From swe-interview-coach
Provides STAR, SBI, and CARL frameworks for behavioral interview prep. Use for extracting stories, mapping to job descriptions, mock interviews, and debriefing.
How this skill is triggered — by the user, by Claude, or both
Slash command
/swe-interview-coach:behavioral-frameworksThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Primary structure for behavioral answers. Every story should map to exactly one S, T, A, R.
Primary structure for behavioral answers. Every story should map to exactly one S, T, A, R.
The context that made the problem worth solving. Sets stakes without over-explaining.
Weak: "We had a frontend codebase that was getting kind of hard to work with and everyone was writing their own components."
Strong: "At Acme, our five frontend teams were duplicating UI components across three apps. QA bounce-backs on UI-heavy features ran at ~40%, and each sprint lost roughly two engineer-days to inconsistent implementations."
The specific outcome you were accountable for — your mandate, not the team's.
Weak: "We decided to build a shared component library so things would be more consistent."
Strong: "I was tasked with designing and driving adoption of a shared component library with a hard deadline of Q2 — without a formal mandate, meaning I needed buy-in from five team leads who had no obligation to participate."
The decisions you made and the steps you took. This is the longest element (~50% of the answer).
Weak: "We built the library, documented it, and did roadshows to get people to use it. We also added linting rules and set up CI checks."
Strong: "I ran a two-week audit to surface the ten most-duplicated components and prioritized those first — faster ROI, shorter time-to-first-win. When two team leads resisted ('another tool to maintain'), I offered to pair on migrating their first feature; once they saw the QA bounce-back drop within a sprint, the objection evaporated. I enforced adoption at the code-review level rather than through a mandate: new components had to extend the library or include a written justification."
The outcome, quantified wherever possible. Covers both the immediate metric and the second-order effect.
Weak: "The library was adopted across the org and things got better. People were happier and we shipped faster."
Strong: "QA bounce-backs on UI features dropped from ~40% to ~12% within two quarters. Sprint velocity on UI-heavy features improved 15% (team estimate). SonarQube duplication score for frontend code fell by 60%. The library is still the standard two years later — zero teams have forked it."
Use for feedback questions: "Tell me about feedback you gave/received," "Describe a time you had a difficult conversation."
Weak: "My tech lead had a habit of dominating planning meetings and not letting others speak. I told him it was hurting the team."
Strong: "During sprint planning, Alex consistently cut off junior engineers before they finished their estimates — twice in the same meeting I watched two people stop contributing entirely. I told him: 'In today's planning I noticed you jumped in before Sarah and Mirek finished their estimates. The effect was they both stopped talking. I'd like you to wait for a full stop before responding.' He wasn't aware of it; by the next sprint the pattern had shifted."
Use for "what would you do differently" follow-ups and retrospective questions.
Weak: "If I did it again I'd communicate more and involve stakeholders earlier."
Strong: "I shipped the migration behind a feature flag without looping in the platform team — I assumed a flag was safe. When the flag state drifted in staging vs. prod, we had a 45-minute incident. The learning: I now treat any state that spans environments as a coordination surface, not a solo call. I added an explicit 'who else owns this state?' check to my pre-ship checklist."
Rambling. No internal time-budget; the Action sprawls into multiple paragraphs with no end in sight. Sounds like: "…and then we also did X, and there was this whole other thing with Y, and oh, also worth mentioning…"
No metrics in Result. Outcome described entirely in adjectives; nothing a skeptic could verify. Sounds like: "The team was really happy with the outcome and things improved a lot."
Blame-shifting. Agency for the failure lands on someone else; your role in it disappears. Sounds like: "The project was late because leadership kept changing the requirements on us."
Weasel pronouns. "We" used where "I" is accurate, diffusing individual ownership. Sounds like: "We decided to refactor the service" — when you made that call alone and drove it solo.
Passive voice obscuring agency. The decision just happened; no named actor made it. Sounds like: "A decision was made to deprecate the old API" — who made it?
Missing the actual decision (skipping Task). Jumping from Situation to Action with no explanation of what you were specifically responsible for. Sounds like: "We had this flaky test suite, so I started looking at the test runner config…" — but what was your mandate? Fix it? Investigate? Own it forever?
Framing failures as successes. The story has no real downside; every obstacle was effortlessly overcome. Sounds like: "It was challenging, but we pulled together and in the end we delivered everything on time." (Interviewers have seen thousands of these — they stop trusting the whole story.)
"What did your manager say?" Probes for stakeholder validation and whether the candidate correctly attributes credit at a level visible above them.
"What was the actual metric?" Probes for precision — distinguishing a real measurement from a feeling. Have a specific number or an honest "we didn't instrument it, but here's the proxy."
"What would you do differently?" Probes for self-awareness and growth mindset; also surfaces whether you've genuinely reflected or are just pattern-matching the expected answer.
"Who pushed back and how did you respond?" Probes for conflict navigation, ability to hold a position under pressure, and whether you updated your view with new evidence.
"What was the hardest trade-off?" Probes for engineering judgment — that you saw real constraints and made a deliberate choice, not that everything was straightforward.
Scope — how many people, systems, or dollars were affected. Matters because it calibrates the level of problem; a 3-person team story reads differently than a cross-org story for a staff role.
Initiative — you started it vs. you were assigned. Matters because it signals proactivity and ownership; "I noticed this and started it without being asked" scores higher than "my manager asked me to fix this."
Measurable impact — a concrete metric, not "improved a lot." Matters because it shows you close loops: you define success, ship, and verify. Vague outcomes suggest you may not have checked.
Retrospective learning — what you took away (the CARL element). Matters because it signals intellectual honesty and growth; interviewers are partly hiring for the person you'll be in two years, not just the person you are today.
npx claudepluginhub kirilxd/swe-interview-coach --plugin swe-interview-coachConducts structured behavioral interviews using the STAR method with standardized questions and scoring. Useful during hiring to ask competency-based questions that predict job performance.
Provides frameworks for technical interview prep: STAR method for behavioral questions, communication strategies, offer evaluation, and salary negotiation.
Draft an interview answer from the LockedIn vault, in English or Korean. Uses the same two-turn writer/reviewer pattern as the resume and cover letter renderers. The user asks an interview question (behavioural, technical, or background); the writer turn pulls evidence from the vault and drafts a STAR-shaped answer; the reviewer turn checks it against a small rubric (clarity, evidence density, persona fit, conciseness, tone) before the final answer is shown. Use when the user says any of <!-- ko-example -->"interview answer", "면접 답변", "STAR 답변", "behavioral question", "tell me about a time…"<!-- /ko-example -->, or names a question and asks for an answer drawn from their experience. Output: a single answer in markdown, with cited entities resolved to natural language, plus a JSON rubric score.