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Ads Audience Targeting

作者 danyangliu · GitHub ↗ · v1.0.0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install audience-segmentation-analyst
功能描述
Build audience segmentation and targeting plans for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, and DSP/programmatic campaigns.
使用说明 (SKILL.md)

Ads Audience Targeting

Purpose

Define ICP segments, audience labels, exclusions, and targeting hypotheses that are ready for ad setup.

When To Trigger

Use this skill when the user asks to:

  • run ads or execute advertising campaigns with clear operational next steps
  • grow revenue or profit, improve roas, reduce cpa, or optimize budget and bidding
  • analyze market, traffic, conversion funnel, and campaign performance signals
  • apply this specific capability: icp segmentation, audience labels, exclusion strategy

Typical trigger keywords:

  • ads, advertising, campaign, growth, strategy
  • revenue, profit, roi, roas, cpa
  • budget, bidding, traffic, conversion, funnel
  • meta, googleads, tiktokads, youtubeads, amazonads, shopifyads, dsp

Input Contract

Required:

  • business_goal: primary objective (sales, leads, traffic, awareness, retention)
  • scope: campaign range, market, timeline, and platform scope
  • context: URL, account context, historical performance, or request text

Optional:

  • kpi_targets: target cpa, roas, revenue, roi, ltv, cvr
  • constraints: budget, policy, brand rules, timeline, resource limits
  • platform_preference: preferred channels and priority
  • baseline_metrics: existing benchmark metrics

Output Contract

Return an execution-ready result with:

  1. Intent Summary (goal, KPI, scope)
  2. Findings (key observations and assumptions)
  3. Action Plan (prioritized next steps)
  4. Risks and Guardrails (what can break and what to monitor)
  5. Handoff Payload (structured fields for downstream skills)

Workflow

  1. Normalize request and confirm objective.
  2. Validate available inputs and list missing critical data.
  3. Analyze according to this skill focus: icp segmentation, audience labels, exclusion strategy.
  4. Generate prioritized actions tied to KPI impact.
  5. Add platform-specific notes and constraints.
  6. Emit a compact handoff payload for execution.

Decision Rules

  • If KPI is missing, infer likely primary KPI from goal and mark assumption explicitly.
  • If data quality is low, return conservative recommendations and required follow-up checks.
  • If platform context is unclear, provide platform-agnostic baseline plus channel variants.
  • If policy or account risk appears high, require compliance or account checks before scale.
  • If urgency is high and uncertainty is high, prioritize reversible low-risk actions first.

Platform Notes

Primary platform scope:

  • Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, DSP/programmatic

Guidance:

  • Use platform-specific recommendations only when evidence supports them.
  • Keep naming explicit: Meta, Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP.
  • If request is cross-channel, provide channel order and budget split rationale.

Constraints And Guardrails

  • Do not fabricate data, performance outcomes, or policy approvals.
  • Separate facts from assumptions in every recommendation.
  • Keep recommendations measurable and tied to explicit KPIs.
  • Avoid irreversible changes without validation checkpoints.

Failure Handling And Escalation

  • If required inputs are missing, request concise follow-up fields before final recommendation.
  • If data sources conflict, report conflict and provide a safe default path.
  • If request implies unsupported account actions, escalate with an exact handoff checklist.
  • If compliance risk is detected, route to Ads Compliance Review before launch.

Examples

Example 1: Meta ecommerce optimization

Input:

  • Goal: sales growth with lower cpa
  • Platform: Meta (Facebook/Instagram)

Output focus:

  • top blockers
  • prioritized fixes
  • week-1 actions and expected KPI movement

Example 2: Google Ads lead generation

Input:

  • Goal: improve lead quality and stabilize cpl
  • Platform: Google Ads

Output focus:

  • search intent structure
  • budget and bidding adjustments
  • lead-routing handoff fields

Example 3: TikTok plus YouTube scale test

Input:

  • Goal: scale traffic while protecting roas
  • Platforms: TikTok Ads and YouTube Ads

Output focus:

  • test matrix
  • risk guardrails
  • monitoring and rollback triggers

Quality Checklist

  • All required sections are present
  • At least 3 registry keywords appear in When To Trigger
  • Input and output contracts are explicit and actionable
  • Workflow is step-based and execution ready
  • Platform references are concrete when applicable
  • At least 3 examples are included
安全使用建议
This skill appears coherent and low-risk: it only needs marketing/account context to produce audience segmentation recommendations and does not request credentials or install software. Before using it, ensure you: (1) do not paste sensitive account credentials or access tokens into prompts — provide high-level performance metrics or anonymized examples instead; (2) verify the skill's source or owner if you need to share real account IDs or proprietary customer data; (3) test with non-sensitive sample data to confirm outputs meet your compliance/privacy needs; and (4) if handing off actions to execution systems, ensure those downstream integrations ask separately for needed API credentials under your control.
功能分析
Type: OpenClaw Skill Name: audience-segmentation-analyst Version: 1.0.0 The skill bundle contains standard metadata files and a SKILL.md file. The SKILL.md defines the purpose, workflow, input/output contracts, and decision rules for an AI agent focused on audience segmentation and targeting for advertising. Crucially, the 'Constraints And Guardrails' section explicitly instructs the agent not to fabricate data, make irreversible changes without validation, or separate facts from assumptions, which are positive indicators against prompt injection. There is no evidence of malicious intent, data exfiltration, unauthorized execution, or any other high-risk behaviors.
能力评估
Purpose & Capability
Name/description (audience segmentation and targeting for ad platforms) aligns with the SKILL.md instructions: it asks for campaign goals, scope, context, KPIs and produces segmentation, action plans, and handoff payloads. No unrelated capabilities or external services are requested.
Instruction Scope
Runtime instructions are limited to marketing inputs (business_goal, scope, context, metrics) and generating recommendations, platform notes, and handoff fields. The SKILL.md does not instruct the agent to read system files, environment variables, credentials, or transmit data to unexpected endpoints.
Install Mechanism
This is an instruction-only skill with no install spec and no code files, so nothing is written to disk or installed. That is the lowest-risk install pattern and appropriate for a guidance/analysis skill.
Credentials
The skill declares no required environment variables, credentials, or config paths. The inputs it requests (account context, historical performance) are appropriate for an ad-targeting planner, but they may include sensitive account data provided by the user — the skill itself does not demand secrets or keys.
Persistence & Privilege
always is false and model invocation is allowed (default). The skill does not request persistent system presence, nor does it modify other skills or system 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 audience-segmentation-analyst
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /audience-segmentation-analyst 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 release of audience-segmentation-analyst skill. - Supports building segmentation and targeting plans for Meta, Google Ads, TikTok Ads, YouTube Ads, and DSP/programmatic campaigns. - Defines clear input/output contracts and step-based workflow for ad audience targeting. - Includes platform-specific guidance, risk handling, and escalation protocols. - Provides actionable examples and a comprehensive quality checklist.
元数据
Slug audience-segmentation-analyst
版本 1.0.0
许可证
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Ads Audience Targeting 是什么?

Build audience segmentation and targeting plans for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, and DSP/programmatic campaigns. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 348 次。

如何安装 Ads Audience Targeting?

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

Ads Audience Targeting 是免费的吗?

是的,Ads Audience Targeting 完全免费(开源免费),可自由下载、安装和使用。

Ads Audience Targeting 支持哪些平台?

Ads Audience Targeting 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 Ads Audience Targeting?

由 danyangliu(@danyangliu-sandwichlab)开发并维护,当前版本 v1.0.0。

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