Python Executor
cortega26/chile-hub
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh).
Analyzes uploaded cohort data to compute retention curves and feature adoption trends, builds heatmaps and charts, and suggests qualitative follow-up research.
Install Cohort Analysis and Retention Explorer
$ npx skills add phuryn/pm-skills --skill cohort-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install phuryn/pm-skills cohort-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/pm-data-analytics/skills/cohort-analysis .claude/skills/cohort-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cohort-analysis" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysis into .claude/skills/cohort-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/phuryn/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add phuryn/pm-skills --skill cohort-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install phuryn/pm-skills cohort-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/pm-data-analytics/skills/cohort-analysis .agents/skills/cohort-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cohort-analysis" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysis into .agents/skills/cohort-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add phuryn/pm-skills --skill cohort-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install phuryn/pm-skills cohort-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/pm-data-analytics/skills/cohort-analysis .cursor/skills/cohort-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cohort-analysis" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysis into .cursor/skills/cohort-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/phuryn/pm-skills.git --path pm-data-analytics/skills/cohort-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add phuryn/pm-skills --skill cohort-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install phuryn/pm-skills cohort-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/pm-data-analytics/skills/cohort-analysis .gemini/skills/cohort-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cohort-analysis" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysis into .gemini/skills/cohort-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install phuryn/pm-skills cohort-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add phuryn/pm-skills --skill cohort-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/pm-data-analytics/skills/cohort-analysis .github/skills/cohort-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cohort-analysis" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysis into .github/skills/cohort-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add phuryn/pm-skills --skill cohort-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install phuryn/pm-skills cohort-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/pm-data-analytics/skills/cohort-analysis .opencode/skills/cohort-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cohort-analysis" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-data-analytics/skills/cohort-analysis into .opencode/skills/cohort-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cohort-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
cohort-analysis- GitHub stars
- 27k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 482 words
- Files
- 1
- Skills in repo
- 67
- Repo updated
- First seen
- Licence
- MIT
At a glance
Analyzes uploaded cohort data to compute retention curves and feature adoption trends, builds heatmaps and charts, and suggests qualitative follow-up research.
- Works in 5 steps: Read and Validate Your Data → Generate Quantitative Analysis → Create Visualizations → …
- Understanding retention differences across user cohorts
- SKILL.md covers Purpose, How It Works, Usage Examples and Key Capabilities, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Starting from a CSV, Excel or JSON file, the skill first checks the data structure for a cohort identifier, time periods and engagement metrics, flags missing values, and summarizes cohort sizes and date ranges before any analysis runs. It then calculates retention rates, drop-off patterns, feature adoption rates across cohorts, and period-over-period changes, optionally generating pandas and numpy scripts when the user wants runnable code.
Visualization covers retention heatmaps of cohorts against time periods, line charts of cohort progression, feature-adoption comparison charts and drop-off visualizations, delivered as interactive charts or static images. From there it looks for patterns worth flagging, such as early churn in a specific cohort, late-stage engagement shifts, adoption clusters or seasonal effects, and compares cohorts against each other as a baseline.
The last step turns quantitative findings into qualitative next steps: targeted interviews with churning users, usage surveys for engaged cohorts, session replays of key interactions, or a win-loss comparison between high and low retention cohorts, plus suggestions for a follow-up A/B test or feature experiment.
When your agent uses it
- Understanding retention differences across user cohorts
- Comparing feature adoption curves between groups of users
- Turning a retention pattern into a concrete follow-up research plan
Example prompts
- “Analyze retention by monthly cohort in cohort_engagement.csv.”
- “Compare adoption curves for our new feature across the last six cohorts.”
- “Why is the March cohort churning faster than earlier ones?”
Requirements
- A cohort data file in CSV, Excel or JSON format
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 8607e3b. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
productcompass.pm
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Cohort Analysis and Retention Explorer loads about 1.3k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 482 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from phuryn/pm-skills at commit 8607e3b, republished under its MIT licence (© phuryn). 482 words, ~1,256 tokens.
Download SKILL.mdSave it as
.claude/skills/cohort-analysis/SKILL.md (or your agent's skills folder).- name
- cohort-analysis
- description
- Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Cohort Analysis & Retention Explorer
Purpose
Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement. Combine quantitative insights with qualitative research recommendations.
