A Cv That Lands You Jobs Effortlessly

name: a-cv-that-lands-you-jobs-effortlessly description: This prompt asks the AI to tailor a candidate’s CV content to a specific job description by generating recruiter-aligned sections: a 3–5 line career summary, a JD-linked skills section with evidence, an action-oriented experience/key projects section, an internship/part-time description framed as measurable value contribution, and a clear career objective with 2–3 year goals. Using the provided JD and existing CV details, the outcome is a set of concise, professional, and role-relevant statements that directly map the candidate’s skills and achievements to the position’s requirements while emphasizing growth mindset, adaptability, and long-term commitment. skillVersionId: 019720bf-9a2e-763a-a5f2-6be765c7780d

What it does

This prompt asks the AI to tailor a candidate’s CV content to a specific job description by generating recruiter-aligned sections: a 3–5 line career summary, a JD-linked skills section with evidence, an action-oriented experience/key projects section, an internship/part-time description framed as measurable value contribution, and a clear career objective with 2–3 year goals. Using the provided JD and existing CV details, the outcome is a set of concise, professional, and role-relevant statements that directly map the candidate’s skills and achievements to the position’s requirements while emphasizing growth mindset, adaptability, and long-term commitment.

Requires the betterprompt CLI to run. For installation and general CLI usage, refer to the betterprompt skill in your skill list or if not yet available in skill list, install via npx skills add https://github.com/BetterPromptme/betterprompt/skills --skill betterprompt

Inputs

textInputs

Pass each text input using --input <key>=<value> flags.

Models and options

This skill's modality is: text.

To discover which model values you can use (and which options keys/values are valid for each model), run:

betterprompt resources --models-only --json

Then filter the returned JSON array to entries where modality is "text".

How to run

Step 1: Collect inputs

First, run betterprompt resources --models-only --json and filter to modality: "text" to discover valid models and available options:

betterprompt resources --models-only --json

Use only the models and option values that appear in the filtered results.

Then collect all inputs from the human:

• Required text inputs:
  • Your_CV
  • JD_of_your_company_apply
• Optional: model and options.
  • Present the human with the default model gemini-2.5-pro-preview and its available options. Look up gemini-2.5-pro-preview in the betterprompt resources output (filtered to modality "text") and show its availableOptions as: key: val1, val2 (default), val3 | key2: .... Mark a value (default) if it matches these defaults: {}.
  • If the human does not specify, defaults are used: model gemini-2.5-pro-preview, options {}. Other models from the resources call are also available.

If any required text input is missing, ask the human for what's missing. Do not assume or fabricate values.

Step 2: Run via BetterPrompt CLI

Use the frontmatter's skillVersionId as the positional argument (for this skill version, use 019720bf-9a2e-763a-a5f2-6be765c7780d).

Command form:

betterprompt generate 019720bf-9a2e-763a-a5f2-6be765c7780d \
  [--input <key>=<value>] \
  [--model <model>] \
  [--options <options JSON>] \
  [--json]

Notes:

• Pass each text input as a separate --input <key>=<value> flag.
• If the human does not mention a model, omit --model and BetterPrompt will use the default model: gemini-2.5-pro-preview.
• If the human does not mention options, omit --options and BetterPrompt will use the default options: {}.
• If the run times out, the response will include a runId you can use to fetch the result later.

Example (using defaults shown above):

betterprompt generate 019720bf-9a2e-763a-a5f2-6be765c7780d \
  --input Your_CV=<value> \
  --input JD_of_your_company_apply=<value> \
  --model gemini-2.5-pro-preview \
  --options '{}'