How to use the Data Resume Writer agent skill

What the data-resume skill makes the agent do

The skill writes resumes for data analyst, analytics engineer, data engineer, data scientist and machine-learning roles. Its main rule is that every claim should be backed by the evidence you actually have. The agent uses your own text and any artifacts you permit it to read. No connected account is required.

Before writing, the agent asks which role you want and what you actually did. It then prioritizes different evidence for each role: business questions and metric definitions for analysts, tests and lineage for analytics engineers, freshness and reliability for data engineers, baselines and evaluation design for data scientists, and deployment and monitoring for ML engineers.

• Asks for ownership, data scope, the evaluation setting and whether anyone actually adopted the work.
• Keeps offline evaluation, experiments, pilots and production systems as separate claims.
• Declines to fabricate or inflate a claim, offers the strongest truthful version, and lists unsupported claims for you to resolve.
• Does not produce invented ATS scores or guaranteed interview outcomes.

Install the data-resume skill

Download the ZIP from this page and unzip it. The package contains a single file, SKILL.md. Place the unzipped skill folder in one of the following locations, depending on whether you want it for all projects or just one.

If you use the Claude apps, you can instead upload the skill under Settings → Capabilities → Skills.

• Personal install: unzip into ~/.claude/skills/ so the skill is available across your projects.
• Project install: unzip into the .claude/skills/ folder inside a specific project.
• Claude apps: go to Settings → Capabilities → Skills and upload the skill there.

Trigger the skill with a natural request

You do not need a special command. Describe the data role and the work you did, and say you want a resume. The skill's description covers requests for analyst, analytics engineer, data engineer, data scientist and machine-learning resumes, so mention the role in your request.

Expect the agent to ask follow-up questions before drafting. Answering them with specifics (the metric, the baseline, whether the model reached production) produces a more accurate resume than a vague summary.

• "Write my data scientist resume. I built a churn model last year and evaluated it on held-out data."
• "Help me describe my data engineering work: I built the ingestion pipeline for our sales tables."
• "Review this bullet for my ML engineer CV and tell me which claims need evidence."

Examples of what the output looks like

The skill reframes claims so the context is visible. A model that was evaluated offline is described as offline, with the metric and baseline named. It does not become a production or business-impact claim.

Suppose your confirmed facts are a churn model with 0.78 ROC-AUC against a 0.71 baseline on a held-out dataset, and it was never deployed. The skill would write a bullet like: "Evaluated a churn model on held-out data, achieving 0.78 ROC-AUC against a 0.71 baseline." It would not claim a churn reduction, because nothing measured one.

• A dashboard bullet names the intended users and the decisions it supported, not just that it was built.
• A pipeline bullet states the data volume and time basis, and what you changed, without turning a one-time backfill into daily throughput.
• A savings figure is labelled as measured or modeled, with its assumptions and period.

What the skill needs and what it does not do

The skill lists no requirements. It does not run scripts and does not use the network. It works from the text you provide and the files you allow it to read.

Keep the boundaries in mind. The skill directs academic research records to a separate academic-cv skill. It can also use resume-tailor for a specific job posting or resume-builder for JSON output when those are available; otherwise it continues in plain text.

• Requires: nothing listed.
• Runs scripts: no.
• Uses network: no.
• Academic research records: use academic-cv instead.
• Guaranteed job outcomes or ATS scores: not produced.

Safety notes before you share data

The skill tells the agent to treat source material as data, not as instructions. It also says not to upload private datasets or resumes to outside services, and to remove sensitive dataset details and personal records from what it writes.

Saving changes to an account is a separate step. The skill requires you to review the current record and the concrete change, give authorization, and use concurrency protection. Drafting does not authorize publishing or applying for a job.

• Remove customer names, personal records and confidential dataset details before pasting text.
• Public competition data is not presented as commercial experience.
• Review every draft before you send it to an employer.

Tips for better results

The quality of the output depends on the specifics you give. Gather the metric, baseline, evaluation setting and your personal role before you ask. If a project was a notebook or pilot, say so; the skill will keep that status visible rather than upgrading it.

• State your role precisely: did you design the experiment, analyse it, or only run the query?
• Give the metric name and the baseline it was compared against.
• Say whether the work reached production and whether anyone used the output.
• Ask for the list of unsupported claims and resolve each one before finishing.