Interview Coach is a Claude Code-based coaching system for the full job-search process, including job-description analysis, application materials, interview practice, answer evaluation, and offer negotiation. It is intended for job seekers who want tailored feedback and structured preparation based on their own experience and interview transcripts. Its catalogue entry consists of commands, a setting, and a skill that provide the coaching workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/noamseg/interview-coach-skill/kickoffgit clone --depth 1 https://github.com/noamseg/interview-coach-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/noamseg/interview-coach-skill/kickoff)<a href="https://agentmods.dev/commands/noamseg/interview-coach-skill/kickoff"><img src="https://agentmods.dev/badge/commands/noamseg/interview-coach-skill/kickoff.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.02577 |
| Opus 5 | $0.00000 | $0.01288 |
| Sonnet 5 | $0.00000 | $0.00515 |
| Haiku 4.5 | $0.00000 | $0.00258 |
Grade A, and why
kickoff scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
kickoff — Setup Workflow
Step 1: Coaching Configuration
Collect:
- Track choice:
Quick PreporFull System - Target role(s)
- Feedback directness (1-5, default 5)
- Interview timeline
- Biggest concern
- Interview history: "Have you been interviewing already? How many interviews have you done for this type of role, and how have they gone?" This shapes the entire coaching path:
- First-time interviewer: Needs fundamentals — storybank building, basic structure, confidence building. Start with practice ladder.
- Active but not advancing: Needs diagnosis. Ask: "Where are you getting stuck — first rounds, final rounds, or not hearing back at all?" First-round failures suggest Relevance/Structure problems. Final-round failures suggest Differentiation/Credibility problems. Tailor the coaching plan accordingly. For candidates who are active but stalling, Ethan Evans' "Magic Loop" framework (via Lenny's Newsletter) can help diagnose whether the problem is interview performance or career positioning. The Magic Loop: (1) Do great work, (2) Tell the right people about it, (3) Ask them what's most important, (4) Do great work on that. If the candidate's stalling pattern maps to steps 2-3 (telling and asking), the coaching should prioritize positioning and communication (pitch, stories, practice). If it maps to step 1 (work substance), the issue may be targeting rather than interviewing skills.
- Experienced but rusty: Needs refreshing, not rebuilding. Focus on updating stories with recent experience and sharpening differentiation.
Step 2: Candidate Context
Required:
- Resume text or upload summary
Strongly recommended:
- LinkedIn URL
- 2-3 target companies
- 3-5 initial stories
Step 2.5: Resume Analysis
Don't just file the resume — analyze it for coaching-relevant signals:
- Positioning strengths: What's the candidate's strongest narrative thread? What would a hiring manager see in 30 seconds? Identify the 2-3 most impressive signals (scope of impact, career trajectory, domain expertise, brand-name companies).
- Likely concerns: What will interviewers worry about? Look for:
- Career gaps or short tenures (< 1 year)
- Lateral moves or title regressions
- Domain switches (e.g., B2C to B2B, startup to enterprise)
- Seniority mismatches (targeting a level above or below recent roles)
- Missing keywords that the target role requires
- "Invisible" contributions — important work that doesn't translate to resume bullets
- Career narrative gaps: Where the story doesn't connect. "You went from engineering at [Company A] to product at [Company B] — that transition is a story you'll need to tell well. Do you have one ready?"
- Story seeds: Resume bullets that likely have rich stories behind them — flag these for storybank building. "This bullet about reducing churn by 40% — there's probably a strong story behind that. Let's capture it."
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 175 lines · 0 tokens per session scan A d615b7a04c0e
kickoff is a command published in the GitHub repository noamseg/interview-coach-skill (2,112 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,577 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.