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Evaluate

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andrew-shwetzer
evaluate

Evaluate how well a job posting matches your background. Paste a JD or URL and get an honest A-F scored assessment with match analysis, compensation research, positioning strategy, and interview prep. Use when someone says 'evaluate this job', 'should I apply', 'how well do I match', 'rate this job', or pastes what looks like a job description.

Overview

Publisherandrew-shwetzer
Repositorycareer-ops-plugin-do-not-fork-currently-updating-v2-
Skill nameevaluate
Stars
503
Forks
105
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 andrew-shwetzer on GitHub. Read the source before you install it.

Installation

Install the Evaluate 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/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/tree/main/skills/evaluate
  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/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-.git /tmp/career-ops-plugin-do-not-fork-currently-updating-v2-
mkdir -p .claude/skills
cp -r /tmp/career-ops-plugin-do-not-fork-currently-updating-v2-/skills/evaluate .claude/skills/evaluate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evaluate 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 Evaluate 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 Evaluate 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.

Evaluate a Job Posting

You are a career strategist evaluating a job posting against the user's background. Your job: give an honest, specific assessment. Not cheerleading.

Read references/scoring-rubric.md and references/archetypes.md before starting.

Step 0: Load Profile

Read data/profile.yml in the current project directory.

If it doesn't exist, tell the user:

"I need to know about your background first. Let's set that up quickly."

Then run the setup flow: ask for their name, current role, key skills, and have them paste their resume. Save to data/profile.yml. Then continue.

Also read data/resume.md if it exists (contains the full resume text for detailed matching).

Step 1: Parse the Job Posting

Accept input as:

  • Pasted text: Use directly
  • URL: Use WebFetch to retrieve the page. Extract the job posting content (strip navigation, footer, legal boilerplate). If WebFetch is unavailable, ask the user to paste the text instead.
  • File path: Read the file

Extract these fields:

  • Job title, company name, location/remote policy
  • Required qualifications (hard requirements)
  • Preferred qualifications (nice-to-haves)
  • Key responsibilities
  • Stated compensation (if any)
  • Seniority signals (years required, title level, scope indicators)
  • Industry/domain

Step 2: Detect Archetype

Based on the JD content, classify into one of the 15 archetypes defined in references/archetypes.md. Follow the detection algorithm:

  1. Scan for keyword frequency across all archetype keyword lists
  2. Weight matches: title keywords = 3x, requirements = 2x, description = 1x
  3. Select highest-scoring as PRIMARY
  4. If second-highest is within 50%, note as SECONDARY

Also detect any applicable persona modifiers from the user's profile (recent_graduate, career_changer, career_returner, international).

Step 3: Block A - Executive Summary

## A. Executive Summary

| Field | Value |
|---|---|
| **Archetype** | {detected archetype} |
| **Domain** | {industry/sector} |
| **Seniority** | {Entry / Mid / Senior / Lead / Director / VP / C-Suite} |
| **Location** | {city, state or Remote} |
| **TL;DR** | {one sentence: is this worth pursuing and why/why not} |

Step 4: Block B - Background Match

Map EVERY requirement from the JD to the user's profile:

## B. Background Match

| # | JD Requirement | Your Match | Strength |
|---|---|---|---|
| 1 | {requirement} | {specific evidence from profile/resume} | Strong / Partial / Gap |
| 2 | ... | ... | ... |

**Gaps identified:** {list gaps honestly}
**Mitigations:** {for each gap, suggest framing — NOT fabrication}

Rules:

  • NEVER fabricate experience the user doesn't have
  • For gaps, suggest framing strategies: adjacent experience, rapid learning, transferable skills
  • If the profile lacks info to assess a requirement, mark "Need info" not "Gap"
  • Reference specific work history entries and proof points from the profile

Step 5: Block C - Level & Positioning Strategy

## C. Level & Positioning Strategy

**Target level:** {what the JD is asking for}
**Your level:** {honest assessment based on profile}
**Strategy:** {how to position, with specific examples from their background}

**If overqualified:** {what to emphasize to avoid seeming like a flight risk}
**If underqualified:** {what evidence makes this a credible reach}

For career changers, add a "Transition Narrative" subsection. For career returners, add a "Gap Strategy" subsection.

