Agent Skillsnowork-studio/notfair-plugin › paid-ads-linkedin

paid-ads-linkedin

GitHub

用于审计、诊断和运营 LinkedIn 广告账户的 Skill,支持通过 MCP 连接或导出文件获取数据,涵盖投放策略规划、性能分析及安全执行变更。

paid-ads/paid-ads-linkedin/SKILL.md nowork-studio/notfair-plugin

Trigger Scenarios

LinkedIn 广告投放优化 广告账户性能审计 Lead 生成质量分析 预算与出价调整

Install

npx skills add nowork-studio/notfair-plugin --skill paid-ads-linkedin -g -y
More Options

Non-standard path

npx skills add https://github.com/nowork-studio/notfair-plugin/tree/main/paid-ads/paid-ads-linkedin -g -y

Use without installing

npx skills use nowork-studio/notfair-plugin@paid-ads-linkedin

指定 Agent (Claude Code)

npx skills add nowork-studio/notfair-plugin --skill paid-ads-linkedin -a claude-code -g -y

安装 repo 全部 skill

npx skills add nowork-studio/notfair-plugin --all -g -y

预览 repo 内 skill

npx skills add nowork-studio/notfair-plugin --list

SKILL.md

Frontmatter
{
    "name": "paid-ads-linkedin",
    "description": "Audit, diagnose, plan, and safely operate connected LinkedIn Ads accounts through the NotFair MCP, with an export-based fallback. Use for LinkedIn advertising, sponsored content, lead-generation forms, job-title or company targeting, campaign groups, creatives, conversions, lead quality, budgets, bids, or approved LinkedIn campaign changes.",
    "argument-hint": "<B2B goal, audience, account, or date range>"
}

LinkedIn Ads

Read ../shared/operating-contract.md and ../shared/measurement-framework.md before acting. Use live account data from the connected platform; use a supplied export when no verified connector is available.

Establish access and qualified-demand context

  1. Follow ../../docs/mcp-connection.md. Resolve ~~linkedin-ads to the live connection. Use its current instructions and capability descriptions to choose tools, and verify the requested platform and account from live data. Do not infer access from another connected platform.
  2. If the connector is missing or unauthorized, request re-authorization or a current export and keep the result plan/review-only.
  3. Define the sales-qualified conversion, CRM feedback loop, account currency, attribution basis, target CPA or pipeline outcome, and complete date window before diagnosing performance.

Keep lead quantity separate from lead quality. Build targeting hypotheses from job function, seniority, company, industry, or account lists only when the business rationale and audience constraints are defensible.

Read and diagnose

Correlate campaign groups, campaigns, creatives, and analytics as needed. Choose available capabilities for individual objects, conversion rules, lead forms, targeting information, or lead responses; retrieve only what the question needs.

Interpret the platform correctly:

  • Hierarchy is account → campaign group → campaign → creative.
  • Money is returned as a major-unit object such as { amount: "50", currencyCode: "USD" }, not micros or cents.
  • Targeting is a whole tree on the campaign. Preserve existing criteria unless the user explicitly approves replacement.
  • Campaign type and cost type are immutable after creation.
  • Lead-form responses contain personal data. Retrieve only when necessary, minimize exposure in the response, and never copy raw lead PII into unrelated artifacts.

For reviews, report spend, impressions, link CTR, leads, qualified leads, CPA, and downstream pipeline or revenue by a complete equivalent period. Name the likely driver only when the data supports it.

Execute approved changes safely

Use dedicated write tools, never the read-only script surface. Show the exact object, current and proposed state, currency exposure, expected effect, and rollback first. Use dry-run previews for spend-affecting creates, budgets, bids, and targeting when available.

  • Prefer pause/activate over hard deletion; conversion rules and matched audiences may not be deletable through the API.
  • Create campaign groups, campaigns, and creatives in draft, then review targeting, budget, conversion association, and creative before activation.
  • Use a stable client request ID only to retry the same uncertain create.
  • Resolve targeting names to LinkedIn URNs before setting the full targeting tree.
  • Hashing and event-shape enforcement belong to the connector. Do not expose raw customer identifiers in the final report.
  • Verify the mutation through returned before/after evidence or a fresh read and report any partial failure.

Finish with the confirmed action, quality metric, observation window, and rollback trigger. If operating from an export, mark recommendations ready_for_review, never published.

Version History

  • daf87d3 Current 2026-09-09 06:24

    从通用 NotFair MCP 迁移至专用 linkedin-ads 连接,细化了工具调用路径与访问验证流程。

  • 8d9d54b 2026-08-19 23:11

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Version
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Hash
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Indexed
2026-08-19 23:11

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