/resume-tailoring

name: resume-tailoring
description: Methodology for tailoring a resume to a job description honestly and effectively — fit analysis, gap handling, keyword mirroring, fast-learner framing, and primary-over-secondary emphasis. Use whenever curating or rewriting a resume for a specific role.

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

1. **The base resume is the source of truth.** Re-word, re-order, re-emphasize. Never invent employers, titles, dates, tools, or metrics. 2. **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. 3. **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

1. **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. 2. **Fit analysis** — two columns:

• *Strong matches*: JD requirement → concrete evidence from the base resume.
• *Gaps*: required things the base doesn't show.

3. **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.

4. **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.

5. **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).

Framing patterns that work

• **Fast-learner / fundamentals** (neutralizes a required-but-unused stack): cite the real pattern — "stack changed completely at every company move and shipped each time, on strong OOP/systems fundamentals." Pairs with the honest current level; never a substitute for a false claim.
• **Differentiators lead**: rare, verifiable assets (publications, OSS, speaking/writing, awards, unusual domain depth) are what a referral rides on — surface them early. They also independently validate softer claims.
• **Quantify**: every bullet earns its place with an outcome or number.

Level & title

• Match the resume's stated title/seniority to the target level when the user asks (e.g., present as "Senior" not "Principal" for a mid/senior role to avoid over-qualification) — change it consistently across tagline, summary, and the current-role header.

Deliverables

• Tailored **.docx + text-based .pdf** (see the `resume-export` skill; ATS-safe layout).
• Optional **role-fit note** (4–5 lines) for a referrer: honest, leads with differentiators, names any gap as fast-ramp.
• A short plain-English summary to the user of what you emphasized and every honesty flag you set.