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 sayantan94/AppliedIn --skill resume-tailoringgit clone --depth 1 https://github.com/sayantan94/AppliedInWrote 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/sayantan94/appliedin/resume-tailoring)<a href="https://agentmods.dev/skills/sayantan94/appliedin/resume-tailoring"><img src="https://agentmods.dev/badge/skills/sayantan94/appliedin/resume-tailoring/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/sayantan94/appliedin/resume-tailoring"><img src="https://agentmods.dev/badge/skills/sayantan94/appliedin/resume-tailoring.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00067 | $0.00771 |
| Opus 5.5 | $0.00027 | $0.00308 |
| Sonnet 5.5 | $0.00013 | $0.00154 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
resume-tailoring 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 2mo 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Résumé tailoring
Tailor the candidate's seed résumé — the LaTeX in state base_latex — to the
job description (state jd_text). Output the full tailored .tex via
save_tailored_resume. The result must stay 100% truthful and still compile.
Instructions
Step 1: Read the JD for signal
Extract must-have skills, seniority, domain, and the exact vocabulary it uses ("agentic", "MCP", "multi-agent", "platform", "LLM evaluation").
Step 2: Edit the LaTeX — reword, rephrase, then reorder
Tailoring is primarily rewording and rephrasing, not restructuring. Change only these:
\resumeItem{...}bullets — rephrase each bullet in the JD's own vocabulary and framing: keep the true accomplishment, but say it the way the JD says it (its verbs, its nouns, its emphasis). Only where it's genuinely true. Then reorder so the most JD-relevant come first.- The Summary line — rephrase it to target this exact role.
- The order of skills within the Skills line.
Example — JD stresses "multi-agent orchestration":
\resumeItem{Built workflows where agents talk to each other} →
\resumeItem{Built multi-agent orchestration for agent-to-agent workflows}
(same fact, JD's words). Never claim orchestration if the seed doesn't show it.
Leave untouched, byte-for-byte:
- Every
\resumeSubheading{...}/\resumeSubheadingSingle{...}line (employer, title, dates, project name, patent) — these are the immutable facts. - The preamble,
\sectionheaders, and document structure.
For detailed techniques (truthful vocabulary mirroring, quantification, hard
cases), consult references/emphasis-techniques.md.
Step 3: Save
Call save_tailored_resume(tailored_latex=<the full .tex>). It validates the
facts survived, compiles the PDF with Tectonic, and uploads it. If it returns
missing_facts, you altered a \resumeSubheading line — restore it verbatim and
re-save.
Hard rules
- NEVER change or drop an employer, title, employment date, degree, institution, certification, or patent number. Copy those lines verbatim.
- NEVER add a skill or achievement the seed doesn't contain.
- Keep the LaTeX valid — balanced braces, defined macros only. Every claim must survive an interview.
- NEVER write internal engineering minutiae. A bullet states what was built and what it achieved, never the private history of how the code got there. Banned: line/file counts and deltas ("cut 3.1K lines across 5 files to 636 across 2"), refactor and rewrite narratives, bug-hunt stories, commit or PR counts, names of internal modules or subprocesses, and framing that describes fixing your own earlier mistake. A reader outside the repo cannot verify any of it, and reducing code is not an accomplishment on its own — it reads as churn. Rewrite to the outcome: what the system now does, at what scale, for whom.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 61 lines · 67 tokens per session scan A 523efac12948
resume-tailoring is a skill published in the GitHub repository sayantan94/AppliedIn (8 stars, last pushed 13d ago), licensed MIT. It adds 67 tokens to every session and 771 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
resume-tailoring compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| resume-tailoringsayantan94/AppliedIn | Skill | 8 | 13d ago | MIT | A | 67 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |