PostHog CLI Queries
debugtheworldbot/keyStats
Runs HogQL queries against this project's PostHog data from the terminal using posthog-cli, with bundled scripts for dashboard metadata the CLI itself has no command for.
Product analytics for instrumenting products, defining metrics, and building retention funnels.
Install Product Analytics
$ npx skills add borghei/Claude-Skills --skill product-analytics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/product-team/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/borghei/Claude-Skills/tree/main/product-team/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/borghei/Claude-Skills/tree/main/product-team/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 borghei/Claude-Skills --skill product-analytics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/product-team/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/borghei/Claude-Skills/tree/main/product-team/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 borghei/Claude-Skills --skill product-analytics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/product-team/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/borghei/Claude-Skills/tree/main/product-team/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/borghei/Claude-Skills.git --path product-team/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 borghei/Claude-Skills --skill product-analytics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/product-team/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/borghei/Claude-Skills/tree/main/product-team/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 borghei/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 borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/product-team/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/borghei/Claude-Skills/tree/main/product-team/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 borghei/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 borghei/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/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/product-team/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/borghei/Claude-Skills/tree/main/product-team/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
- 874
- Token cost
- ~2.1k tokens
- SKILL.md length
- 970 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- MIT
At a glance
Product analytics for instrumenting products, defining metrics, and building retention funnels.
- Works in 4 steps: Define the North Star (one number that… → Decompose into inputs (drivers of the NS). → Add guardrails / counter-metrics that… → …
- Designing a metric tree
- SKILL.md covers When to use this skill, Inputs the advisor expects, Clarify First and Workflows, plus 5 more sections
- Runs Python scripts from its folder; calls python3
What it does
Product Analytics is an agent skill from borghei/Claude-Skills. Product analytics for instrumenting products, defining metrics, and building retention funnels. Use when designing a metric tree, instrumenting a feature, auditing instrumentation, defining a North Star, or building an analytics roadmap.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/cohort-retention-and-funnel-analysis.md`, `references/instrumentation-and-event-design.md` and `references/metric-tree-and-north-star.md`).
It sits in Data & Analytics, covering Product analytics. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- Designing a metric tree
- Instrumenting a feature
- Auditing instrumentation
- Defining a North Star
Example prompts
- “/product-analytics”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Define the North Star (one number that summarizes value delivered).
- Decompose into inputs (drivers of the NS).
- Add guardrails / counter-metrics that catch unintended consequences.
- Run metric_tree_designer.py against your candidate tree to surface
What it can do on your machine
Read from SKILL.md and the folder at commit c9a1487. 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 3 files 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 2.1k tokens when it runs, and up to ~7.7k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 970 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 borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 970 words, ~2,117 tokens.
Download SKILL.mdSave it as
.claude/skills/product-analytics/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- product-analytics
- description
- Product analytics for instrumenting products, defining metrics, and building retention funnels. Use when designing a metric tree, instrumenting a feature, auditing instrumentation, defining a North Star, or building an analytics roadmap.
- license
- MIT + Commons Clause
- metadata.version
- 1.0.0
- metadata.author
- borghei
- metadata.category
- product-team
- metadata.domain
- product-analytics
- metadata.updated
- 2026-05-27
- metadata.tags
- analytics, metrics, north-star, retention, activation, funnel, cohort, instrumentation
Product Analytics
A product analytics skill focused on decisions from data, not dashboards. Covers the metric tree, instrumentation patterns, funnel + retention + cohort analysis, and the operational rituals that turn measurement into product changes.
When to use this skill
- Designing the North Star metric and its tree of input metrics
- Auditing product instrumentation (events, properties, gaps)
- Building or refreshing an activation funnel for a new product or feature
- Designing or analyzing retention cohorts (D1/D7/D30/W1/W4/M1/M3)
- Building or refining the PM analytics dashboard
- Translating product data into decisions and roadmap inputs
- Auditing dashboards for actionability (kill the vanity)
Inputs the advisor expects
- Product type (B2B SaaS, consumer, marketplace, etc.)
