Performance Report
AnthropicOfficialPublished by Anthropic
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- Content generator
- Business and productivity
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The performance-report skill transforms raw marketing data into structured, professional reports suitable for stakeholders. It supports various reporting scopes, including specific campaigns, individual channels, content performance, or overall marketing summaries. By incorporating trend analysis, attribution insights, and a prioritized optimization framework, it helps teams move from data collection to strategic decision-making.
What it does
- Generates executive summaries with headline metrics and trend indicators.
- Creates structured dashboards comparing current performance against targets and prior periods.
- Provides channel-specific metric analysis for email, social, paid, SEO, and content.
- Identifies performance trends, inflection points, and seasonal patterns.
- Prioritizes optimization recommendations using a high-impact/low-effort matrix.
- Suggests next-period priorities and testing experiments based on findings.
When to use it
- When wrapping up a marketing campaign to evaluate its success.
- When preparing recurring weekly, monthly, or quarterly channel summaries.
- When you need to translate complex data into an executive-ready report.
- When identifying underperforming areas that require optimization.
Advantages
- Standardizes reporting formats across different marketing channels.
- Provides clear, actionable insights rather than just raw data.
- Helps align marketing activities with business objectives.
- Facilitates data-driven decision-making through prioritized recommendations.
- Supports various reporting cadences from weekly standups to quarterly reviews.
Usage examples
- “Generate a monthly performance report for our paid social campaigns.”
- The agent will pull or request paid social data, compare it against the previous month, highlight top-performing posts, and suggest optimizations for the next month.
- “Create a QBR summary for our email marketing channel.”
- The agent will produce a strategic review including open rates, CTR, list growth, and recommendations for upcoming email experiments.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/performance-report/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip performance-report.zip -d .agents/skills
Documentation by Anthropic
Performance Report
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Generate a marketing performance report with key metrics, trend analysis, insights, and optimization recommendations.
Trigger
User runs /performance-report or asks for a marketing report, performance analysis, campaign results, or metrics summary.
Inputs
Report type — determine which type of report the user needs:
Campaign report — performance of a specific campaign
Channel report — performance across a specific channel (email, social, paid, SEO, etc.)
Content performance — how content pieces are performing
Overall marketing report — cross-channel summary (weekly, monthly, quarterly)
Custom — user-defined scope
Time period — the reporting window (last week, last month, last quarter, custom date range)
Data source:
If ~~marketing analytics is connected, discover what accounts and platforms are available, then pull performance data automatically
If ~~product analytics is connected: pull performance data automatically
If not connected: ask the user to provide metrics. Prompt with: "Please paste or share your performance data. I can work with spreadsheets, CSV data, dashboard screenshots described in text, or just the key numbers."
Comparison period (optional) — prior period or year-over-year for trend context
Stakeholder audience (optional) — who will read this report (executive summary style vs. detailed analyst view)
Report Structure
1. Executive Summary
2-3 sentence overview of performance in the period
Headline metric with trend direction (up/down/flat vs. prior period)
One key win and one area of concern
2. Key Metrics Dashboard
Present core metrics in a summary table:
| Metric | This Period | Prior Period | Change | Target | Status |
|---|
Status indicators:
On track (meeting or exceeding target)
At risk (below target but within acceptable range)
Off track (significantly below target)
Metrics by Report Type
Campaign Report:
Impressions and reach
Click-through rate (CTR)
Conversion rate
Cost per acquisition (CPA)
Return on ad spend (ROAS) or ROI
Total conversions/signups/leads
Channel Report (Email):
Emails sent, delivered, bounced
Open rate
Click-through rate
Unsubscribe rate
Conversion rate
Channel Report (Social):
Impressions and reach
Engagement rate (likes, comments, shares)
Follower growth
Click-through rate
Top-performing posts
Channel Report (Paid):
Spend
Impressions and clicks
CTR
CPC and CPM
Conversions and CPA
ROAS
Channel Report (SEO/Organic):
Organic sessions
Keyword rankings (movement)
Pages indexed
Backlinks acquired
Top-performing pages
Content Performance:
Pageviews and unique visitors
Time on page
Bounce rate
Social shares
Conversions attributed to content
