技能详情（站内镜像，无评论）
作者：danyangliu @danyangliu-sandwichlab
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 196 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：danyangliu-sandwichlab/ads-data-query-assistant
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
This instruction-only skill is internally consistent with its stated purpose (translating ad queries into metrics, findings, and a handoff payload) and does not request credentials, installs, or file/system access.
目的
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 — th…
说明范围
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.
安装机制
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.
证书
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.
持久
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 elevat…
综合结论
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 tha…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Ads Data Query」。简介：Run natural-language data query workflows for Meta (Facebook/Instagram), Google…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/danyangliu-sandwichlab/ads-data-query-assistant/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
name: ads-data-query-assistant
description: Run natural-language data query workflows for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and Shopify Ads reports.
---
# 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