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Compensation

Community
jiaxiaojunQAQ
compensation

Offers, compensation framing, and negotiation planning. Use when evaluating offers or raises.

Overview

PublisherjiaxiaojunQAQ
RepositorySkillJect
Skill namecompensation
Stars
79
Forks
8
Bundled files
1
Links
Skill sourceRepository
  • 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 jiaxiaojunQAQ on GitHub. Read the source before you install it.

Installation

Install the Compensation 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.
  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/jiaxiaojunQAQ/SkillJect.git /tmp/SkillJect
mkdir -p .claude/skills
cp -r /tmp/SkillJect/data/skills_sample/compensation-1.0.0 .claude/skills/compensation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compensation 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 Compensation 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 Compensation 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.

Compensation

Structured guidance for offers, pay, and negotiation (not legal or tax advice): confirm triggers, propose the stages below, and adapt if the user wants a lighter pass.

When to Offer This Workflow

Trigger conditions:

  • User mentions negotiation, offers, raises, compensation, or closely related work
  • They want a structured workflow rather than ad-hoc tips
  • They are preparing a review, rollout, or stakeholder communication

Initial offer: Explain the four stages briefly and ask whether to follow this workflow or work freeform. If they decline, continue in their preferred style.

Workflow Stages

Stage 1: Clarify context & goals

Anchor on market context and priorities. Ask what success looks like, constraints, and what must not break. Capture unknowns early.

Stage 2: Design or plan the approach

Translate goals into a concrete plan around total comp components. Compare alternatives and explicit trade-offs; avoid implicit assumptions.

Stage 3: Implement, validate, and harden

Execute with verification loops tied to negotiation script and BATNA. Prefer small steps, measurable checks, and rollback points where risk is high.

Stage 4: Operate, communicate, and iterate

Close the loop with written follow-ups: monitoring, documentation, stakeholder updates, and lessons learned for the next cycle.

Checklist Before Completion

  • Goals and constraints are explicit for compensation discussions
  • Risks and trade-offs are stated, not hand-waved
  • Verification steps match the change’s impact (tests, canary, peer review)
  • Operational follow-through is covered (monitoring, docs, owners)

Tips for Effective Guidance

  • Be procedural: stage-by-stage, with clear exit criteria
  • Ask for missing context (environment, scale, deadlines) before prescribing
  • Prefer checklists and concrete examples over generic platitudes
  • If the user declines the workflow, switch to freeform help without lecturing

Handling Deviations

  • If the user wants to skip a stage: confirm and continue with what they need.
  • If context is missing: ask targeted questions before strong recommendations.
  • Prefer concrete examples, trade-offs, and verification steps over generic advice.

Quality Bar

  • Each recommendation should be actionable (what to do next).
  • Call out failure modes relevant to compensation talks (relationship risk, miscommunication, or unrealistic asks).
  • Keep tone direct and respectful of the user’s time.

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 Compensation AI skill do?

Offers, compensation framing, and negotiation planning. Use when evaluating offers or raises.

Why use Compensation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jiaxiaojunQAQ/SkillJect/tree/main/data/skills_sample/compensation-1.0.0. 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 Compensation?

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 Compensation?

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

Is the Compensation AI skill free?

It is published on GitHub by jiaxiaojunQAQ. 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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