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Th0rgal

long-running-tasks

@Th0rgal/long-running-tasks
Th0rgal
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12 forks
Updated 4/1/2026
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Handle tasks that may exceed tool timeouts (model training, large builds, data processing).

Installation

$npx agent-skills-cli install @Th0rgal/long-running-tasks
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Details

Pathskill/long-running-tasks/SKILL.md
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Scoped Name@Th0rgal/long-running-tasks

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

npx agent-skills-cli list

Skill Instructions


name: long-running-tasks description: Handle tasks that may exceed tool timeouts (model training, large builds, data processing).

Long-Running Tasks

Use when

  • Running tasks that may take more than 10 minutes (model quantization, training, large builds)
  • Starting processes that should continue even if the connection drops
  • Monitoring background jobs and reporting progress

Don't use when

  • The task completes quickly and fits within the normal tool timeout window.
  • The task must be interactive (requires continuous prompts or TTY input).

Outputs

  • Log files (e.g., task.log, build.log) should be saved in artifacts/ when possible.
  • Final outputs should be written to artifacts/ for easy retrieval.

Templates or Examples

  • Use the command patterns in the “Commands” and “Examples” sections as templates.

Strategy

For any task that might exceed the Bash tool timeout (10 minutes):

  1. Run in background with output logging
  2. Return immediately to confirm the task started
  3. Check periodically and report progress
  4. Confirm completion when done

Commands

Start a long-running task

# Use nohup to persist after session ends, redirect all output to log
nohup <command> > task.log 2>&1 &
echo "Task started with PID $!"

Check if task is still running

# Check by process name
pgrep -f "<command_pattern>" && echo "Still running" || echo "Completed"

# Or check the log for completion indicators
tail -20 task.log

Monitor progress

# Watch log file for updates
tail -f task.log  # (use with timeout or Ctrl+C)

# Or get last N lines
tail -50 task.log

Examples

Model Quantization

# Start quantization in background
nohup python quantize.py --model GLM-4.7-flash --format NVFP4 > quantize.log 2>&1 &
echo "Quantization started. Check quantize.log for progress."

Then periodically:

tail -30 quantize.log
pgrep -f "quantize.py" && echo "Still running..." || echo "Process completed!"

Large Build

# Start build in background
nohup cargo build --release > build.log 2>&1 &
echo "Build started with PID $!"

Check progress:

tail -20 build.log

Data Processing Pipeline

# Start pipeline
nohup ./process_data.sh input/ output/ > pipeline.log 2>&1 &

# Check progress (if script outputs progress)
grep -E "Progress|Completed|Error" pipeline.log | tail -10

Best Practices

  1. Always use nohup - Ensures task survives if connection drops
  2. Redirect both stdout and stderr - Use > file.log 2>&1
  3. Save the PID - echo $! right after starting
  4. Check periodically - Every 5-10 minutes for long tasks
  5. Look for completion markers - grep for "done", "error", "completed"
  6. Clean up - Remove log files after confirming success

Reporting to User

When starting a long task, tell the user:

  • What command was started
  • Where logs are saved
  • How to check progress manually
  • Estimated completion time (if known)

When checking progress, report:

  • Current status (running/completed/failed)
  • Recent log output (last 10-20 lines)
  • Any errors or warnings seen

When task completes:

  • Confirm success or failure
  • Summarize results
  • Clean up temporary files if appropriate