Top and bottom performers
Overall Marketing Report:
Total leads generated
Marketing qualified leads (MQLs)
Pipeline contribution
Customer acquisition cost (CAC)
Channel-by-channel summary
3. Trend Analysis
Performance trend over the period (week-over-week or month-over-month)
Notable inflection points and what caused them
Seasonal or cyclical patterns observed
Comparison to benchmarks or targets
4. What Worked
Top 3-5 wins with specific data
Why these performed well (hypothesis)
How to replicate or scale
5. What Needs Improvement
Bottom 3-5 performers with specific data
Hypotheses for underperformance
Recommended fixes
6. Insights and Observations
Patterns in the data that are not obvious from the metrics alone
Audience behavior insights
Content or creative themes that resonated
External factors that may have influenced performance (seasonality, news, competitive moves)
7. Recommendations
For each recommendation:
What to do
Why (linked to a specific insight from the data)
Expected impact (high, medium, low)
Effort to implement (high, medium, low)
Priority (immediate, next sprint, next quarter)
Prioritize recommendations in a 2x2 matrix format:
| Low Effort | High Effort | |
|---|---|---|
| High Impact | Do first | Plan for next sprint |
| Low Impact | Do if time allows | Deprioritize |
8. Next Period Focus
Top 3 priorities for the upcoming period
Tests or experiments to run
Targets for key metrics
Metric Definitions and Benchmarks
Email Marketing
| Metric | Definition | Benchmark Range | What It Tells You |
|---|---|---|---|
| Delivery rate | Emails delivered / emails sent | 95-99% | List health and sender reputation |
| Open rate | Unique opens / emails delivered | 15-30% | Subject line and sender effectiveness |
| Click-through rate (CTR) | Unique clicks / emails delivered | 2-5% | Content relevance and CTA effectiveness |
| Click-to-open rate (CTOR) | Unique clicks / unique opens | 10-20% | Email content quality (for those who opened) |
| Unsubscribe rate | Unsubscribes / emails delivered | <0.5% | Content-audience fit and frequency tolerance |
| Bounce rate | Bounces / emails sent | <2% | List quality and data hygiene |
| Conversion rate | Conversions / emails delivered | 1-5% | End-to-end email effectiveness |
| Revenue per email | Total revenue / emails sent | Varies | Direct revenue attribution |
| List growth rate | (New subscribers - unsubscribes) / total list | 2-5% monthly | Audience building health |
Social Media
| Metric | Definition | What It Tells You |
|---|---|---|
| Impressions | Number of times content was displayed | Content distribution and reach |
| Reach | Number of unique users who saw content | Audience breadth |
| Engagement rate | (Likes + comments + shares) / reach | Content resonance |
| Click-through rate | Link clicks / impressions | Traffic driving effectiveness |
| Follower growth rate | Net new followers / total followers per period | Audience building |
| Share/Repost rate | Shares / reach | Content virality and advocacy |
| Video view rate | Views / impressions | Video content hook effectiveness |
| Video completion rate | Completed views / total views | Video content quality and length fit |
| Social share of voice | Your mentions / total category mentions | Brand visibility vs. competitors |
Paid Advertising (Search and Social)
| Metric | Definition | What It Tells You |
|---|---|---|
| Impressions | Times ad was shown | Budget utilization and targeting breadth |
| Click-through rate (CTR) | Clicks / impressions | Ad creative and targeting relevance |
| Cost per click (CPC) | Total spend / clicks | Cost efficiency of traffic generation |
| Cost per mille (CPM) | Cost per 1,000 impressions | Awareness cost efficiency |
| Conversion rate | Conversions / clicks | Landing page and offer effectiveness |
| Cost per acquisition (CPA) | Total spend / conversions | Full-funnel cost efficiency |
| Return on ad spend (ROAS) | Revenue / ad spend | Revenue generation efficiency |
| Quality Score (search) | Google's relevance rating (1-10) | Ad-keyword-landing page alignment |
| Frequency | Average times a user sees the ad | Ad fatigue risk |
| View-through conversions | Conversions from users who saw but did not click | Display/awareness campaign influence |
SEO / Organic Search
| Metric | Definition | What It Tells You |
|---|---|---|
| Organic sessions | Visits from organic search | SEO effectiveness and content reach |
| Keyword rankings | Position for target keywords | Search visibility |
| Organic CTR | Clicks / impressions in search results | Title and meta description effectiveness |
| Pages indexed | Number of pages in search index | Crawlability and site health |
| Domain authority | Third-party authority score | Overall site strength |
| Backlinks | Number of external sites linking to you | Content authority and off-page SEO |
| Page load speed | Time to interactive | User experience and ranking factor |
| Organic conversion rate | Organic conversions / organic sessions | Content quality and intent alignment |
| Top entry pages | Most-visited pages from organic search | Content driving the most organic traffic |
Content Marketing
