# Resume to JSON
Paste a plain-text resume and get a JSON Resume (v1.0.0) document — or validate an existing resume.json against the schema. Runs in your browser.
## Run it
- **CLI:** `gizza tool resume-to-json "Jane Doe
Senior Software Engineer
San Francisco, CA | jane.doe@example.com | (555) 123-4567 | linkedin.com/in/janedoe
Experience
Senior Software Engineer — Acme Corp
Jan 2020 – Present
- Led migration to a service mesh across 40 services"`
- **Web:** https://gizza.ai/tools/resume-to-json/
- **Agents:** machine-readable descriptor (parameters JSON Schema) at https://gizza.ai/tools/resume-to-json/tool.json
## Inputs
- `data` — Resume text or resume.json _(field)_
- `mode` — Mode _(field)_
- `schema_ref` — Include $schema reference and meta.version _(field)_
- `pretty` — Pretty-print (indent) the JSON _(field)_
## Output
- JSON (text)
## Query parameters
Open the tool pre-filled and auto-run via URL:
- `data` — Resume text or resume.json
- `mode` — Mode
- `schema_ref` — Include $schema reference and meta.version
- `pretty` — Pretty-print (indent) the JSON
Example: `https://gizza.ai/tools/resume-to-json/?data=Jane%20Doe%0ASenior%20Software%20Engineer%0ASan%20Francisco%2C%20CA%20%7C%20jane.doe%40example.com%20%7C%20%28555%29%20123-4567%20%7C%20linkedin.com%2Fin%2Fjanedoe%0A%0AExperience%0A%0ASenior%20Software%20Engineer%20%E2%80%94%20Acme%20Corp%0AJan%202020%20%E2%80%93%20Present%0A-%20Led%20migration%20to%20a%20service%20mesh%20across%2040%20services&mode=auto&schema_ref=true&pretty=true`
---
## About this tool
This tool converts a pasted resume into the standard **JSON Resume** format — the open
v1.0.0 schema used by resume builders, themes, and registries — and can also **validate**
an existing `resume.json` document against that schema. Everything runs locally in your
browser; the resume text never leaves your machine.
**Extract** reads the resume the way a human skims it: the header block becomes `basics`
(name, label, email, phone, URL, location, and LinkedIn/GitHub/Twitter/GitLab/Stack
Overflow/Medium/Dribbble/Behance links as `profiles`), and recognized section headings —
Experience, Education, Skills, Projects, Languages, Awards, Certifications, Publications,
Volunteer, Interests, References, Summary — become the matching schema sections. Date
ranges like `Jan 2020 – Present`, `03/2021`, or `2016 – 2019` are normalized to the
schema's ISO-8601 partial dates (`YYYY`, `YYYY-MM`, or `YYYY-MM-DD`), with an open
`endDate` for "Present". Bullets become `highlights`, grouped skill lines like
`Languages: Rust, Python` become `{name, keywords}` entries, and `English (Native)`
becomes `{language, fluency}`. Empty sections are omitted, and only canonical schema
field names are emitted, so the output drops straight into any JSON Resume theme or CLI.
**Validate** checks a pasted `resume.json` and returns a report:
`{valid, errors, warnings, summary}`. Type mismatches and malformed dates are errors;
suspicious email/URL formats and unknown keys are warnings (the schema allows extra
fields, but most tools ignore them). The default **Auto-detect** mode validates when the
input is a JSON object and extracts otherwise.
### Worked example
Pasting this resume text:
```text
Jane Doe
Senior Software Engineer
San Francisco, CA | jane.doe@example.com | linkedin.com/in/janedoe
Experience
Senior Software Engineer — Acme Corp
Jan 2020 – Present
- Led migration to a service mesh across 40 services
```
produces this JSON Resume document:
```json
{
"basics": {
"name": "Jane Doe",
"label": "Senior Software Engineer",
"email": "jane.doe@example.com",
"location": { "city": "San Francisco", "region": "CA" },
"profiles": [
{
"network": "LinkedIn",
"username": "janedoe",
"url": "https://linkedin.com/in/janedoe"
}
]
},
"work": [
{
"name": "Acme Corp",
"position": "Senior Software Engineer",
"startDate": "2020-01",
"highlights": ["Led migration to a service mesh across 40 services"]
}
]
}
```
### Limits and edge cases
- Input is capped at **1 MiB** of text; larger input is rejected with an error.
- Extraction is a **heuristic, rule-based parser for English section headings** (plus
common variants like "Employment History" or "Core Competencies"). It is not a machine
learning model: unusual layouts, tables, multi-column text, or non-English headings may
land in the wrong section — review the output before publishing it.
- Paste **plain text only**. PDF and Word files must be converted to text first (copy
and paste from the document works well); this tool does not do OCR.
- Extracted output is schema-valid by construction; a "Present"/"Current" end date is
represented by omitting `endDate`, exactly as the schema expects.
- In validate mode the input must be a JSON **object**; a JSON array, string, or broken
JSON returns an error that points at the problem.
## FAQ
What is the JSON Resume schema?
JSON Resume is an open standard that stores a resume as a single JSON document with
well-known sections: `basics`, `work`, `volunteer`, `education`, `awards`,
`certificates`, `publications`, `skills`, `languages`, `interests`, `references`, and
`projects`. Because the shape is standardized, one `resume.json` file can be rendered by
many independent themes, builders, and command-line tools. This tool targets schema
version **v1.0.0** and emits only its canonical field names.
How accurate is the extraction?
It is a deterministic, rule-based parser — not an AI model. It recognizes the common
resume conventions: a header block with name, title, and contact links; section headings
like "Experience" or "Technical Skills"; `Role — Company` entry lines; date ranges in
`Jan 2020 – Present`, `03/2021`, and `2016 – 2019` styles; and bullet lists. Clean,
conventionally formatted resumes extract very well; creative layouts may need a manual
touch-up afterwards. The output is always structurally valid JSON Resume, so anything
that needs fixing is a copy edit, not a schema repair.
What is the difference between errors and warnings in validation?
Errors are violations of the schema's hard constraints: a field with the wrong JSON type
(for example `basics.name` as a number, or `skills[0].keywords` as a string instead of an
array) or a date that does not match the schema's ISO-8601 pattern (`YYYY`, `YYYY-MM`,
or `YYYY-MM-DD` — `"Jan 2020"` is an error). Warnings flag things that are technically
allowed but usually mistakes: an email or URL that does not look well-formed, and unknown
keys (the schema sets `additionalProperties: true`, but most renderers silently ignore
fields they do not know). `valid` is `true` when there are no errors.
Why is my start date missing or wrong?
Dates are read from each entry's header lines, and the first recognized date or date
range wins. Supported forms are `Month YYYY` (including abbreviations like `Sept. 2019`),
`Month DD, YYYY`, `MM/YYYY`, `YYYY-MM`, `YYYY-MM-DD`, and bare `YYYY`, joined by `-`,
`–`, `—`, or the word "to"; `Present`, `Current`, `Ongoing`, and `Today` mark an open
end. If an entry's dates sit inside a paragraph rather than on the title/subtitle lines,
move them next to the role line and re-run.
Can I turn the JSON back into a readable resume?
Yes — that is the point of the standard. Any JSON Resume-compatible theme, builder, or
CLI can render the extracted document to HTML or PDF, and structured-resume tools
(including a JSON-to-Markdown resume builder) accept the same shape. Tick "Include
$schema reference" to embed the official schema URL and a `meta.version` field so other
tools can identify the format immediately.
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