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/researchgit 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/research)<a href="https://agentmods.dev/commands/noamseg/interview-coach-skill/research"><img src="https://agentmods.dev/badge/commands/noamseg/interview-coach-skill/research.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 | $0.02049 |
| Opus 5 | $0.00000 | $0.01025 |
| Sonnet 5 | $0.00000 | $0.00410 |
| Haiku 4.5 | $0.00000 | $0.00205 |
Grade A, and why
research 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
research — Company Research Workflow
A lightweight alternative to prep for when the candidate wants to understand a company before committing to a full prep cycle. Use when they're evaluating whether to apply, building a target list, or doing early-stage reconnaissance.
When to Use Research vs. Prep
| Situation | Use |
|---|---|
| Evaluating whether to apply | research |
| Building a target company list | research (run multiple) |
| Have an interview scheduled | prep |
| Want to understand company culture before networking | research |
| Need predicted questions and story mapping | prep |
Sequence
- Ask for company name and the candidate's target role type (if not already in coaching state).
- Research publicly available information. Follow the same Company Knowledge Sourcing tiers from
prep— Tier 1 (verified), Tier 2 (general knowledge), Tier 3 (unknown/say so). - Assess fit against the candidate's profile (from
coaching_state.mdif available, or from what they've told you). - Output the research brief.
Research Depth Levels
| Level | When to Use | What to Do | Time Investment |
|---|---|---|---|
| Quick Scan | Building a target list, evaluating 5+ companies at once | Company website + careers page + recent news. Enough for a basic fit assessment. | 5-10 min |
| Standard | Evaluating whether to apply. Default for research. |
Full protocol: website, careers, news, Glassdoor, LinkedIn, blog. Produces a complete research brief. | 15-20 min |
| Deep Dive | High-priority target, interview scheduled, want maximum intelligence | Standard + employee posts/talks, product reviews, competitor analysis, leadership team profiles. | 30+ min |
Default to Standard. Suggest Deep Dive when:
- The candidate has an interview scheduled at this company
- The candidate explicitly asks for comprehensive intelligence
- The company is in the candidate's top 3 targets
Structured Search Protocol
Search for information in this order. Each step builds on the previous ones:
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 · 144 lines · 0 tokens per session scan A 91561ce1ab24
research 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 2,049 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.