apex-star-story-blueprints
Purpose
This skill creates structured STAR (Situation, Task, Action, Result) blueprints to help the candidate articulate compelling examples that align with the most important role requirements.
Expert lens (apply internally; do not print)
When generating this output, apply the three-expert perspective defined in the orchestrator:
- •UN Hiring Manager: Is the content framed to pass competency-based screening?
- •Technical Specialist: Does terminology align with the role's domain and UN-style frameworks?
- •ATS Analyst: Are keywords integrated naturally for system parsing?
Prioritize (1) factual grounding, (2) role alignment, (3) screening resilience.
Inputs
Required:
- •
USER_JOB_HISTORY_TEXT: to source real examples. - •
JOB_DESCRIPTION_TEXT: to identify the requirements.
Optional:
- •
TERM_EXTRACTOR: to prioritize high‑starred requirements. - •
apex-candidate-evidence-bankoutput: for quick evidence lookup.
Output format
For each selected requirement, output:
- •Targeted requirement: the specific competency or requirement, including its star weight if known.
- •Relevant experience selected: identify the role or project from the user’s history to base the story on.
- •STAR blueprint: a breakdown with headings:
- •Situation:
- •Task:
- •Action:
- •Result:
- •Tailoring note: a one‑sentence suggestion on how to align the language and tone with the organization’s mission or context.
Create 3–4 such blueprints.
Example (for pattern reference; do not copy verbatim)
- •Targeted requirement: Results-Based Management
- •Relevant experience selected: Programme Manager, [Org], [Country]
- •STAR blueprint:
- •Situation: The programme lacked a coherent M&E framework, causing reporting delays across [N] field offices.
- •Task: Design and deploy a unified results framework within [timeframe].
- •Action: Developed a logic model aligned to [Framework], trained [N] staff on indicator protocols, and integrated real-time dashboards using [Tool].
- •Result: Reduced reporting turnaround by [X]%, achieved [Y]% data completeness, and received commendation from [Donor].
- •Tailoring note: Emphasize alignment with the organization's commitment to evidence-based programming.
Selection rules
- •Choose requirements that are emphasized in the job description or have high star ratings (★★★ or above) in the term extractor.
- •Only select requirements for which there is clear evidence in the user's history. Use placeholders for missing metrics.
- •Prefer examples where the user's impact is evidenced by metrics or significant scope (e.g., budget managed, people served, percentage improvements). These make the strongest STAR stories.
Steps
- •Identify 3–4 critical requirements to highlight.
- •Match each requirement to a compelling experience from the candidate’s history.
- •Draft the STAR blueprint, ensuring each element clearly describes the scenario, what was done and what was achieved.
- •Add a tailoring note per blueprint.