ResumeSkills is a collection of AI-agent skills for improving resumes, preparing job applications, practicing interviews, and planning career moves. It is intended for job seekers, career changers, and professionals using Claude Code for tasks such as ATS checks, job-description matching, resume tailoring, and salary negotiation. The catalogue entry consists of the project's career-focused skills.
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 Paramchoudhary/ResumeSkills --skill resume-quantifiergit clone --depth 1 https://github.com/Paramchoudhary/ResumeSkillsWrote 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/paramchoudhary/resumeskills/resume-quantifier)<a href="https://agentmods.dev/skills/paramchoudhary/resumeskills/resume-quantifier"><img src="https://agentmods.dev/badge/skills/paramchoudhary/resumeskills/resume-quantifier.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00017 | $0.02013 |
| Opus 5 | $0.00009 | $0.01007 |
| Sonnet 5 | $0.00003 | $0.00403 |
| Haiku 4.5 | $0.00002 | $0.00201 |
Grade A, and why
resume-quantifier 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- resume-quantifier — 100% identical, 0 lines differ
- resume-quantifier — 100% identical, 0 lines differ
- resume-quantifier — 95% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Quantifier
When to Use This Skill
Use this skill when the user:
- Needs to add metrics and numbers to their resume
- Has bullets without quantifiable results
- Doesn't know what numbers to include
- Says they "don't have metrics" or "can't measure impact"
- Mentions: "add metrics", "quantify", "add numbers", "measure impact", "no data"
Core Capabilities
- Find hidden metrics in any experience
- Estimate numbers when exact data unavailable
- Create before/after comparisons
- Identify measurable impact points
- Transform vague statements into quantified achievements
- Guide users to discover their metrics
Why Quantification Matters
The Problem:
- "Managed projects" vs "Managed 12 projects worth $2M"
- "Improved processes" vs "Reduced cycle time by 40%"
- "Helped customers" vs "Resolved 50+ tickets daily with 98% satisfaction"
Studies Show:
- Resumes with numbers get 30% more attention
- Quantified bullets are 40% more memorable
- Numbers provide credibility and scale
The Quantification Framework
Categories of Metrics
1. Money
- Revenue generated
- Costs reduced/saved
- Budget managed
- Deal sizes closed
- Profit margins improved
2. Time
- Hours saved
- Cycle time reduced
- Project duration
- Response times
- Time to market
3. Percentages
- Growth rates
- Improvement percentages
- Efficiency gains
- Error reduction
- Conversion rates
4. Volume/Scale
- Number of customers/users
- Projects managed
- Team size
- Transactions processed
- Items produced
5. Quality
- Satisfaction scores
- Error rates
- Accuracy rates
- Compliance rates
- SLA adherence
6. Frequency
- Per day/week/month
- Annual totals
- Meeting cadences
- Report cycles
Finding Hidden Metrics
The Discovery Questions
For any experience, ask:
Scale Questions:
- How many people/projects/customers?
- What was the budget/revenue involved?
- How large was the team?
- How many locations/regions?
Impact Questions:
- What changed because of your work?
- What would have happened without you?
- What problems did you solve?
- What got better/faster/cheaper?
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 · 351 lines · 17 tokens per session scan A 6cdf56f990ff
resume-quantifier is a skill published in the GitHub repository Paramchoudhary/ResumeSkills (2,147 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 2,013 once invoked, about $0.0001 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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