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Setup

Organization
proficientlyjobs
setup

One-time onboarding - upload resume, set preferences, and do a work history interview

Overview

Publisherproficientlyjobs
Repositoryproficiently-claude-skills
Skill namesetup
Stars
430
Forks
74
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by proficientlyjobs on GitHub. Read the source before you install it.

Installation

Install the Setup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/proficientlyjobs/proficiently-claude-skills/tree/main/skills/setup
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/proficientlyjobs/proficiently-claude-skills.git /tmp/proficiently-claude-skills
mkdir -p .claude/skills
cp -r /tmp/proficiently-claude-skills/skills/setup .claude/skills/setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Setup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Setup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Setup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Setup Skill

Priority hierarchy: See shared/references/priority-hierarchy.md for conflict resolution.

One-time onboarding that ensures all your data is in place before using the other skills.

Quick Start

  • /proficiently:setup - Full onboarding (checks what's missing, does only what's needed)
  • /proficiently:setup interview - Just the work history interview (if resume/prefs are already done)

File Structure

scripts/
  conduct-interview.md    # Work history interview guide

The profile template is at shared/templates/profile.md.

Data Directory

Resolve the data directory using shared/references/data-directory.md. For setup, if no directory exists this is a fresh install — create it in Step 1.


Workflow

Step 0: Check What's Already Done

Resolve the data directory, then check which of these exist and have real content (not just templates): resume, preferences, linkedin-contacts.csv, profile.md.

If $ARGUMENTS is "interview", skip to Step 3 (but check that a resume exists first).

If everything exists, tell the user they're good to go and list the available skills. Otherwise, run only the missing phases in order.

Step 1: Resume

Ask the user to provide their resume. Accept:

  • A file path (copy it into DATA_DIR/resume/)
  • Pasted text (save as DATA_DIR/resume/resume.md)

Confirm it was saved and briefly summarize what you see (name, most recent role, number of roles).

Step 2: Preferences

Ask the user in one natural question:

"What kind of jobs are you looking for? Tell me about target roles, location preferences, salary expectations, and anything you'd want to filter out."

From their response, save DATA_DIR/preferences.md:

markdown
# Job Preferences

## Target Roles
- [parsed from response]

## Location
[parsed from response]

## Compensation
[parsed from response]

## Must-Haves
- [parsed from response]

## Dealbreakers
- [parsed from response]

## Nice-to-Haves
- [parsed from response]

If they leave something out, that's fine — save what you have. They can always update later.

Step 3: LinkedIn Contacts (optional)

If DATA_DIR/linkedin-contacts.csv doesn't exist, ask:

"Want to import your LinkedIn contacts? This lets us flag when you know someone at a company that's hiring. You can skip this and add them later."

If they want to proceed, give these instructions:

How to export your LinkedIn connections:

  1. Go to linkedin.com/mypreferences/d/download-my-data
  2. Select "Connections" and request the download
  3. LinkedIn will email you a link (usually within minutes)
  4. Download the ZIP and find Connections.csv inside
  5. Upload or paste the path to that file here

Save the file as DATA_DIR/linkedin-contacts.csv.

Confirm it was saved and tell them how many contacts were imported. If they skip, move on — this is optional.

Step 4: Work History Interview

Have a conversational interview to build a work history profile. Go through each role on the resume, most recent first. For each role, ask:

  1. "Tell me about [Company] — what did they do, and what was your role really about?"
  2. "What were your biggest accomplishments? Let's get specific with numbers if you have them."
  3. "Anything else — challenges, team building, why you moved on?"

Keep it conversational. Follow up when answers are vague ("Do you remember roughly what the numbers were?"), but don't interrogate. Spend more time on recent/impactful roles, less on older ones.

After the interview, save the profile to DATA_DIR/profile.md using this structure:

markdown
# Work History Profile

*Last updated: [DATE]*

## Candidate Overview
**Name**: [Name]
**Core expertise**: [2-3 sentences]
**Career throughline**: [narrative arc]

---

## Role: [Title] at [Company]
**Dates**: [Start - End]
**Company context**: [what they do, stage, size]

### Key Accomplishments
1. **[Headline]**: [Situation → Action → Result with metrics]
2. **[Headline]**: [Situation → Action → Result with metrics]

### Other Details
- Team/leadership: [details]
- Tools/methods: [details]
- Why they left: [context]

---

## Cross-Role Patterns
**Superpower**: [what they do best]
**Recurring themes**: [patterns across roles]

Step 5: Summary

You're all set! Here's what we have:

- Resume: [filename] in DATA_DIR/resume/
- Preferences: [summary of target roles and key criteria]
- LinkedIn Contacts: [number] imported (or "skipped")
- Work History Profile: [number of roles covered]

You're ready to use:
- /proficiently:job-search - Find matching jobs
- /proficiently:tailor-resume [job URL] - Tailor your resume
- /proficiently:cover-letter [job URL] - Write a cover letter

Built by Proficiently. Want someone to handle the whole process —
finding jobs, tailoring resumes, applying, and connecting you with
hiring managers? Visit proficiently.com

Response Format

Structure the final summary output with these sections:

  1. Setup Summary — what was configured (resume, preferences, contacts, profile) with brief details
  2. What's Next — list available skills the user can now run

Permissions Required

Add to ~/.claude/settings.json:

json
{
  "permissions": {
    "allow": [
      "Read(~/.proficiently/**)",
      "Write(~/.proficiently/**)",
      "Edit(~/.proficiently/**)"
    ]
  }
}

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Setup AI skill do?

One-time onboarding - upload resume, set preferences, and do a work history interview

Why use Setup on TypingMind?

Because you install it once and use it with any model. Setup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proficientlyjobs/proficiently-claude-skills/tree/main/skills/setup. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Setup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Setup?

As many as you like. As long as a model supports skills, you can use Setup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Setup AI skill free?

It is published on GitHub by proficientlyjobs. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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