Business Metrics Calculator
nimrodfisher/data-analytics-skills
Standard business metric calculation with industry benchmarks.
A skill your agent uses when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
Install Product Analytics
$ npx skills add alirezarezvani/claude-skills --skill product-analytics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills product-analytics --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/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/product-team/skills/product-analytics .claude/skills/product-analytics && 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 "product-analytics" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics into .claude/skills/product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-analytics", 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/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analyticsType 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 alirezarezvani/claude-skills --skill product-analytics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills product-analytics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/product-team/skills/product-analytics .agents/skills/product-analytics && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "product-analytics" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics into .agents/skills/product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-analytics", 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 alirezarezvani/claude-skills --skill product-analytics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills product-analytics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/product-team/skills/product-analytics .cursor/skills/product-analytics && 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 "product-analytics" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics into .cursor/skills/product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-analytics", 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/alirezarezvani/claude-skills.git --path product-team/skills/product-analytics--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 alirezarezvani/claude-skills --skill product-analytics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills product-analytics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/product-team/skills/product-analytics .gemini/skills/product-analytics && 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 "product-analytics" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics into .gemini/skills/product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-analytics", 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 alirezarezvani/claude-skills product-analyticsInstalls 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 alirezarezvani/claude-skills --skill product-analytics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/product-team/skills/product-analytics .github/skills/product-analytics && 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 "product-analytics" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics into .github/skills/product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-analytics", 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 alirezarezvani/claude-skills --skill product-analytics -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills product-analytics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/product-team/skills/product-analytics .opencode/skills/product-analytics && 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 "product-analytics" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/product-team/skills/product-analytics into .opencode/skills/product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-analytics", 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
product-analytics- GitHub stars
- 28k
- Used in
- 1 other repo
- Token cost
- ~1.4k tokens
- SKILL.md length
- 541 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
- Works in 5 steps: Define cohort anchor event (signup,… → Define retained behavior (active day,… → Build retention matrix by cohort… → …
- Defining product KPIs
- SKILL.md covers When To Use, Workflow, KPI Guidance By Stage and Dashboard Design Principles, plus 5 more sections
- Runs Python scripts from its folder; calls python3
What it does
Product Analytics is an agent skill from alirezarezvani/claude-skills. Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/dashboard-templates.md`, `references/metrics-frameworks.md` and `scripts/metrics_calculator.py`).
It sits in Data & Analytics, covering Product analytics and OKRs and executive reporting. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- Defining product KPIs
- Building metric dashboards
- Retention analysis
- Interpreting feature adoption trends across product stages
Example prompts
- “/product-analytics”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Define cohort anchor event (signup, activation, first purchase).
- Define retained behavior (active day, key action, repeat session).
- Build retention matrix by cohort week/month and age period.
- Compare curve shape across cohorts.
- Flag early drop points and investigate journey friction.
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Product Analytics loads about 1.4k tokens when it runs, and up to ~2.5k if it reads all its reference files. Until then it costs about 45 tokens; SKILL.md has 541 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); the scripts in this folder are not scanned.
SKILL.md
The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 541 words, ~1,371 tokens.
Download SKILL.mdSave it as
.claude/skills/product-analytics/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- product-analytics
- description
- Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
Product Analytics
Define, track, and interpret product metrics across discovery, growth, and mature product stages.
When To Use
Use this skill for:
- Metric framework selection (AARRR, North Star, HEART)
- KPI definition by product stage (pre-PMF, growth, mature)
- Dashboard design and metric hierarchy
- Cohort and retention analysis
- Feature adoption and funnel interpretation
Workflow
- Select metric framework
- AARRR for growth loops and funnel visibility
- North Star for cross-functional strategic alignment
- HEART for UX quality and user experience measurement
- Define stage-appropriate KPIs
- Pre-PMF: activation, early retention, qualitative success
- Growth: acquisition efficiency, expansion, conversion velocity
- Mature: retention depth, revenue quality, operational efficiency
- Design dashboard layers
- Executive layer: 5-7 directional metrics
- Product health layer: acquisition, activation, retention, engagement
- Feature layer: adoption, depth, repeat usage, outcome correlation
- Run cohort + retention analysis
- Segment by signup cohort or feature exposure cohort
- Compare retention curves, not single-point snapshots
- Identify inflection points around onboarding and first value moment
- Interpret and act
- Connect metric movement to product changes and release timeline
- Distinguish signal from noise using period-over-period context
- Propose one clear product action per major metric risk/opportunity
KPI Guidance By Stage
Pre-PMF
- Activation rate
- Week-1 retention
- Time-to-first-value
- Problem-solution fit interview score
Growth
- Funnel conversion by stage
- Monthly retained users
- Feature adoption among new cohorts
- Expansion / upsell proxy metrics
Mature
- Net revenue retention aligned product metrics
- Power-user share and depth of use
- Churn risk indicators by segment
- Reliability and support-deflection product metrics
Dashboard Design Principles
- Show trends, not isolated point estimates.
- Keep one owner per KPI.
- Pair each KPI with target, threshold, and decision rule.
- Use cohort and segment filters by default.
- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).
See:
references/metrics-frameworks.mdreferences/dashboard-templates.md
Cohort Analysis Method
- Define cohort anchor event (signup, activation, first purchase).
- Define retained behavior (active day, key action, repeat session).
- Build retention matrix by cohort week/month and age period.
- Compare curve shape across cohorts.
- Flag early drop points and investigate journey friction.
Show full SKILL.md (216 more words)Show less
Retention Curve Interpretation
- Sharp early drop, low plateau: onboarding mismatch or weak initial value.
- Moderate drop, stable plateau: healthy core audience with predictable churn.
- Flattening at low level: product used occasionally, revisit value metric.
- Improving newer cohorts: onboarding or positioning improvements are working.
Anti-Patterns
| Anti-pattern | Fix |
|---|---|
| Vanity metrics — tracking pageviews or total signups without activation context | Always pair acquisition metrics with activation rate and retention |
| Single-point retention — reporting "30-day retention is 20%" | Compare retention curves across cohorts, not isolated snapshots |
| Dashboard overload — 30+ metrics on one screen | Executive layer: 5-7 metrics. Feature layer: per-feature only |
| No decision rule — tracking a KPI with no threshold or action plan | Every KPI needs: target, threshold, owner, and "if below X, then Y" |
| Averaging across segments — reporting blended metrics that hide segment differences | Always segment by cohort, plan tier, channel, or geography |
| Ignoring seasonality — comparing this week to last week without adjusting | Use period-over-period with same-period-last-year context |
Tooling
scripts/metrics_calculator.py
CLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output.
# Retention analysis
python3 scripts/metrics_calculator.py retention events.csv
python3 scripts/metrics_calculator.py retention events.csv --format json
# Cohort matrix
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json
# Funnel conversion
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format jsonCSV format for retention/cohort:
user_id,cohort_date,activity_date
u001,2026-01-01,2026-01-01
u001,2026-01-01,2026-01-03
u002,2026-01-02,2026-01-02CSV format for funnel:
user_id,stage
u001,visit
u001,signup
u001,activate
u002,visit
u002,signupCross-References
- Related:
product-team/experiment-designer— for A/B test planning after identifying metric opportunities - Related:
product-team/product-manager-toolkit— for RICE prioritization of metric-driven features - Related:
product-team/product-discovery— for assumption mapping when metrics reveal unknowns - Related:
finance/saas-metrics-coach— for SaaS-specific metrics (ARR, MRR, churn, LTV)
© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (scripts, references) in product-team/skills/product-analytics of alirezarezvani/claude-skills.
- SKILL.md
- references/dashboard-templates.md
- references/metrics-frameworks.md
- scripts/metrics_calculator.py
Open the folder on GitHubat commit 19392f7
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in alirezarezvani/claude-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Product Analytics 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 |
|---|---|---|---|---|---|---|
| Product Analytics this skillalirezarezvani/claude-skills | 28k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Business Metrics Calculatornimrodfisher/data-analytics-skills | 468 | — | ~668 | Automated safety check: Pass | MIT | |
| Pm Metricsserejaris/personal-corp-os | 229 | — | ~3k | Automated safety check: Pass | MIT | |
| Add React Analyticsgotempsh/temps | 828 | — | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| App Analyticsappeeky/aso-skills | 2.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Product Metrics Dashboard Designphuryn/pm-skills | 27k | — | ~1.3k | Automated safety check: Pass | MIT |
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Questions about Product Analytics
What does Product Analytics do?
A skill your agent uses when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages. Product Analytics is an agent skill from alirezarezvani/claude-skills. Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
When should I use Product Analytics?
Product Analytics fits situations like: defining product KPIs; building metric dashboards; retention analysis; interpreting feature adoption trends across product stages.
How do I install Product Analytics in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill product-analytics -a claude-code`. Or copy the skill folder (product-team/skills/product-analytics in alirezarezvani/claude-skills) into .claude/skills/product-analytics in your project. Claude Code loads it when a task matches its description.
How do I install Product Analytics in Codex?
Run `npx skills add alirezarezvani/claude-skills --skill product-analytics -a codex`. Or copy the skill folder (product-team/skills/product-analytics in alirezarezvani/claude-skills) into .agents/skills/product-analytics in your project. Codex loads it when a task matches its description.
Can I use Product Analytics 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 alirezarezvani/claude-skills --skill product-analytics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-analytics, .gemini/skills/product-analytics, .github/skills/product-analytics and .opencode/skills/product-analytics in your project.
What does Product Analytics need to run?
Going by SKILL.md and its folder, Product Analytics needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Product Analytics access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Product Analytics 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Product Analytics use?
Product Analytics 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 Product Analytics use?
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.1k tokens, read only when the agent opens those files.
What are the alternatives to Product Analytics?
Skills that share tags, products or a category with Product Analytics: Business Metrics Calculator (nimrodfisher/data-analytics-skills, 468 stars), Pm Metrics (serejaris/personal-corp-os, 229 stars), Add React Analytics (gotempsh/temps, 828 stars) and App Analytics (appeeky/aso-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Product Analytics?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,891 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.