How It Works
Step 1: Read and Validate Your Data
- Accept CSV, Excel, or JSON data files with user cohort information
- Verify data structure: cohort identifier, time periods, engagement metrics
- Check for missing values and data quality issues
- Summarize key statistics (cohort sizes, date ranges, metrics available)
Step 2: Generate Quantitative Analysis
- Calculate cohort retention rates and engagement trends
- Identify retention curves, drop-off patterns, and anomalies
- Compute feature adoption rates across cohorts
- Calculate month-over-month or period-over-period changes
- Generate Python analysis scripts using pandas and numpy if requested
Step 3: Create Visualizations
- Generate retention heatmaps (cohorts vs. time periods)
- Create line charts showing cohort progression
- Build comparison charts for feature adoption
- Visualize drop-off points and engagement trends
- Output as interactive charts or static images
Step 4: Identify Insights & Patterns
- Spot one or more significant patterns:
- Early churn in specific cohorts
- Late-stage engagement changes
- Feature adoption clusters
- Seasonal or temporal trends
- Highlight surprising findings and deviations
- Compare cohort performance to establish baselines
Step 5: Suggest Follow-Up Research
- Recommend qualitative research methods:
- Targeted user interviews with churning users
- Feature usage surveys with engaged cohorts
- Session replays of key interaction patterns
- Win/loss analysis for high vs. low retention cohorts
- Design follow-up quantitative studies
- Suggest A/B tests or feature experiments
Usage Examples
Example 1: Upload CSV Data
Upload cohort_engagement.csv with columns: cohort_month, weeks_active,
user_id, feature_x_usage, engagement_score
Request: "Analyze retention patterns and identify why Q4 2025 cohorts
underperform compared to Q3"Example 2: Describe Data Format
"I have monthly user cohorts from Jan-Dec 2025. Each row shows:
cohort date, user ID, purchase frequency, and support tickets.
Analyze which cohorts show best long-term retention."Example 3: Feature Adoption Analysis
Upload feature_usage.xlsx with cohort adoption data.
Request: "Compare adoption curves for our new feature across cohorts.
Which cohorts adopted fastest? Any patterns?"Key Capabilities
- Data Reading: Import CSV, Excel, JSON, SQL query results
- Retention Analysis: Calculate and visualize retention rates over time
- Cohort Comparison: Compare metrics across cohort groups
- Anomaly Detection: Flag unusual patterns or drop-offs
- Python Scripts: Generate reusable analysis code for ongoing analysis
- Visualizations: Create heatmaps, charts, and interactive dashboards
- Research Design: Suggest targeted follow-up studies and interview approaches
- Statistical Summary: Provide quantitative metrics and correlation analysis
Show full SKILL.md (162 more words)Show less
Tips for Best Results
- Include time dimension: Provide data across multiple time periods
- Define cohort clearly: Make cohort grouping explicit (signup month, feature launch date, etc.)
- Provide context: Explain product changes, launches, or events during the period
- Multiple metrics: Include retention, engagement, feature usage, revenue, etc.
- Sufficient data: At least 3-4 cohorts for meaningful pattern identification
- Request specific output: Ask for visualizations, Python scripts, or research recommendations
Output Format
You'll receive:
- Data Summary: Cohort overview and data quality assessment
- Quantitative Findings: Key metrics, retention rates, and trend analysis
- Visualizations: Charts showing retention curves, adoption patterns
- Pattern Identification: 2-3 significant insights from the data
- Research Recommendations: Specific qualitative and quantitative follow-ups
- Analysis Scripts (if requested): Python code for reproducible analysis
- Next Steps: Prioritized actions based on findings
Further Reading
© phuryn, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in pm-data-analytics/skills/cohort-analysis of phuryn/pm-skills.
Open the folder on GitHubat commit 8607e3b
Compare with similar skills
Cohort Analysis and Retention Explorer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cohort Analysis and Retention Explorer this skillphuryn/pm-skills | 27k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Vaex Out-of-Core DataFramesdavila7/claude-code-templates | 32k | 12 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Release Evidence WorkflowAli-Marandi/ClimateDataAnalyzer | 107 | — | ~1.6k | Automated safety check: Pass | MIT | |
| SeabornzLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Retentioneering Product Analyticsretentioneering/retentioneering-tools | 925 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 |
Similar skills
Vaex Out-of-Core DataFrames
davila7/claude-code-templates
Processes tabular datasets too large for RAM with Vaex: lazy DataFrames, fast aggregations, big-data plots and ML pipelines over CSV, HDF5, Arrow and Parquet.
Release Evidence Workflow
Ali-Marandi/ClimateDataAnalyzer
Build an auditable release-evidence workflow for a desktop or packaged application.
