Skip to main content
ClaudeWave
Skill693 estrellas del repoactualizado 12d ago

performance-report

The performance-report skill generates comprehensive marketing reports across campaigns, channels, content, or overall marketing performance by automatically pulling data from connected analytics platforms or accepting user-provided metrics. Use this skill when completing campaign wrap-ups, preparing stakeholder summaries for weekly or monthly reviews, or translating raw performance data into actionable executive summaries with trend analysis and prioritized optimization recommendations.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/openyak/openyak /tmp/performance-report && cp -r /tmp/performance-report/backend/app/data/plugins/marketing/skills/performance-report ~/.claude/skills/performance-report
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Performance Report

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../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

1. **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

2. **Time period** — the reporting window (last week, last month, last quarter, custom date range)

3. **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."

4. **Comparison period** (optional) — prior period or year-over-year for trend context

5. **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
instrument-data-to-allotropeSkill

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

nextflow-developmentSkill

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

scientific-problem-selectionSkill

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".

scvi-toolsSkill

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

single-cell-rna-qcSkill

Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.

startSkill

Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.

cowork-plugin-customizerSkill

>

create-cowork-pluginSkill

>