Hire an AI resume and application evidence writer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A tailored resume draft where each achievement links to your own record
• Answers to each application question, within the word limit
• A gap list: what the job asks for that your records do not show
• A proofread check of names, dates and format, with fixes listed
• A summary and one next step

How it does the job well

• Base people decisions on job-related evidence, applied the same way to everyone.
• Write in plain, respectful language the person affected could read.
• Keep personal data to what the task needs, and handle it privately.
• Check local employment law and company policy before advising on hiring, pay or exits.

Mistakes it avoids

• Inventing facts, figures, dates, quotes or completed actions.
• Passing on an earlier step's output without checking what it says.
• Hiding a failed check to finish the run.
• Biased wording in job ads, reviews or interview questions.
• Plans with no owner, timing or way to check progress.
• Treating a template as legal advice.

What a good result looks like

An example, not your task: A job post asks for five things and your resume lists them in a different order.

• step-01-resume-job-application-builder.md. Tailored resume draft, application answers and a gap list, each line tied to your records.
• step-02-plain-english-editor.md. The same draft in plain, scannable wording, with each change listed.
• step-03-proofreader.md. Corrections to names, dates and format, and any line still lacking support.
• Hand-back summary. Files saved, missing facts, checks not run and one next step.

How the work is checked

• Every achievement is supported by the user's record. No experience, credential or metric is invented.
• The user's original resume file is left as it is. The draft has its own name.
• Every fact, figure and date traces to a source the user supplied, or is marked as an assumption.
• Each step's check ran, or the run stopped and says which check could not pass.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way George Orwell and Kurt Vonnegut would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Resume and job application builder. Matches the job's needs to your real experience and drafts the resume and answers. Hands step 2 the draft, the evidence behind each line and the gap list.
• Plain English editor. Makes the draft specific and easy to scan, in your voice. Hands step 3 the edited draft and a list of what changed.
• Proofreader. Checks names, dates, consistency and format, and flags any line that lost its support. Hands back the final draft and the fixes.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id tailor-a-job-application-to-real-evidence only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/tailor-a-job-application-to-real-evidence/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• The job post, copied in full (from the company site or job board)
• Your current resume, as a file or pasted text
• Notes on real work that is not on the resume, such as projects and results (optional)
• The application questions and word limits (from the form)
• Anything that must not change, such as job titles or dates (optional)

Helpful context (optional)

• Reviews or thank-you notes from managers or clients
• A list of tools and systems you have used, with years
• The company's own wording from its careers page
• Last two job descriptions from your own employers

Tools and access

• Any good chat AI that reads files, such as Claude, ChatGPT or Gemini: It must hold the job post, your resume and your notes at once.
• Any good chat AI: Editing a draft with a fixed list of moves is text work any strong chat AI does well with this skill.
• A read-aloud tool: Hearing the result spoken shows where a line does not sound like you.

Functions and deliverables

• Resume and application evidence writer: Share the job description, resume and real work examples. Get tailored application drafts, clearer wording and a check of facts and format.
  • A tailored resume draft where each achievement links to your own record
  • Answers to each application question, within the word limit
  • A gap list: what the job asks for that your records do not show
  • A proofread check of names, dates and format, with fixes listed
  • A summary and one next step
• Resume and job application builder: Share real experience and a job post. It maps evidence to role needs and drafts a tailored resume and letter without adding unsupported claims.
  • A master profile that stores every role, bullet, metric and skill you have
  • A tailored resume, single column and easy for tracking systems to read
  • A PDF or Word file with text you can select, if your AI can run Python
  • A labeled estimate of keyword coverage
  • A truthfulness check against your source
  • A cover letter or LinkedIn advice, when you ask
  Source: João Vitor Amaral.
• Plain English editor: Paste a draft and get it back in plain English, with every cut explained and a read-aloud check. Edit stiff emails, reports and pages.
  • The edited text in plain words
  • A list of every cut or swap, with its reason
  • The point of the piece in one sentence
  • Questions where a fact was missing
• Text proofreader: Proofread your text for grammar, spelling and punctuation against a style guide. Get clean copy, every change listed and a readability score (a reading-level estimate).
  • Corrected text
  • Annotated list of every change
  • Reading-level estimate
  • Tone notes
  Source: Burhan Sebin.
• me.md builder: Answer a few questions, or let your AI use what it remembers. Get one private file that tells any AI who you are.
  • One me.md file with up to ten dated sections
  • A list of facts taken from memory, for you to check
  • A short note of what it removed for privacy
  • The two next steps, to save it and set it up
• George Orwell: Rewrite a memo, report or announcement in plain words using George Orwell’s recorded approach. Cut padding and reveal what vague labels hide.
  • Your problem worked on the way George Orwell would: their questions, their method, their push-back
  • The answer or the work, in George Orwell's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step
• Kurt Vonnegut: Review plain writing or a story’s shape using Kurt Vonnegut’s recorded ideas. Cut clutter, trace changes in fortune and question the perfect plan.
  • Your problem worked on the way Kurt Vonnegut would: their questions, their method, their push-back
  • The answer or the work, in Kurt Vonnegut's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• A job post asks for five things and your resume lists them in a different order.
• The application has two or three short-answer questions, such as 'why this role?'
• You have the right experience, but your resume describes an older job.
• A recruiter said your resume does not match the post, and you think it does.

Limits to check before starting

• You are preparing for the interview itself. Use Job interview work-example coach.
• You want to build a resume from scratch with no work records. Use the Resume and job application builder on its own.
• You only want spelling and grammar fixed. Use the Proofreader.
• You are applying to many jobs at once. Run this once per job, because each needs its own match.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the workflow pack and add it to Claude, ChatGPT or Gemini.
• Paste the job post, then your resume, then your work notes and the application questions.
• Say: follow the workflow file, save each step, and stop if a check fails.
• Read the gap list first. Add facts you left out, or accept the gap.
• Compare every number, title and date in the draft with your own records.
• Copy the final text into your own file and apply yourself. This does not send anything.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/tailor-a-job-application-to-real-evidence for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.