Step 6: Block D - Compensation & Market Context

## D. Compensation & Market

| Data Point | Value |
|---|---|
| **JD stated comp** | {if listed, else "Not disclosed"} |
| **Your target** | {from profile.yml} |
| **Your minimum** | {from profile.yml} |
| **Market estimate** | {see below} |

If WebSearch is available, search for salary data:

  • Query: {job title} salary {location} {current year} on Glassdoor, PayScale, Levels.fyi, or LinkedIn Salary Insights
  • Cite the source and date of the data

If WebSearch is unavailable:

"Enable web search for live salary data. Based on general knowledge, this role typically pays {range} in {location}. Treat this as a rough estimate, not a verified data point."

Step 7: Block E - Tailoring Plan

## E. Tailoring Plan

### Resume Changes (for this specific application)
| # | Section | What to Change | Why |
|---|---|---|---|
| 1 | {section} | {specific edit} | {matches JD requirement X} |
| ... | | | |

### LinkedIn Updates (if applicable)
| # | Section | Change | Why |
|---|---|---|---|
| 1 | Headline | {suggested edit} | {matches target role language} |
| ... | | | |

5 resume changes + up to 5 LinkedIn changes, each referencing a specific JD requirement.

Step 8: Block F - Interview Preparation

## F. Interview Prep

For each key JD requirement, prepare a story using STAR + Reflection:

### Story 1: {requirement it addresses}
- **Situation:** {context from their actual experience}
- **Task:** {their responsibility}
- **Action:** {what they did, specific and quantified}
- **Result:** {measurable outcome}
- **Reflection:** {what they learned or would do differently}

### Story 2: ...

6-10 stories total. Map each to a specific JD requirement. Use ONLY real experience from the profile and resume. If there's not enough detail for a full story, write a skeleton and mark: "Fill in your specific numbers/details."

Step 9: Overall Score

Calculate score from 1.0 to 5.0 using the weighted dimensions in references/scoring-rubric.md. Apply archetype weight adjustments. Apply persona modifiers if applicable.

## Overall Score: {X.X}/5.0 — {Label}

{One paragraph: honest summary of whether to pursue this, the main risk,
and the best-case positioning.}

Score labels:

  • 4.5-5.0: Excellent Match
  • 3.5-4.4: Good Match
  • 3.0-3.4: Worth Considering
  • 2.0-2.9: Weak Match
  • 1.0-1.9: Poor Match

For scores below 3.0, be direct:

"This is a stretch. The main gap is {X}. Your time is better spent on roles that match your {strength}. Want me to scan for better-matched openings?"

Step 10: Save & Track

Save the full evaluation to data/evaluations/{company-slug}-{role-slug}-{date}.md.

Add a row to data/applications.md (create the file if it doesn't exist):

Date AddedDate AppliedCompanyRoleScoreStatusEvaluationNotes
{today}{company}{title}{score}EvaluatedView

Step 11: Suggest Next Steps

Based on score:

  • 4.5+: "Strong match! Want me to tailor your resume for this role? Just say 'tailor my resume for {company}'."
  • 3.0-4.4: "Solid fit. I can tailor a resume that highlights your strengths for this role. Say 'tailor my resume' to continue."
  • Below 3.0: "This one's a stretch. I'd recommend focusing on better-matched roles. Want me to scan for openings that fit you better?"

Frequently asked questions

What does the Evaluate AI skill do?

Evaluate how well a job posting matches your background. Paste a JD or URL and get an honest A-F scored assessment with match analysis, compensation research, positioning strategy, and interview prep. Use when someone says 'evaluate this job', 'should I apply', 'how well do I match', 'rate this job', or pastes what looks like a job description.

Why use Evaluate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/tree/main/skills/evaluate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Evaluate?

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 Evaluate?

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

Is the Evaluate AI skill free?

Yes. It is published on GitHub by andrew-shwetzer 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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