performance-digest
Generate executive-ready marketing performance summaries with insights, trends, and prioritized recommendations
Best use case
performance-digest is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Generate executive-ready marketing performance summaries with insights, trends, and prioritized recommendations
Teams using performance-digest should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/performance-digest/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How performance-digest Compares
| Feature / Agent | performance-digest | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate executive-ready marketing performance summaries with insights, trends, and prioritized recommendations
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
SKILL.md Source
# performance-digest
Generate executive-ready performance summaries with insights and recommendations.
## Triggers
Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):
- "KPIs" / "MQL" / "SQL" / "CAC" / "LTV" → marketing metrics shorthand
- "funnel report" → marketing funnel analysis
## Purpose
This skill generates clear, actionable performance summaries by:
- Aggregating metrics across all marketing channels
- Highlighting key wins and areas of concern
- Providing context through comparisons and trends
- Translating data into strategic insights
- Delivering recommendations with priority
## Behavior
When triggered, this skill:
1. **Determines report scope**:
- Time period (daily, weekly, monthly, quarterly)
- Audience level (team, manager, executive)
- Focus area (overall, channel, campaign)
2. **Aggregates metrics**:
- Pull data from data-pipeline
- Calculate period-over-period changes
- Compare against targets
3. **Identifies highlights**:
- Top performers
- Underperformers
- Anomalies and outliers
- Trend shifts
4. **Generates insights**:
- Why metrics moved
- What it means for business
- What action to take
5. **Formats for audience**:
- Executive: High-level, strategic
- Manager: Tactical, actionable
- Team: Detailed, operational
## Report Types
### Daily Digest
```yaml
daily_digest:
audience: marketing_team
time: 9:00 AM
length: 2 minutes read
sections:
- yesterday_snapshot
- notable_changes
- today_priorities
- quick_wins
metrics:
- spend_vs_budget
- conversions
- anomalies
```
### Weekly Summary
```yaml
weekly_summary:
audience: marketing_manager
time: Monday 8:00 AM
length: 5 minutes read
sections:
- week_performance
- channel_breakdown
- campaign_highlights
- next_week_focus
metrics:
- all_core_kpis
- week_over_week
- trend_analysis
```
### Monthly Report
```yaml
monthly_report:
audience: marketing_leadership
time: 1st of month
length: 10 minutes read
sections:
- executive_summary
- goal_progress
- channel_performance
- campaign_analysis
- competitive_context
- recommendations
metrics:
- all_kpis
- month_over_month
- year_over_year
- target_vs_actual
```
### Quarterly Review
```yaml
quarterly_review:
audience: c_suite
time: End of quarter
length: 15 minutes read
sections:
- quarter_highlights
- business_impact
- market_position
- strategic_progress
- next_quarter_plan
- investment_request
metrics:
- revenue_impact
- market_share
- brand_metrics
- efficiency_ratios
```
## Report Templates
### Executive Summary Template
```markdown
# Marketing Performance Summary
**Period**: [Date Range]
**Prepared For**: [Audience]
**Prepared By**: performance-digest skill
---
## At a Glance
| KPI | Actual | Target | Status |
|-----|--------|--------|--------|
| Revenue | $X | $Y | ✅ 110% |
| New Customers | X | Y | ⚠️ 95% |
| CAC | $X | $Y | ✅ -8% |
| ROAS | X.Xx | Y.Yx | ❌ 85% |
**Overall Status**: On Track / At Risk / Behind
---
## Key Wins 🎯
1. **[Win Title]**
- Result: [Metric achieved]
- Impact: [Business impact]
- Credit: [Team/campaign]
2. **[Win Title]**
- Result: [Metric achieved]
- Impact: [Business impact]
---
## Areas of Concern ⚠️
1. **[Issue Title]**
- Current: [Metric]
- Target: [Target]
- Gap: [X%]
- Action: [Recommendation]
---
## Channel Performance
| Channel | Spend | Revenue | ROAS | vs Target |
|---------|-------|---------|------|-----------|
| Paid Search | $X | $Y | Z.Zx | ✅ +12% |
| Paid Social | $X | $Y | Z.Zx | ⚠️ -5% |
| Email | $X | $Y | Z.Zx | ✅ +25% |
