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campaign-retrospective-analyst

Run retrospective analysis for campaigns on Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and DSP/programmatic channels.

danyangliu-sandwichlab0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install danyangliu-sandwichlab/campaign-retrospective-analyst

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

Ads Campaign Review

Purpose

Core mission:

  • root-cause analysis, lesson extraction, next-cycle design

This skill is specialized for advertising workflows and should output actionable plans rather than generic advice.

When To Trigger

Use this skill when the user asks for:

  • ad execution guidance tied to business outcomes
  • growth decisions involving revenue, roas, cpa, or budget efficiency
  • platform-level actions for: Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic
  • this specific capability: root-cause analysis, lesson extraction, next-cycle design

High-signal keywords:

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

Input Contract

Required:

  • question_or_report_goal
  • metric_scope: KPI, dimensions, and date range
  • data_source_scope

Optional:

  • attribution_window
  • benchmark_reference
  • dashboard_filters
  • confidence_threshold

Output Contract

  1. Metric Definition Clarification
  2. Query Plan
  3. Result Summary
  4. Interpretation and Caveats
  5. Decision Recommendation

Workflow

  1. Disambiguate metric definitions and time window.
  2. Build query slices by platform, funnel, and audience.
  3. Compute trend deltas and variance drivers.
  4. Summarize findings with confidence level.
  5. Propose concrete next actions.

Decision Rules

  • If metric definitions conflict, lock one canonical definition before analysis.
  • If sample size is small, mark result as directional not conclusive.
  • If attribution changes materially alter result, show both views.

Platform Notes

Primary scope:

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

Platform behavior guidance:

  • Keep recommendations channel-aware; do not collapse all channels into one generic plan.
  • For Meta and TikTok Ads, prioritize creative testing cadence.
  • For Google Ads an...

Source information

Source
github
Repository
openclaw/skills
Path
skills/danyangliu-sandwichlab/campaign-retrospective-analyst
Version
—
Commit
4a62246c913435cdfe6ad7f66189a80c8afb1f32
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# Ads Campaign Review

## Purpose
Core mission:
- root-cause analysis, lesson extraction, next-cycle design

This skill is specialized for advertising workflows and should output actionable plans rather than generic advice.

## When To Trigger
Use this skill when the user asks for:
- ad execution guidance tied to business outcomes
- growth decisions involving revenue, roas, cpa, or budget efficiency
- platform-level actions for: Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic
- this specific capability: root-cause analysis, lesson extraction, next-cycle design

High-signal keywords:
- ads, advertising, campaign, growth, revenue, profit
- roas, cpa, roi, budget, bidding, traffic, conversion, funnel
- meta, googleads, tiktokads, youtubeads, amazonads, shopifyads, dsp

## Input Contract
Required:
- question_or_report_goal
- metric_scope: KPI, dimensions, and date range
- data_source_scope

Optional:
- attribution_window
- benchmark_reference
- dashboard_filters
- confidence_threshold

## Output Contract
1. Metric Definition Clarification
2. Query Plan
3. Result Summary
4. Interpretation and Caveats
5. Decision Recommendation

## Workflow
1. Disambiguate metric definitions and time window.
2. Build query slices by platform, funnel, and audience.
3. Compute trend deltas and variance drivers.
4. Summarize findings with confidence level.
5. Propose concrete next actions.

## Decision Rules
- If metric definitions conflict, lock one canonical definition before analysis.
- If sample size is small, mark result as directional not conclusive.
- If attribution changes materially alter result, show both views.

## Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic

Platform behavior guidance:
- Keep recommendations channel-aware; do not collapse all channels into one generic plan.
- For Meta and TikTok Ads, prioritize creative testing cadence.
- For Google Ads an...

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