Update tasks.json with the current commit hash to mark task success.
npx skills add https://github.com/logic-math/rick --skill mark-task-success
Please help me install this Agent Skill. Skill: mark-task-success Source: https://github.com/logic-math/rick/tree/main/.rick/skills/mark_task_success_skill Command: npx skills add https://github.com/logic-math/rick --skill mark-task-success
This Skill streamlines the process of marking task success in AI coding workflows, reducing manual steps and ensuring accurate tracking of task completion.
After completing a task, mark it as successful with the command: python3 .rick/skills/mark_task_success_skill/mark_task_success.py --job job_N --task taskX
High-intent search queries and answers about installing and using this skill.
Automated task tracking in AI coding workflows is achieved by running a Python script that updates tasks.json with the current commit hash. This eliminates manual steps and ensures accurate tracking of task completion.
Commit hash tracking records the specific code commit for each task by updating the tasks.json file. This provides a clear, automated trail of changes directly linking task success to code history in the Rick CLI framework.
Yes, you need Python installed to execute the marking script. The workflow also assumes the presence of a .rick/ directory and a tasks.json file to properly parse and update task completion records.
The best way to mark a task completed is by running the Python script with specific arguments: python3 .rick/skills/mark_task_success_skill/mark_task_success.py --job job_N --task taskX.
Automated task marking reduces manual steps and prevents human error by directly linking task success to the commit hash. This ensures accurate tracking of task completion and a reliable history of changes.
This task marking is designed for AI coding workflows that require tracking task completion and commit history. It is ideal for teams using AI coding tools that previously required manual marking of task completion.
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