- Current analytics stack (Amplitude / Mixpanel / GA4 / Segment / Snowflake + dbt + Looker)
- Existing North Star + input metrics
- Current event taxonomy + instrumentation gaps
- Top product questions you can't answer today
- Org expectations: who consumes analytics, at what cadence
Clarify First
Before designing the metric tree or audit, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Product type — B2B SaaS, consumer, marketplace, etc. (drives the North Star pattern and input metrics)
- The value moment — what "delivered value" looks like for a user (defines the North Star and activation event)
- Current analytics stack and event taxonomy — Amplitude/Mixpanel/GA4/Segment plus existing events (drives the instrumentation audit and gap list)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Workflows
Workflow 1 — Design the metric tree
- Define the North Star (one number that summarizes value delivered).
- Decompose into inputs (drivers of the NS).
- Add guardrails / counter-metrics that catch unintended consequences.
- Run
metric_tree_designer.pyagainst your candidate tree to surface imbalance, missing layers, anti-patterns.
python3 product-analytics/scripts/metric_tree_designer.py \
--input metric_tree.json --format markdownWorkflow 2 — Audit instrumentation
- Pull the current event taxonomy + properties.
- Run
event_taxonomy_auditor.pyto flag PII risk, schema drift, naming inconsistency, duplication, undocumented events, and gaps. - Generate the remediation backlog and assign owners.
python3 product-analytics/scripts/event_taxonomy_auditor.py \
--input event_inventory.json --format markdownWorkflow 3 — Analyze retention cohorts
- Pull cohort retention data (raw counts by cohort week and offset).
- Run
retention_cohort_analyzer.pyto compute retention rates, identify patterns (smile curve, leaky bucket), and surface cohort-level alerts.
python3 product-analytics/scripts/retention_cohort_analyzer.py \
--input retention.json --format markdownDecision frameworks
North Star metric — what makes one good
A good North Star metric:
- Measures value delivered to the user (not just usage)
- Aligns to business outcome indirectly via clear chain
- Is a leading indicator of long-term success
- Can move week-over-week (so it can be acted on)
- Is hard to game without delivering real value
Common patterns by product type:
| Product type | Common North Star |
|---|---|
| Communication / messaging | Messages sent per WAU |
| Marketplace | Successful transactions per MAU |
| Content | Hours of meaningful content consumed |
| Productivity SaaS | Activated workspaces × engagement depth |
| Consumer payments | Active payment senders per week |
| Developer tool | Weekly active developers performing core action |
Don't pick "DAU" or "Revenue" as North Star — they're outputs, not value drivers.
Metric tree structure
A clean metric tree has three layers:
- North Star (1 metric)
- Input metrics (3–5 that combine to produce the NS)
- Driver metrics (per input, 3–5 that move the input)
Plus a guardrails / counter-metrics sidebar (3–5 that catch unintended consequences).
If you have 30 KPIs at the top level, you have no top level.
The activation question
For any new product or feature, ask: "What does it look like when a user realizes value from this?"
That's the activation event. A clear definition makes:
- Onboarding design — clearer
- Funnel analysis — possible
- Eval of marketing channels — sharper
- Customer success interventions — better-timed
Common mistake: defining activation as "completed signup." Signup is table stakes; activation is the moment of value.
Show full SKILL.md (375 more words)Show less
Retention curve shapes
| Shape | Diagnosis | Action |
|---|---|---|
| Power-law smile | Healthy product-market fit | Invest in scale |
| Slow decay then flat | Product-market fit | Investigate the flatline cohort segment |
| Steep then zero | Novelty product | Re-evaluate the value proposition |
| Linear decline | Leaky bucket | Improve retention features |
| Inverted (rising) | Network effects kicking in | Acquire harder |
Read shape before reading numbers.
Vanity vs actionable metrics
| Metric | Vanity if | Actionable if |
|---|---|---|
| DAU / MAU | Tracked alone | Decomposed by segment, action |
| Pageviews | Tracked alone | Tied to conversion funnel |
| Total revenue | Tracked alone | Decomposed by cohort, channel, segment |
| App downloads | Tracked alone | Paired with activation rate |
| Total accounts | Tracked alone | Paired with active accounts |
The test: "If this metric goes up 10% next week, what do we change?" If you don't have an answer, it's vanity.