| Organic | $0 | $Y | - | ✅ +8% |
---
## Top Campaigns
| Rank | Campaign | Revenue | ROAS | Notes |
|------|----------|---------|------|-------|
| 1 | [Name] | $X | Z.Zx | [Insight] |
| 2 | [Name] | $X | Z.Zx | [Insight] |
| 3 | [Name] | $X | Z.Zx | [Insight] |
---
## Trends
### Positive Trends ↑
- [Trend 1]: [X% improvement over Y period]
- [Trend 2]: [X% improvement over Y period]
### Concerning Trends ↓
- [Trend 1]: [X% decline over Y period]
- [Trend 2]: [X% decline over Y period]
---
## Recommendations
### Immediate Actions (This Week)
1. [ ] [Action] - Expected impact: [X%]
2. [ ] [Action] - Expected impact: [X%]
### Strategic Recommendations (This Quarter)
1. [ ] [Recommendation] - Investment: $X, ROI: Y%
2. [ ] [Recommendation] - Investment: $X, ROI: Y%
---
## Next Period Outlook
- **Target**: [Key goal]
- **Focus**: [Priority areas]
- **Risks**: [Key risks to monitor]
- **Opportunities**: [Growth opportunities]
```
### Daily Digest Template
```markdown
# Daily Marketing Digest
**Date**: 2025-12-08
**Prepared**: 9:00 AM
---
## Yesterday's Snapshot
| Metric | Yesterday | Avg (7d) | Status |
|--------|-----------|----------|--------|
| Spend | $4,523 | $4,200 | +8% |
| Impressions | 245K | 220K | +11% |
| Clicks | 3,421 | 3,100 | +10% |
| Conversions | 87 | 75 | +16% |
**Overall**: Strong day, above average on all metrics
---
## Notable Changes
### ✅ Wins
- Email campaign "Holiday Sale" hit 32% open rate (vs 24% avg)
- LinkedIn ads CPC dropped 15% with new creative
### ⚠️ Watch
- Google Ads CTR down 8% - reviewing ad copy
- Instagram reach declined for 3rd day
### 🚨 Action Needed
- Facebook ad account approaching spending limit
- [Action: Increase daily budget]
---
## Today's Priorities
1. [ ] Review and approve new ad creative for launch
2. [ ] Increase FB budget to avoid delivery issues
3. [ ] Prep weekly report for 10am team meeting
---
## Quick Stats
```
Budget Pacing: ████████████████░░░░ 78% spent, 80% of month
Conversion Goal: ████████████████░░░░ 82% achieved
```
```
## Insight Generation
### Performance Insights
```yaml
insight_types:
win:
template: "[Metric] exceeded target by [X%] driven by [cause]"
example: "Email revenue exceeded target by 25% driven by holiday campaign"
concern:
template: "[Metric] fell [X%] below target due to [cause], recommend [action]"
example: "CAC rose 15% above target due to increased competition, recommend testing new channels"
trend:
template: "[Metric] has [increased/decreased] [X%] over [period], indicating [interpretation]"
example: "Organic traffic has increased 12% over 3 months, indicating SEO investments paying off"
anomaly:
template: "[Metric] showed unusual [spike/drop] of [X%] on [date], likely due to [cause]"
example: "Conversions showed unusual spike of 200% on Friday, likely due to influencer mention"
```
### Context Additions
```yaml
context_types:
comparison:
- vs_target: "Target: $X, Actual: $Y (Z%)"
- vs_prior_period: "vs Last Week: +X%"
- vs_prior_year: "vs Last Year: +X%"
- vs_industry: "Industry Avg: X%, Our Performance: Y%"
benchmark:
- channel_benchmark: "Email avg open rate: 21%, Ours: 28%"
- competitor_context: "While competitors pulled back spend, we maintained"
explanation:
- why_up: "Driven by [factor1], [factor2]"
- why_down: "Impacted by [factor1], [factor2]"
- external_factor: "Industry-wide due to [event]"
```
## Usage Examples
### Weekly Summary
```
User: "Weekly performance summary"
Skill generates:
1. Aggregate week's data
2. Compare to prior week
3. Highlight wins/concerns
4. Generate recommendations
Output:
"Weekly Marketing Performance
Week of Dec 2-8, 2025
Summary:
- Revenue: $342,100 (+22% vs last week)
- Spend: $125,432 (+12%)
- ROAS: 2.73x (target: 2.5x) ✅
- New Customers: 3,421 (+8%)
Top Win:
Holiday email campaign drove $45K revenue (32% of total)
with industry-leading 28% open rate
Area of Concern:
Paid social ROAS declined to 2.1x (from 2.5x)
- Cause: Increased competition during holiday
- Action: Refresh creative, test new audiences
Recommendations:
1. Double down on email (highest ROAS at 8.5x)
2. Test new social audiences before year end
3. Increase retargeting budget by 20%
Full report: .aiwg/marketing/reports/weekly-2025-12-08.md"
```
### Quick Status
```
User: "How are we doing?"
