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Compensation

作者 clawkk · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install compensation
功能描述
Offers, compensation framing, and negotiation planning. Use when evaluating offers or raises.
使用说明 (SKILL.md)

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.
安全使用建议
This skill appears low-risk and consistent with its stated purpose. Before using it: (1) remember it's guidance, not legal or tax advice; (2) avoid pasting employer-identifying confidential documents or sensitive personal identifiers unless you intend them to be processed by the agent; (3) ask the agent for sources when it provides market numbers or salary ranges (to avoid relying on hallucinated figures); and (4) if you prefer the skill not be invoked autonomously, disable or restrict skill permissions in your agent settings. Overall safe to use for structured negotiation planning, but verify critical recommendations independently.
功能分析
Type: OpenClaw Skill Name: compensation Version: 1.0.0 The skill bundle provides a structured framework for compensation negotiation and offer evaluation. The content in SKILL.md is purely procedural guidance for the AI agent, focusing on professional communication and planning without any executable code, network requests, or instructions to access sensitive data.
能力评估
Purpose & Capability
Name, description, and detailed workflow all align: the skill provides staged negotiation and compensation planning guidance and does not request unrelated capabilities or secrets.
Instruction Scope
SKILL.md contains only procedural guidance (questions to ask, stages, checklists). It does not instruct the agent to read files, access environment variables, phone home, or transmit data to third parties beyond normal agent behavior.
Install Mechanism
No install spec and no code files — instruction-only skills pose minimal installation risk because nothing is written to disk or downloaded.
Credentials
No environment variables, credentials, or config paths are requested; the level of requested access is minimal and appropriate for the stated purpose.
Persistence & Privilege
always:false (default) and autonomous invocation is allowed (platform default). The skill does not request persistent or elevated privileges, nor does it modify other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install compensation
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /compensation 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial public release of the "compensation" skill: - Introduces a four-stage workflow for evaluating offers, pay, and negotiation planning. - Provides structured triggers for when to suggest the workflow versus freeform help. - Emphasizes explicit goals, risks, trade-offs, and concrete verification steps. - Includes checklists and best practices for actionable, stage-by-stage guidance. - Adapts flexibly to user preference for workflow depth or freeform advice.
元数据
Slug compensation
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Compensation 是什么?

Offers, compensation framing, and negotiation planning. Use when evaluating offers or raises. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 133 次。

如何安装 Compensation?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install compensation」即可一键安装,无需额外配置。

Compensation 是免费的吗?

是的,Compensation 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Compensation 支持哪些平台?

Compensation 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Compensation?

由 clawkk(@clawkk)开发并维护,当前版本 v1.0.0。

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