Clarifies user jobs-to-be-done to align products, content, and AI prompts with real progress.
npx skills add https://github.com/dontbesilent2025/dbskill --skill dbs-jtbd
Please help me install this Agent Skill. Skill: dbs-jtbd Source: https://github.com/dontbesilent2025/dbskill/tree/main/skills/dbs-jtbd Command: npx skills add https://github.com/dontbesilent2025/dbskill --skill dbs-jtbd
Users often describe solutions ("write me an article", "build me a course") instead of the underlying progress they want, leading to misaligned products, content, and AI outputs. This Skill applies the Jobs to Be Done framework to uncover the real task behind a request, the forces driving switching decisions, and the observable criteria users use to judge solutions.
Ask the agent to use /dbs-jtbd to analyze why users choose a particular solution and rewrite your request as a JTBD-based task statement.
High-intent search queries and answers about installing and using this skill.
Start from the user's situation, identify the progress they want to make, and define the outcome they expect, using the format: when in situation X, I want progress Y, so that outcome Z. Avoid treating stated solutions or demographics as the task itself.
Place the task statement at the start of the prompt, then add your situation, available materials, boundaries, expected deliverable, and three checkable acceptance criteria. This lets the AI infer solutions from constraints instead of guessing from abstract labels.
The four forces are push (pain from the current solution), pull (promise of the new solution), anxiety (fear of switching costs or failure), and habit (attachment to the old way). Adoption requires push and pull to outweigh anxiety and habit.
JTBD is not a substitute for user personas, feature lists, or a universal explanation of behavior. It only explains choices and progress within specific situations, and should not be used to fabricate interview data or reduce every task to money or efficiency.
Yes. When materials are sufficient, the Skill forms a task hypothesis immediately rather than interrogating the user. It asks at most one minimal factual question, and clearly labels conclusions as assumptions pending verification when evidence is missing.
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