# Resume Tailor

> Tailor an existing resume to a specific job description and deliver it as an editable Google Doc (or .docx). Use this skill whenever the user supplies a resume together with a job description, job posting, JD spreadsheet, or a bulleted list of role requirements and wants the resume rewritten, tailored, optimized, keyword-matched, ATS-checked, or scored against that role — even if they only say something like "here's the JD, update my resume", "make this match the job", "will this get through ATS", or paste a posting with no instructions at all. Also use it when the user asks for a resume review or scorecard, or asks to fix, modernize, or rebuild a resume with no JD attached.

- Skill: `pramoddutta/resume-tailor-2` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add pramoddutta/resume-tailor-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/pramoddutta/resume-tailor-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics, Docs & Writing
- Author: PramodDutta (https://skillmd.com/u/pramoddutta)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/pramoddutta/resume-tailor-2

---


# Resume Tailor

Turn a resume plus a job description into a tailored, ATS-safe, honest resume the
candidate can edit in Google Docs — with every change visible so they can approve it.

The job here is *not* to maximize keyword overlap. It is to make every genuine match
legible to a recruiter in six seconds, and to tell the candidate the truth about the
gaps rather than papering over them. A resume that wins a screen and loses the
interview has cost the candidate more than a rejection would have.

## Workflow

Follow these six steps in order. Step 4 is a gate — do not skip it.

### 1. Read both inputs

**Resume** — usually a PDF or .docx in `/mnt/user-data/uploads`. If its content is not
already visible in context, consult the `file-reading` and `pdf-reading` skills. Extract
every role, date, metric, tool, and claim. Note what's *missing* too: education, dates,
certifications, and unexplained gaps all matter later.

**Job description** — may arrive as pasted text, a bulleted list, a link, or a
spreadsheet. For `.xlsx`/`.csv`, consult the `xlsx` skill; JD spreadsheets typically
carry one requirement per row, sometimes with a priority or must-have column — respect
that column if present, since it tells you which gaps are fatal.

If the user gives a JD but no resume, ask for the resume — do not invent a history. If
they give a resume but no JD, skip to the review path in
`references/writing-rules.md` (Review Mode) and produce a scorecard plus a rebuilt resume.

### 2. Extract the JD's real requirements

Pull out and group:

- **Named hard skills** — specific tools, platforms, languages, certifications. These are
  the gates. Count how many times each appears and where (title and first paragraph =
  gate; buried in a nice-to-have list = soft).
- **Responsibilities** — the verbs. These become bullet openers.
- **Seniority signals** — years required, team size, scope, leadership language.
- **Domain** — the industry or problem space.
- **Recurring vocabulary** — the exact phrasing the JD reuses. Prefer *their* words over
  synonyms; ATS keyword matching is literal, and a human reader recognizes their own
  language.

### 3. Cross-reference against the resume

Build a match table. Three verdicts only:

| Verdict | Meaning |
|---|---|
| ✅ Match | Real evidence exists in the resume. May need rewording, not inventing. |
| 🟡 Partial | Adjacent or implied experience. Surfaceable — but confirm with the user before asserting it. |
| 🙈 Absent | No evidence. Stays absent. |

Adjacency is where the value is, and where the discipline is. Testing an analytics
product is genuine adjacency to "data quality." Having used a database is *not*
adjacency to a named BI platform. When unsure, mark it 🟡 and ask.

### 4. Report before you write

Show the user the match table and a blunt fit estimate *before* producing the resume.
State plainly which named requirements are absent and that they will not be added.
If the absent items are the JD's headline requirements, say the role is a stretch and
say what would actually change that — usually a specific certification or a cover note
that names the gap.

This step exists because the candidate, not the assistant, decides whether to apply.
Handing over a polished document that quietly overstates their background takes that
decision away from them.

### 5. Build the resume

Write a JSON spec and render it:

```bash
node scripts/build_resume.js spec.json                    # working copy
node scripts/build_resume.js spec.json --clean            # send-ready copy
```

Inline markup, usable in any text field:

| Markup | Renders as | Purpose |
|---|---|---|
| `==text==` | yellow highlight | a change made for this JD — lets the user audit every edit |
| `[text]` | red bold | a fact only the candidate can supply |
| `**text**` | bold | a metric or term worth anchoring the eye on |

`--clean` strips highlights, drops `note` blocks, and **refuses to build if any
`[placeholder]` remains**, printing the offenders. That guardrail is the point: it makes
it structurally hard to send out a resume with `[N]` still in it.

Schema and a complete worked example: `references/resume-json.md`.
Bullet formulas, section order, ATS rules, and the defects to fix on sight:
`references/writing-rules.md`.

Default output is one working copy. Produce a second variant only when the user has a
real reason for one — a different target title, a different seniority level, or a
one-page hard limit. Variants are a cost to maintain; don't generate them reflexively.

### 6. Deliver

Upload to Google Drive as a native Google Doc so the user can edit immediately:

```
Google Drive:create_file(
  title="<Name> — <Target Role> — <Company>",
  base64Content=<base64 of the .docx>,
  contentMimeType="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
)
```

Drive converts .docx to a Google Doc automatically — highlights and colours survive. If
the connector is unavailable, search the MCP registry and offer it; meanwhile deliver the
.docx with `present_files`, which opens in Google Docs anyway.

Then close with: the Drive link, the count of highlighted changes, and the three or four
placeholders that matter most — ranked by what they do for the application, not by
where they appear in the document.

## Non-negotiables

**Never add a skill, tool, employer, date, or metric the resume does not support.**
Not in the summary, not in a skills row, not softened to "exposure to." The candidate
will be asked about it by someone who does the job for a living.

**Numbers come from the candidate.** Where a bullet needs a metric that isn't in the
source resume, emit a `[placeholder]` rather than a plausible-sounding figure. An
invented "reduced regression by 40%" is the single most damaging thing this skill could
produce, because it reads perfectly and cannot be defended.

**Reframing is fair; relabelling is not.** Rewriting "drive continuous improvement in
automation practices" as "drive continuous improvement initiatives that cut regression
from X to Y" is honest reframing into the JD's vocabulary. Adding a platform they have
never opened is not.

**Flag contradictions rather than smoothing them.** Mismatched locations, an award dated
before the job that granted it, a claimed year count the timeline doesn't support — a
recruiter will spot these, so surface them to the user.

**Say when the fit is poor.** A candidate who applies knowing they are a 60% match and
addresses the gap in a cover note does better than one who was told everything looked great.

## Reference files

- `references/writing-rules.md` — bullet formulas, section order and cuts, ATS
  constraints, tense and style rules, standing defects to fix, and Review Mode (no JD).
- `references/resume-json.md` — the spec schema with a full worked example.