| Metric | Definition | What It Tells You |
|---|---|---|
| Pageviews | Total views of content pages | Content reach and distribution |
| Unique visitors | Distinct users viewing content | Audience size |
| Average time on page | Time spent on content pages | Content engagement and depth |
| Bounce rate | Single-page sessions / total sessions | Content-audience fit and UX |
| Scroll depth | How far users scroll on a page | Content engagement through the piece |
| Social shares | Times content was shared on social | Content resonance and virality |
| Backlinks earned | External links to content | Content authority and SEO value |
| Lead generation | Leads attributed to content | Content conversion effectiveness |
| Content ROI | Revenue attributed / content production cost | Overall content investment return |
Overall Marketing / Pipeline
| Metric | Definition | What It Tells You |
|---|---|---|
| Marketing qualified leads (MQLs) | Leads meeting marketing qualification criteria | Top-of-funnel effectiveness |
| Sales qualified leads (SQLs) | MQLs accepted by sales | Lead quality |
| MQL to SQL conversion rate | SQLs / MQLs | Marketing-sales alignment and lead quality |
| Pipeline generated | Dollar value of opportunities created | Marketing impact on revenue |
| Pipeline velocity | How fast deals move through pipeline | Campaign urgency and quality |
| Customer acquisition cost (CAC) | Total marketing + sales cost / new customers | Efficiency of customer acquisition |
| CAC payback period | Months to recover CAC from revenue | Unit economics health |
| Marketing-sourced revenue | Revenue from marketing-originated deals | Direct marketing contribution |
| Marketing-influenced revenue | Revenue from deals where marketing touched | Broader marketing impact |
Reporting Templates by Cadence
Weekly Marketing Report
Quick-scan format for team standups:
Top 3 metrics with week-over-week change
What worked this week (1-2 bullet points with data)
What needs attention (1-2 bullet points with data)
This week's priorities (3-5 action items)
Monthly Marketing Report
Standard stakeholder report:
Executive summary (3-5 sentences)
Key metrics dashboard (table with MoM and target comparison)
Channel-by-channel performance summary
Campaign highlights and results
What worked and what did not (with hypotheses)
Recommendations and next month priorities
Budget spend vs. plan
Quarterly Business Review (QBR)
Strategic review for leadership:
Quarter performance vs. goals
Year-to-date trajectory
Channel ROI analysis
Campaign performance summary
Competitive and market observations
Strategic recommendations for next quarter
Budget request and allocation plan
Key experiments and learnings
Dashboard Design Principles
Lead with the metrics that map to business objectives (not vanity metrics)
Show trends over time, not just point-in-time snapshots
Include comparison context: prior period, target, benchmark
Use consistent color coding: green (on track), yellow (at risk), red (off track)
Group metrics by funnel stage or business question
Keep dashboards to one page/screen — detail goes in appendix
Update cadence should match decision cadence (real-time for paid, weekly for content)
Trend Analysis and Forecasting
Trend Identification
When analyzing performance data, look for:
Directional trends: is the metric consistently going up, down, or flat over 4+ periods?
Inflection points: where did performance change direction and what happened then?
Seasonality: are there predictable patterns by day of week, month, or quarter?
Anomalies: one-time spikes or drops — what caused them and are they repeatable?
Leading indicators: which metrics change first and predict future outcomes?
Trend Analysis Process
Chart the metric over time (at least 8-12 data points for meaningful trends)
Identify the overall direction (upward, downward, flat, cyclical)
Calculate the rate of change (is it accelerating or decelerating?)
Overlay key events (campaigns launched, product changes, market events)
Compare to benchmarks or targets
Identify correlations with other metrics
Form hypotheses about causation (and plan tests to validate)
Simple Forecasting Approaches
Linear projection: extend the current trend line forward (useful for stable metrics)
Moving average: smooth out noise by averaging the last 3-6 periods
Year-over-year comparison: use last year's pattern as a baseline, adjusted for growth rate
Funnel math: forecast outputs from inputs (e.g., if we generate X leads at Y conversion rate, we will get Z customers)
Scenario modeling: create best case, expected case, and worst case projections
Forecasting Caveats
Short-term forecasts (1-3 months) are more reliable than long-term
Forecasts based on fewer than 12 data points should be flagged as low confidence
External factors (market shifts, competitive moves, economic changes) can invalidate trend-based forecasts
Always present forecasts as ranges, not exact numbers
Attribution Modeling Basics
What Is Attribution?
Attribution determines which marketing touchpoints get credit for a conversion. This matters because buyers typically interact with multiple channels before converting.
