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 snehag01/rebound --skill resume-tailoringgit clone --depth 1 https://github.com/snehag01/reboundWrote 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/snehag01/rebound/resume-tailoring)<a href="https://agentmods.dev/skills/snehag01/rebound/resume-tailoring"><img src="https://agentmods.dev/badge/skills/snehag01/rebound/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/snehag01/rebound/resume-tailoring"><img src="https://agentmods.dev/badge/skills/snehag01/rebound/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 | $0.00053 | $0.00897 |
| Opus 5 | $0.00026 | $0.00449 |
| Sonnet 5 | $0.00011 | $0.00179 |
| Haiku 4.5 | $0.00005 | $0.00090 |
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 10d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Tailoring — the Rebound method
Tailor a resume so it is genuinely relevant to the target JD and defensible in an interview. Relevance without honesty gets people screened out at the technical round; honesty without relevance never gets them in. Do both.
Non-negotiables
- The base resume is the source of truth. Re-word, re-order, re-emphasize. Never invent employers, titles, dates, tools, or metrics.
- Honesty-first tooling. Never present a technology the person hasn't used as expertise. Label unproven-but-plausible tools "(working knowledge)" or "(familiar)". If they've never touched it, it goes in framing (fast-ramp), not in a skills list as a core competency.
- Primary over secondary. Surface JD-relevant secondary skills, but never rank them above the person's actual primary stack. (A backend/distributed engineer applying to a full-stack role still leads with backend depth; React/Node support it, don't headline over it.)
Workflow
- Parse the JD into: title, level, must-have (required) quals, nice-to-have (preferred) quals, and the explicit tech stack. Note remote/hybrid and any hard filters.
- Fit analysis — two columns:
- Strong matches: JD requirement → concrete evidence from the base resume.
- Gaps: required things the base doesn't show.
- Handle material gaps by asking, not guessing. For a top required skill the base lacks, ask the user their real exposure (production / some / none). Their answer sets the treatment:
- Production → feature it as a core skill.
- Some / adjacent → "(working knowledge)" + transferable framing.
- None → do not claim it; lean on fast-learner framing (see below) and genuine adjacent strengths.
- Rewrite for the role:
- Summary + title/tagline: lead with the strongest truthful matches; mirror the JD's own words (ATS keyword match) without stuffing.
- Experience bullets: reorder and re-frame toward the JD. Keep every metric. Lead bullets with a bolded outcome/verb.
- Skills: front-load matched keywords in sensible groupings; drop dead/irrelevant tools.
- Cluster similar roles. If several target roles are near-identical, build ONE differentiated resume and reuse it — don't ship 13 trivially different files. Genuinely distinct role families each get their own drastically-different cut (summary, skills, reframed bullets).
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.
- 10d ago First seen · 42 lines · 53 tokens per session scan A 74ef82fff6d3
resume-tailoring is a skill published in the GitHub repository snehag01/rebound (4 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 897 once invoked, about $0.0003 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 2026-08-31.
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