Ads Audience Targeting
/install audience-segmentation-analyst
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:
- Intent Summary (goal, KPI, scope)
- Findings (key observations and assumptions)
- Action Plan (prioritized next steps)
- Risks and Guardrails (what can break and what to monitor)
- Handoff Payload (structured fields for downstream skills)
Workflow
- Normalize request and confirm objective.
- Validate available inputs and list missing critical data.
- Analyze according to this skill focus: icp segmentation, audience labels, exclusion strategy.
- Generate prioritized actions tied to KPI impact.
- Add platform-specific notes and constraints.
- 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
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install audience-segmentation-analyst - 安装完成后,直接呼叫该 Skill 的名称或使用
/audience-segmentation-analyst触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
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。