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 skills add nimadorostkar/Claude-Skills-collection --skill resumegit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/skills/nimadorostkar/claude-skills-collection/resume)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/resume"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/resume/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/resume"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/resume.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.01279 |
| Opus 5 | $0.00018 | $0.00639 |
| Sonnet 5 | $0.00007 | $0.00256 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
resume 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 13d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume
Purpose
Write a resume that survives a six-second scan and a hiring manager's skepticism. Most resumes fail on the same thing: they list responsibilities rather than demonstrating outcomes.
When to Use
- Writing or rewriting a resume.
- Tailoring a resume to a specific role.
- A resume that generates no responses.
- Reviewing someone else's resume.
Capabilities
- Structure and prioritization.
- Turning responsibilities into evidence.
- Tailoring without dishonesty.
- Handling gaps, career changes, and short tenures.
- Applicant-tracking-system considerations.
Inputs
- The person's actual experience, in detail.
- The specific role, and its posting.
- What is genuinely relevant to it.
Outputs
- A resume where each bullet is evidence.
- A version tailored to the role, drawing on real experience.
- No adjectives doing the work that numbers should do.
Workflow
- Extract the outcomes — For each role: what changed because you were there? Not what you were responsible for. What was different afterwards.
- Quantify — Numbers survive a six-second scan; adjectives do not. If a number is not available, use a comparison, a scale, or a before-and-after.
- Lead with the strongest evidence — The top third of the first page is nearly all that is read on the first pass. Put the best thing there.
- Tailor to the posting, honestly — Reorder, re-emphasize, and use the posting's vocabulary where it genuinely describes what you did. Never invent.
- Cut everything irrelevant — A resume is an argument for one role, not a complete autobiography.
- Check it survives a keyword scan — Most applications pass through automated filtering. If the posting says "Kubernetes" and your resume says "container orchestration", you may be filtered out for a skill you have.
Best Practices
- "Responsible for the deployment pipeline" says nothing. "Cut deploy time from 45 to 6 minutes; deploy frequency tripled" says everything. The first describes a job description; the second describes a person.
- Adjectives are what you write when you do not have evidence. "Highly motivated", "results-driven", "passionate" — cut all of them, always. They are free to write, so they carry no information.
- Numbers do not have to be impressive to be useful. "Reduced the on-call page volume from ~30 to ~8 per week" is a small number and a compelling one.
- Tailoring is reordering and re-emphasizing what is true. It is not inventing. The interview will find the invention.
- Two pages maximum, and one page for under ten years of experience. Length is not evidence of substance.
- If a hiring manager can substitute anyone else's name at the top and the resume still reads correctly, it is not a resume — it is a job description.
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.
- 13d ago First seen · 117 lines · 37 tokens per session scan A fa8f2635d15c
resume is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 25d ago), licensed MIT. It adds 37 tokens to every session and 1,279 once invoked, about $0.0002 per session on Opus 5. 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.
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