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/storiesgit 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/stories)<a href="https://agentmods.dev/commands/noamseg/interview-coach-skill/stories"><img src="https://agentmods.dev/badge/commands/noamseg/interview-coach-skill/stories.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 5.1 | $0.00000 | $0.03805 |
| Opus 5 | $0.00000 | $0.01903 |
| Sonnet 5 | $0.00000 | $0.00761 |
| Haiku 4.5 | $0.00000 | $0.00380 |
Grade A, and why
stories 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 6d 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
stories — Storybank Workflow
Use references/storybank-guide.md.
Menu:
Storybank Menu
1) View
2) Add
3) Improve
4) Find gaps
5) Retire/archive
6) Drill — rapid-fire retrieval practice
7) Narrative identity — extract your career themes and see how stories connect
Adding Stories — Guided Discovery
When the candidate selects "Add," don't jump straight to STAR format. Most people can't produce stories on command. Use the guided exploration prompts from references/storybank-guide.md (peak experiences, challenge/growth, impact/influence, failure/learning) to surface stories first, then structure them:
- Ask one reflective prompt at a time. Wait for the response.
- Listen for the story embedded in their answer — they may not realize they're telling one.
- When you hear a promising story, say: "That's a strong story. Let's capture it." Then walk through STAR.
- After STAR, extract the earned secret (see
references/differentiation.md). - Index it in the storybank table.
Don't skip the reflective prompts and go straight to "tell me a story about leadership." That produces rehearsed, thin stories. The prompts produce real ones.
Story Construction Principles
Story coach Matthew Dicks (author of Storyworthy, via Lenny's Podcast) identifies what makes stories memorable and compelling — principles the coach should apply when helping candidates shape raw material into interview stories:
- Every story is about a moment of transformation: "I used to think X, then Y happened, and now I know Z." The most memorable interview stories follow this pattern — they show the gap between who the candidate was before and after the experience. When extracting stories, look for the transformation: what changed in the candidate's understanding?
- Stories need stakes: What was at risk? Stories without stakes are anecdotes. As Dicks puts it: "Everyone loves the word storytelling in business... but to be a storyteller means you have to separate yourself from the herd, and in their mind, that risks them getting picked off. But the alternative is you're in the herd, which means you're forgettable."
- Start as close to the end as possible: The #1 revision Dicks gives is "you've started your story in the wrong place." For interview stories, this means: don't set the scene for 90 seconds before getting to the action.
- The "But & Therefore" test: Replace "and then" connectors with "but" and "therefore" to create cause-and-effect chains. If you can't, the story lacks narrative tension.
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.
- 6d ago First seen · 215 lines · 0 tokens per session scan A 3a16cd8a9644
stories is a command published in the GitHub repository noamseg/interview-coach-skill (2,124 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,805 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.