: {e}")
def main():
parser = argparse.ArgumentParser(description="Wait for a tmux session with smart early-exit.")
parser.add_argument("session", help="tmux session name")
parser.add_argument("seconds", type=float, help="total seconds to wait")
args = parser.parse_args()
timed_out = smart_sleep(args.session, args.seconds, 2)
print_tmux_window(args.session, 100)
raise SystemExit(0 if timed_out else 1)
if __name__ == "__main__":
main()
```
## Begin Monitoring
When you need to monitor a tmux window, run:
```bash
python ./tmux_wait.py my_ajet_session_name 30
```
This means:
1. Monitor the tmux session named my_ajet_session_name
2. Wait for 30 seconds
- Exit code 0: Normal timeout (command is still running)
- Exit code 1: Command finished early or session disappeared
## Using SSH
When using SSH, always use a local tmux window to establish the SSH connection.
## When You Want to Delay Before Reading tmux Again
You must have early-return-on-error capability. Do not use `sleep xxx`; instead use `python ./tmux_wait.py my_ajet_session_name xxx`
DO NOT USE: `sleep 60 && tmux capture-pane -t my_ajet_session_name -p | tail -80`
YOU SHOULD USE: `python ./tmux_wait.py my_ajet_session_name 30 && tmux capture-pane -t my_ajet_session_name -p | tail -80`
- 60 seconds is too long
- Always use `python ./tmux_wait.py` to wait
## Examples:
### Without SSH
0 examples available
### With SSH
1 example available
```agent
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.
- 2mo ago First seen · 209 lines · 0 tokens per session scan A 3313234b4d74
regular_task is a skill published in the GitHub repository binary-husky/AlphaAutoResearch (11 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,989 tokens. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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