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/linkedingit 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/linkedin)<a href="https://agentmods.dev/commands/noamseg/interview-coach-skill/linkedin"><img src="https://agentmods.dev/badge/commands/noamseg/interview-coach-skill/linkedin.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 | $0.03084 |
| Opus 5 | $0.00000 | $0.01542 |
| Sonnet 5 | $0.00000 | $0.00617 |
| Haiku 4.5 | $0.00000 | $0.00308 |
Grade A, and why
linkedin 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 4d 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 — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
linkedin — LinkedIn Profile Optimization
Optimize the candidate's LinkedIn profile as a search-discoverable, credibility-building, job-landing surface. LinkedIn is its own platform with its own rules — not a resume mirror. The profile needs to be optimized for how LinkedIn actually works: recruiter boolean search, algorithm distribution, section-specific mechanics, and content strategy.
Also read references/differentiation.md (for earned secret integration into profile) and references/storybank-guide.md (for storybank data to feed into About/Experience rewrites).
How LinkedIn Actually Works
Recruiter Search: LinkedIn Recruiter uses boolean search across profile fields. Headline has the highest keyword weight, followed by current title, skills, experience descriptions, and about. Recruiter Lite matches on title + headline + skills. Full Recruiter also searches experience descriptions and about. Skills are filterable (checkbox filters in Recruiter search). Open to Work increases visibility in recruiter results.
Algorithm (for content/engagement): Posts go through 3-phase distribution: quality filter → test audience → broader network. Comments are ~15x more valuable than likes for reach. PDF carousels get 3-6x engagement vs text. External links reduce reach ~60%. Engagement in first 60-90 min determines distribution.
Section Impact Ranking (for recruiter discovery + profile visits):
- Headline (highest search weight, first thing seen)
- Current title (second-highest search weight)
- Skills (filterable in recruiter search — the only filterable field)
- Experience descriptions (searched in full Recruiter)
- About section (searched, but lower weight)
- Photo + banner (affects click-through rate from search results)
- Open to Work signal (increases recruiter visibility)
- Featured section (credibility when they visit your profile)
- Recommendations (social proof, low search weight)
- Custom URL + completeness signals
Priority Check
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.
- 4d ago First seen · 286 lines · 0 tokens per session scan A dca47f7fbb5b
linkedin is a command published in the GitHub repository noamseg/interview-coach-skill (2,092 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,084 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
git
Git operations with intelligent commit messages and workflow optimization.
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.