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Ads Data Query

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 ads-data-query-assistant
功能描述
Run natural-language data query workflows for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads reports.
使用说明 (SKILL.md)

Ads Data Query

Purpose

Translate questions into precise metric pulls and decision-ready summaries.

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: query translation, metric extraction, decision summary

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: query translation, metric extraction, decision summary.
  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, Amazon Ads, Shopify Ads

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 looks coherent and low-risk: it only contains instructions for translating ad-related requests into actionable analysis and handoff payloads and does not request credentials or install code. Before installing: 1) Confirm you trust the skill publisher (source is unknown). 2) If you plan to enable autonomous agent actions, restrict which skills have direct access to ad account credentials — this skill can generate handoff payloads that other skills could use to execute changes. 3) Review any handoff payloads before they are acted on, and test recommendations in a staging account. 4) Ensure compliance with advertising platform policies and data/privacy rules when sharing account-level data with any skill.
功能分析
Type: OpenClaw Skill Name: ads-data-query-assistant Version: 1.0.0 The skill bundle is benign. All files consist of standard metadata and a well-structured `SKILL.md` documentation. The `SKILL.md` explicitly defines the skill's purpose, workflow, and includes multiple guardrails and constraints (e.g., 'Do not fabricate data', 'Avoid irreversible changes without validation checkpoints') that actively prevent malicious prompt injection or harmful agent behavior. There is no evidence of data exfiltration, malicious execution, persistence mechanisms, obfuscation, or any other indicators of compromise.
能力评估
Purpose & Capability
The name/description (ads data query and metric extraction across ad platforms) aligns with the SKILL.md: it defines inputs, outputs, workflows, and platform-specific notes. The only minor ambiguity: the 'When To Trigger' language includes 'run ads or execute advertising campaigns with clear operational next steps', but the skill's instructions describe analysis and handoff payloads rather than actually performing account-level operations — this is consistent with no credentials or execution steps being requested.
Instruction Scope
SKILL.md contains concrete runtime instructions limited to normalizing inputs, validating data, translating queries to metric pulls, producing prioritized actions, guardrails, and a handoff payload. It does not instruct reading arbitrary files, accessing environment variables, calling unknown endpoints, or exfiltrating data. The instructions are explicit and scoped to analysis/handoff rather than direct account manipulation.
Install Mechanism
No install spec and no code files — this is an instruction-only skill. That is the lowest-risk install mechanism and matches the described behavior.
Credentials
The skill declares no required environment variables, no primary credential, and no config paths. This is proportionate to an analysis/handoff skill which should not need direct account credentials. There are no hidden or undeclared env accesses in SKILL.md.
Persistence & Privilege
always is false (not force-included) and disable-model-invocation is false (the agent may invoke autonomously). Autonomous invocation is the platform default and not inherently suspicious, but because the skill produces structured handoff payloads intended for downstream execution, users should be cautious about pairing it with other skills that have direct account credentials or execution capabilities. The skill itself does not request elevated persistence or system modification rights.
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 ads-data-query-assistant
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ads-data-query-assistant 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 ads-data-query-assistant skill. - Enables natural-language data query workflows for Meta, Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads. - Structured input/output contracts for execution-ready insights and actionable campaign recommendations. - Includes robust workflow steps, decision rules, platform-specific notes, and failure handling logic. - Provides platform examples to illustrate output focus for Meta, Google Ads, and TikTok/YouTube cross-channel scenarios.
元数据
Slug ads-data-query-assistant
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 Data Query 是什么?

Run natural-language data query workflows for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads reports. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 321 次。

如何安装 Ads Data Query?

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

Ads Data Query 是免费的吗?

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

Ads Data Query 支持哪些平台?

Ads Data Query 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 Data Query?

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

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