菜单
Add or improve concrete example stories, prompt variants, weak and strong answer variants, and follow-up angles for behavioral interview specs in this repository. Use when a user asks for more concrete examples inside each interview, more variants by role or level, or better example coverage.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | interview-examples-variants |
| description | Add or improve concrete example stories, prompt variants, weak and strong answer variants, and follow-up angles for behavioral interview specs in this repository. Use when a user asks for more concrete examples inside each interview, more variants by role or level, or better example coverage. |
interview.json, especially prompt, scenario, storyAnatomy, exampleStories, followUps, and evaluation.Each exampleStories item should include:
id: stable hyphen-case id.title: concrete situation title.level: expected role or level, such as Director, VP Engineering, or CTO.promptVariant: the prompt wording this story answers.situation: specific context, stakes, constraints, and disagreement.strongAnswerSketch: 3-5 action bullets with decisions, mechanisms, and trade-offs.whyItWorks: 2-4 bullets tied to the competency and evaluation signals.weakVersion: a plausible but insufficient answer pattern.followUpAngles: questions that test ownership, judgment, metrics, disagreement, and learning.Prefer examples that differ by operating context:
Avoid creating variants that only rename the same problem. If two examples test the same behavior, make one reveal a different failure mode or stakeholder tension.
Run:
python3 _scripts/validate_interviews.py
python3 _scripts/summarize_coverage.py
python3 build.py
Add or improve Markdown bold emphasis for key phrases in behavioral interview JSON text. Use when highlighting competency briefs, prompts, prompt variants, interviewer intent, evidence, decision criteria, trade-offs, operating mechanisms, red flags, follow-up probes, or rubric signals with **key phrase** markers while preserving valid project data.
Promote a specific behavioral interview on LinkedIn from a provided interview.json URL or path. Use when drafting LinkedIn copy that explains behavioral interviews, their education and interviewing value, the project, and includes both the concrete interview anchor link and the full catalog link.
Review behavioral interview specs in this repository for realism, balance, educative value, interviewer usability, and data quality. Use when asked to critique interviews, audit the catalog, identify gaps, score interview quality, or write/update REVIEW.md with findings and recommendations.
Review behavioral interview specs in this repository for realism, balance, educative value, interviewer usability, and data quality. Use when asked to critique interviews, audit the catalog, identify gaps, score interview quality, or write/update REVIEW.md with findings and recommendations.
Create new behavioral interview specs for the spec-driven-behavioral-interviews repository. Use when adding a new interview dataset, choosing category placement, scaffolding data/book or data/examples entries, filling the JSON schema, or preparing a new interview for the static explorer.
Improve existing behavioral interview specs in this repository. Use when upgrading an interview JSON file, making generic examples more concrete, strengthening strong and weak answer patterns, deepening follow-up probes, improving evaluation rubrics, or aligning an existing interview with the project schema and quality bar.