Seaborn
zLanqing/codex-claude-academic-skills
Statistical visualization with pandas integration. An agent skill from zLanqing/codex-claude-academic-skills.
Retentioneering Product Analytics
retentioneering/retentioneering-tools
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering.
Senior Data Scientist
Raidriar7170/hermes-skilleval
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
More from phuryn/pm-skills
All 67 skills in this repoAgreement-Based Code Review
phuryn/pm-skills
Reviews a diff by anchoring on agreements between two sides of a boundary, forcing a concrete violating execution, and refuting each finding before reporting it.
A/B Test Analysis
phuryn/pm-skills
Validates an experiment's setup, works out lift, p-value and confidence interval from A/B test data, and recommends whether to ship, extend or stop.
Team OKR Brainstorm
phuryn/pm-skills
Drafts three alternative sets of team OKRs, each with an inspiring objective and measurable key results, tied to the company strategy you provide.
Dummy Dataset Generator
phuryn/pm-skills
Generates realistic test datasets with custom columns, row counts and business constraints, output as CSV, JSON, SQL inserts or a runnable Python script.
Grammar and Flow Checker
phuryn/pm-skills
Reviews a draft for grammar, logic and flow problems and returns located, prioritized fix suggestions without rewriting the whole text.
Customer Interview Script
phuryn/pm-skills
Builds a structured customer interview script with opening, warm-up, jobs-to-be-done exploration and wrap-up sections, following Mom Test rules against leading questions.
Related
Questions about Cohort Analysis and Retention Explorer
What does Cohort Analysis and Retention Explorer do?
Analyzes uploaded cohort data to compute retention curves and feature adoption trends, builds heatmaps and charts, and suggests qualitative follow-up research. Starting from a CSV, Excel or JSON file, the skill first checks the data structure for a cohort identifier, time periods and engagement metrics, flags missing values, and summarizes cohort sizes and date ranges before any analysis runs. It then calculates retention rates, drop-off patterns, feature adoption rates across cohorts, and period-over-period changes, optionally generating pandas and numpy scripts when the user wants runnable code.
When should I use Cohort Analysis and Retention Explorer?
Cohort Analysis and Retention Explorer fits situations like: understanding retention differences across user cohorts; comparing feature adoption curves between groups of users; turning a retention pattern into a concrete follow-up research plan.
How do I install Cohort Analysis and Retention Explorer in Claude Code?
Run `npx skills add phuryn/pm-skills --skill cohort-analysis -a claude-code`. Or copy the skill folder (pm-data-analytics/skills/cohort-analysis in phuryn/pm-skills) into .claude/skills/cohort-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Cohort Analysis and Retention Explorer in Codex?
Run `npx skills add phuryn/pm-skills --skill cohort-analysis -a codex`. Or copy the skill folder (pm-data-analytics/skills/cohort-analysis in phuryn/pm-skills) into .agents/skills/cohort-analysis in your project. Codex loads it when a task matches its description.
Can I use Cohort Analysis and Retention Explorer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add phuryn/pm-skills --skill cohort-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cohort-analysis, .gemini/skills/cohort-analysis, .github/skills/cohort-analysis and .opencode/skills/cohort-analysis in your project.
What does Cohort Analysis and Retention Explorer need to run?
SKILL.md names no scripts, command-line tools or credentials: Cohort Analysis and Retention Explorer is instructions for the agent only. Our summary lists: A cohort data file in CSV, Excel or JSON format.
Does Cohort Analysis and Retention Explorer access the network?
SKILL.md names 1 domain. As links in the text: productcompass.pm. This is read from the text; nothing was executed.
Is Cohort Analysis and Retention Explorer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Cohort Analysis and Retention Explorer use?
Cohort Analysis and Retention Explorer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Cohort Analysis and Retention Explorer use?
About 1.3k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Cohort Analysis and Retention Explorer?
Skills that share tags, products or a category with Cohort Analysis and Retention Explorer: Python Executor (cortega26/chile-hub, 113 stars), Vaex Out-of-Core DataFrames (davila7/claude-code-templates, 32k stars), Release Evidence Workflow (Ali-Marandi/ClimateDataAnalyzer, 107 stars) and Seaborn (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cohort Analysis and Retention Explorer?
phuryn (a GitHub user) maintains it in phuryn/pm-skills, which has 26,845 GitHub stars. The repository holds 67 skills in this directory. The repository was last updated on September 14, 2026.
Source: phuryn/pm-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.