Common Attribution Models
| Model | How It Works | Best For | Limitation |
|---|---|---|---|
| Last touch | 100% credit to last interaction before conversion | Understanding final conversion triggers | Ignores awareness and nurture |
| First touch | 100% credit to first interaction | Understanding top-of-funnel effectiveness | Ignores nurture and conversion drivers |
| Linear | Equal credit to all touchpoints | Fair representation of all channels | Does not reflect relative impact |
| Time decay | More credit to touchpoints closer to conversion | Balanced view favoring recent interactions | May undervalue awareness |
| Position-based (U-shaped) | 40% first, 40% last, 20% split among middle | Valuing both discovery and conversion | Somewhat arbitrary weighting |
| Data-driven | Algorithmic credit based on conversion patterns | Most accurate representation | Requires significant data volume |
Attribution Practical Guidance
Start with last-touch attribution if you have no model in place — it is the simplest and most actionable
Compare first-touch and last-touch to understand which channels drive awareness vs. conversion
Use position-based (U-shaped) as a reasonable middle ground for most B2B companies
Data-driven attribution requires high conversion volume to be statistically meaningful
No model is perfect — use attribution directionally, not as absolute truth
Multi-touch attribution is better than single-touch, but any model is better than none
Attribution Pitfalls
Do not optimize one channel in isolation based on single-touch attribution
Awareness channels (display, social, PR) will always look bad in last-touch models
Conversion channels (search, retargeting) will always look bad in first-touch models
Self-reported attribution ("how did you hear about us?") provides useful qualitative color but is unreliable as quantitative data
Cross-device and cross-channel tracking gaps mean attribution data is always incomplete
Optimization Recommendations Framework
Optimization Process
Identify: which metrics are underperforming vs. target or benchmark?
Diagnose: where in the funnel is the problem? (impressions, clicks, conversions, retention)
Hypothesize: what is causing the underperformance? (audience, message, creative, offer, timing, technical)
Prioritize: which fixes will have the biggest impact with the least effort?
Test: design an experiment to validate the hypothesis
Measure: did the change improve the metric?
Scale or iterate: roll out wins broadly; iterate on inconclusive or failed tests
Optimization Levers by Funnel Stage
| Funnel Stage | Problem Signal | Optimization Levers |
|---|---|---|
| Awareness | Low impressions, low reach | Budget, targeting, channel mix, creative format |
| Interest | Low CTR, low engagement | Ad creative, headlines, content hooks, audience targeting |
| Consideration | High bounce rate, low time on page | Landing page content, page speed, content relevance, UX |
| Conversion | Low conversion rate | Offer, CTA, form length, trust signals, page layout |
| Retention | High churn, low repeat engagement | Onboarding, email nurture, product experience, support |
Testing Best Practices
Test one variable at a time for clean results
Define the success metric before launching the test
Calculate required sample size before starting (do not end tests early)
Run tests for a minimum of one full business cycle (typically one week for B2B)
Document all tests and results, regardless of outcome
Share learnings across the team — failed tests are valuable information
A test that confirms the status quo is not a failure — it builds confidence in your current approach
Continuous Optimization Cadence
Daily: monitor paid campaigns for budget pacing, anomalies, and disapproved ads
Weekly: review channel performance, pause underperformers, scale winners
Bi-weekly: refresh ad creative and test new variants
Monthly: full performance review, identify new optimization opportunities, update forecasts
Quarterly: strategic review of channel mix, budget allocation, and targeting strategy
Output Formatting
Use tables for data presentation
Bold key numbers and trends
Keep the executive summary concise (suitable for forwarding to leadership)
Include a "detailed appendix" section for granular data if the user provided a lot of metrics
After the Report
Ask: "Would you like me to:
Create a slide-ready summary of these results?
Draft a stakeholder email with the key takeaways?
Dive deeper into any specific metric or channel?
Set up a reporting template you can reuse next period?"
Files
- ATTRIBUTION.mdAdded by AutoGineer936 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md18.8 KiB
ATTRIBUTION.md
Attribution
This folder is an unmodified copy of the Agent Skill "performance-report", distributed by AutoGineer (https://autogineer.com/agent-skills).
Commit: e050c2ec856373787143d7057663a6ebd1277df3
Published by: Anthropic (https://github.com/anthropics)
Publisher: Anthropic (https://github.com/anthropics)
Licence: Apache License 2.0 (https://github.com/anthropics/knowledge-work-plugins/blob/e050c2ec856373787143d7057663a6ebd1277df3/marketing/LICENSE)
Licence evidence: licence file marketing/LICENSE
Retrieved: 2026-11-07
The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.
Files added by AutoGineer
ATTRIBUTION.md: this file, written by AutoGineer
LICENSE: copied from marketing/LICENSE
Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter
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- anthropics/knowledge-work-plugins · marketing/skills/performance-report · commit e050c2e
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- LICENSE (added by AutoGineer from marketing/LICENSE)
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