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Job Application

Community
cnfeat
job-application

Tailors a CV and cover letter to a specific job description. Use when asked to write a cover letter, tailor a CV or resume, optimise for ATS, match a job description, or prepare a job application. Produces an ATS-optimised tailored CV summary and a personalised cover letter aligned to the role's requirements.

Overview

Publishercnfeat
Repositorytop-pm-skills
Skill namejob-application
Stars
55
Forks
6
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Job Application 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/cnfeat/top-pm-skills/tree/main/参考skill/pm-claude-skills-main/pm-claude-skills-main/plugins/pm-business/skills/job-application
  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.

Use it in TypingMind

Enable Job Application 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 Job Application 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 Job Application 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.

Job Application Skill

This skill tailors a CV and cover letter to a specific job description — optimising for ATS keyword matching while keeping the writing human and compelling. It also flags gaps between the candidate's profile and the role requirements.

Required Inputs

Ask the user for these if not provided:

  • Job description (paste in full)
  • Current CV / resume (paste or describe key experience, roles, and skills)
  • The specific thing that excites them about this role (used in the cover letter — must be genuine)
  • Any particular strengths to emphasise (optional)
  • Any gaps they're worried about (optional — helps address them proactively)

Output Structure


Part 1: JD Analysis

Before writing anything, analyse the job description and output:

Must-Have Requirements

[List explicit requirements from the JD — qualifications, years of experience, specific skills]

Key Themes in the JD

[3–5 themes that repeat or are emphasised — these are the keywords and priorities the hiring manager cares about most]

ATS Keywords to Include

[List 10–15 specific keywords and phrases from the JD that should appear in the CV and cover letter. Include: tools, methodologies, job titles, skills]

Gaps Assessment

[Honest comparison between the candidate's profile and the JD requirements. Flag: "Strong match" / "Partial match — can be positioned as X" / "Gap — address in cover letter or don't apply"]


Part 2: Tailored CV Summary / Profile Section

Rewrite or create the candidate's CV summary/profile section (the 3–5 lines at the top of a CV) specifically for this role:

Rules:

  • Open with the job title or a near-match (ATS reward)
  • Include 2–3 keywords from the JD naturally
  • Reference years of experience in the relevant area
  • End with a forward-looking line connecting their background to what this role needs
  • Keep to 60–80 words maximum

Tailored CV Summary: [Write the summary]


Part 3: Experience Bullet Point Rewrites

For the 2–3 most relevant roles on the CV, suggest how to reframe existing bullet points to better match this JD:

[Role Title] at [Company]

Original BulletTailored VersionWhy
[Candidate's original text][Improved version with JD keywords and stronger impact framing][Brief note on what changed]

Rules for bullet point rewrites:

  • Lead with an action verb
  • Include a quantified outcome where possible (%, £, time saved, users impacted)
  • Weave in JD keywords naturally — not forced
  • Keep to one line (2 max)

Part 4: Cover Letter

Format: 3 paragraphs + closing. Target: 250–350 words. Anything longer won't be read.


[Hiring Manager's name if known, otherwise "Hiring Team"]

Paragraph 1 — The Hook (Why this role, specifically) [2–4 sentences. Reference something specific about the company or role — not generic enthusiasm. The candidate's genuine reason for applying goes here. This is what makes it human. Generic openers like "I am writing to apply for..." are filtered out mentally within 3 seconds.]

Paragraph 2 — The Evidence (Why them) [3–5 sentences. 2–3 specific examples from their background that directly address the JD's key themes. Use the language of the JD. Include at least one quantified achievement. Don't list everything — pick the 2–3 strongest matches and go deep, not broad.]

Paragraph 3 — The Forward Bridge (Why now) [2–3 sentences. Connect their trajectory to this role. Why is this the logical next step? What do they want to learn or build that this role enables? This should feel like the natural continuation of their career, not just "I want a new challenge."]


I'd welcome the chance to discuss how my background could contribute to [Company/Team]. Thank you for your time.

[Name] [Email] | [LinkedIn URL] | [Location if relevant]


Part 5: Application Checklist

Before submitting:

  • CV summary updated with tailored version above
  • ATS keywords appear in CV body (not just summary)
  • Cover letter is under 400 words
  • Company name is spelled correctly throughout (sounds obvious — it happens)
  • No generic phrases: "passionate about," "results-driven," "team player" without evidence
  • LinkedIn profile updated to match CV (recruiters cross-check)
  • Role title in subject line if emailing directly

Quality Checks

  • JD analysis completed before writing (not skipped)
  • ATS keywords are integrated naturally (not stuffed)
  • Cover letter opens with something specific (not a generic opener)
  • Paragraph 2 includes at least one quantified achievement
  • Cover letter is 250–350 words
  • Gaps are either addressed or strategically omitted

Anti-Patterns

  • Do not fabricate or embellish experience — only use real achievements from the provided CV
  • Do not use the same cover letter template for every role — every letter must reference specific details of the job description
  • Do not address selection criteria that aren't in the JD — match keywords the employer actually used
  • Do not omit ATS optimisation — ensure role-specific keywords from the JD appear naturally in the CV summary
  • Do not write a cover letter that re-summarises the CV — it must add context and motivation, not repeat bullet points

Example Trigger Phrases

  • "Help me apply for this job: [paste JD]"
  • "Tailor my CV for this role: [paste JD + CV]"
  • "Write a cover letter for [role] at [company]"
  • "Optimise my application for ATS for this job description"

Frequently asked questions

What does the Job Application AI skill do?

Tailors a CV and cover letter to a specific job description. Use when asked to write a cover letter, tailor a CV or resume, optimise for ATS, match a job description, or prepare a job application. Produces an ATS-optimised tailored CV summary and a personalised cover letter aligned to the role's requirements.

Why use Job Application on TypingMind?

Because you install it once and use it with any model. Job Application 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 Job Application in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfeat/top-pm-skills/tree/main/参考skill/pm-claude-skills-main/pm-claude-skills-main/plugins/pm-business/skills/job-application. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Job Application?

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 Job Application?

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

Is the Job Application AI skill free?

Yes. It is published on GitHub by cnfeat under the MIT license. 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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