Skill returns quick status:
"Marketing Performance Status
Period: MTD (Dec 1-8)
✅ On Track:
- Revenue: 105% of target
- Email: Exceeding all metrics
- Organic: Traffic up 12% MoM
⚠️ Watch:
- Paid social ROAS down 10%
- CAC creeping up (now $42 vs $38 target)
❌ Action Needed:
- Conversion rate drop on landing page
- Investigate and fix today
Overall: Solid performance, one issue to address"
```
### Executive Report
```
User: "Prepare executive summary for leadership meeting"
Skill generates executive-ready report:
"Preparing Executive Marketing Summary...
Report: Q4 Marketing Performance Review
Key Highlights:
1. Revenue attribution: $1.2M (+18% YoY)
2. Marketing efficiency improved: CAC down 12%
3. Brand awareness: Share of voice up 5 points
Investment Recommendation:
Request 15% budget increase for Q1 based on:
- Proven ROAS of 3.2x
- Market opportunity in healthcare vertical
- Competitor pullback creating opportunity
Full report with visualizations prepared.
Location: .aiwg/marketing/reports/exec-q4-2025.md"
```
## Integration
This skill uses:
- `data-pipeline`: Source all marketing data
- `competitive-intel`: Market context
- `artifact-metadata`: Track report versions
## Agent Orchestration
```yaml
agents:
analysis:
agent: marketing-analyst
focus: Data analysis and insights
reporting:
agent: reporting-specialist
focus: Report formatting and visualization
strategy:
agent: campaign-strategist
focus: Recommendations and action items
```
## Configuration
### Report Scheduling
```yaml
schedule:
daily_digest:
time: "09:00"
timezone: "America/New_York"
recipients: [marketing_team]
weekly_summary:
day: "Monday"
time: "08:00"
recipients: [marketing_manager, director]
monthly_report:
day: 1
time: "08:00"
recipients: [leadership, finance]
```
### Metric Thresholds
```yaml
thresholds:
green:
vs_target: ">= 100%"
vs_prior: ">= -5%"
yellow:
vs_target: "80-99%"
vs_prior: "-5% to -15%"
red:
vs_target: "< 80%"
vs_prior: "< -15%"
```
### Audience Customization
```yaml
audience_config:
executive:
detail_level: high
focus: business_impact
length: brief
visualizations: summary_charts
manager:
detail_level: medium
focus: tactical_insights
length: moderate
visualizations: detailed_charts
team:
detail_level: detailed
focus: operational_metrics
length: comprehensive
visualizations: data_tables
```
## Output Locations
- Daily digests: `.aiwg/marketing/reports/daily/`
- Weekly summaries: `.aiwg/marketing/reports/weekly/`
- Monthly reports: `.aiwg/marketing/reports/monthly/`
- Executive reports: `.aiwg/marketing/reports/executive/`
## References
- Report templates: templates/marketing/report-*.md
- KPI definitions: .aiwg/marketing/config/kpis.yaml
- Benchmark data: .aiwg/marketing/benchmarks/Related Skills
regression-performance
Detect performance regressions by comparing benchmarks across versions with latency, throughput, and statistical significance analysis
flow-performance-optimization
Orchestrate continuous performance optimization with baseline establishment, bottleneck identification, optimization implementation, load testing, and SLO validation
aiwg-orchestrate
Route structured artifact work to AIWG workflows via MCP with zero parent context cost
venv-manager
Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.
pytest-runner
Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.
vitest-runner
Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.
eslint-checker
Run ESLint for JavaScript/TypeScript code quality and style enforcement. Use for static analysis and auto-fixing.
repo-analyzer
Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.
pr-reviewer
Review GitHub pull requests for code quality, security, and best practices. Use for automated PR feedback and approval workflows.
YouTube Acquisition
yt-dlp patterns for acquiring content from YouTube and video platforms
Quality Filtering
Accept/reject logic and quality scoring heuristics for media content
Provenance Tracking
W3C PROV-O patterns for tracking media derivation chains and production history