Common engagements
"Help me design our analytics for the launch"
- Define activation event and 3–5 input metrics.
- Spec event taxonomy (event names, properties, user/account context).
- Pilot dashboards (one for the team, one for execs).
- Set the review cadence; don't let dashboards rot.
"Our funnel rate is dropping. What's wrong?"
- Decompose: which step's conversion dropped?
- Segment: which user segment is driving it?
- Cross-check: is the dropping segment newly acquired?
- Test hypotheses against the data; don't guess.
"Help me audit our instrumentation"
- Pull the event inventory (last 30 days, all events fired ≥10x).
- Tag PII risk, naming inconsistency, gaps.
- Identify the events that should be fired but aren't.
- Build the remediation backlog with owners.
Anti-patterns to avoid
- More dashboards = more insight. Usually inverse. Cull aggressively.
- Confusing event volume for insight. Tracking everything badly is worse than tracking a few things well.
- PII in event properties. Privacy + compliance nightmare.
- Custom event names per developer. Naming convention or chaos.
- No event documentation. Future you and the next analyst will hate present you.
- One metric for the whole product. Different surfaces need different metrics.
- Vanity North Star. "Total signups" tells you nothing about value.
References
references/metric-tree-and-north-star.md— patterns by product type, tree structure, anti-patternsreferences/instrumentation-and-event-design.md— event taxonomy, naming, PII, schema disciplinereferences/cohort-retention-and-funnel-analysis.md— analysis techniques, segmentation, anti-patterns
Related skills
product-team/ab-test-setup— experimentation (paired with metrics)product-team/product-strategist— strategy upstream of metricsdata-analytics/skills — for the data engineering sideengineering/data-quality-auditor— for instrumentation data qualityc-level-advisor/chief-data-officer-advisor— for platform decisions
© borghei, 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 6 other files (scripts, references) in product-team/product-analytics of borghei/Claude-Skills.
- SKILL.md
- references/cohort-retention-and-funnel-analysis.md
- references/instrumentation-and-event-design.md
- references/metric-tree-and-north-star.md
- scripts/event_taxonomy_auditor.py
- scripts/metric_tree_designer.py
- scripts/retention_cohort_analyzer.py
Open the folder on GitHubat commit c9a1487
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 skillborghei/Claude-Skills | 874 | — | ~2.1k | Automated safety check: Pass | MIT | |
| PostHog CLI Queriesdebugtheworldbot/keyStats | 1.5k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Retentioneering Contributingretentioneering/retentioneering-tools | 920 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Retentioneering Product Analyticsretentioneering/retentioneering-tools | 920 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Retention Analysisliangdabiao/claude-data-analysis-ultra-main | 290 | 1 repos | ~1.3k | Automated safety check: Notes | None | |
| Feature Analytics Instrumentation Plannermistralai/mistral-vibe | 5.1k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 |
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Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering.
Retention Analysis
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- Official
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Funnel Analysis
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Categories
Questions about Product Analytics
What does Product Analytics do?
Product analytics for instrumenting products, defining metrics, and building retention funnels. Product Analytics is an agent skill from borghei/Claude-Skills. Product analytics for instrumenting products, defining metrics, and building retention funnels.
When should I use Product Analytics?
Product Analytics fits situations like: designing a metric tree; instrumenting a feature; auditing instrumentation; defining a North Star.
How do I install Product Analytics in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill product-analytics -a claude-code`. Or copy the skill folder (product-team/product-analytics in borghei/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 borghei/Claude-Skills --skill product-analytics -a codex`. Or copy the skill folder (product-team/product-analytics in borghei/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 borghei/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 (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Product Analytics use?
About 2.1k tokens (SKILL.md is roughly 8.5k 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 5.6k 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: PostHog CLI Queries (debugtheworldbot/keyStats, 1.5k stars), Retentioneering Contributing (retentioneering/retentioneering-tools, 920 stars), Retentioneering Product Analytics (retentioneering/retentioneering-tools, 920 stars) and Retention Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Product Analytics?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/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.