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.
git 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/outreach)<a href="https://agentmods.dev/commands/noamseg/interview-coach-skill/outreach"><img src="https://agentmods.dev/badge/commands/noamseg/interview-coach-skill/outreach.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 | $0.05194 |
| Opus 5 | $0.00000 | $0.02597 |
| Sonnet 5 | $0.00000 | $0.01039 |
| Haiku 4.5 | $0.00000 | $0.00519 |
Grade A, and why
outreach 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 8d 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 — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
outreach — Networking Outreach Coaching
Coach the candidate through the full outreach lifecycle: cold LinkedIn messages, warm introduction requests, informational interview asks, recruiter replies, follow-up sequences, and referral requests. Messages are built on the candidate's Positioning Statement so every outreach is differentiated, not generic.
Also read references/differentiation.md (for earned secret integration into message hooks) and references/storybank-guide.md (for story selection to build credibility in messages).
How Outreach Actually Works
Response Rates by Channel: Cold email 3-5% baseline, top quartile 15-25%. LinkedIn InMail 10-25%. Connection request + personalized follow-up: 45% acceptance, 39% positive reply. Personalized messages 6x higher response. Hierarchy: warm intro > connection request + personalized follow-up > InMail with personalization > cold email with research > generic cold.
Message Length: Cold email 75-125 words. InMail under 400 chars = 22% higher response. Connection request: 300 char limit. Subject line: 28-39 chars.
Platform Mechanics: Connection request 300 chars. InMail 1900 chars. DM to connections ~8000 chars. Plain text email outperforms HTML.
Warm Introductions: 3-5x higher conversion. Double opt-in best practice. Forwardable email framework. Social capital dynamics.
Informational Interviews: 25-33% acceptance. 15-20 minute bounded asks. Never ask for a job during an informational. Same-day follow-up.
Job Search Councils
Phyl Terry's "Never Search Alone" methodology (via Lenny's Podcast) introduces a powerful concept the coach should recommend for candidates in active search: Job Search Councils — small peer groups of 3-5 active job seekers who meet weekly to provide accountability, mock interview practice, emotional support, and network sharing. As Terry notes: "Everyone, no matter who they are, feels insecure and anxious in the job search. And if you do it alone, it magnifies that." The psychological hack: putting anxious people together and asking them to be open "flips the anxiety and the fear into hope, into motivation, into accountability and confidence." Recommend this for any candidate who appears isolated in their search.
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.
- 8d ago First seen · 448 lines · 0 tokens per session scan A eae731df8a6e
outreach is a command published in the GitHub repository noamseg/interview-coach-skill (2,142 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,194 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.