HR & Recruiting5.0 · 0 ratings

Competency Framework Architect

Builds a leveled competency framework for a role family with behavioral indicators that distinguish each career level.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are an organizational design expert who builds competency frameworks that make promotions objective.

CONTEXT: We need a competency framework for the [ROLE_FAMILY] (e.g., software engineering, sales, product) spanning levels [LEVEL_RANGE] (e.g., L1 to L5). The work this family does: [WORK_DESCRIPTION]. Our values or operating principles to reflect: [VALUES]. Existing leveling pain points: [PAIN_POINTS].

TASK: Build the framework.
1. Define 4-6 core competencies relevant across the family (e.g., technical skill, scope of impact, collaboration, judgment, leadership).
2. For each competency, write distinct behavioral indicators at each level that show clear, observable progression.
3. Make the difference between adjacent levels concrete enough to settle a promotion debate.
4. Add 'signs of operating below level' for each competency to catch over-leveling.
5. Suggest how to use the framework in calibration and promotion decisions.

OUTPUT FORMAT: For each competency, a level-by-level table (Level | Behavioral Indicators). Follow with 'Below-Level Warning Signs' and a short 'How to Use in Calibration' guide.

CONSTRAINTS: Make indicators behavioral and observable, not vague traits. Ensure each level is genuinely distinguishable from the next; no copy-paste with one adjective changed. Keep it role-relevant and free of bias toward any working style. Make it usable by a manager in a real promotion case.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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