Chdb Datastore
vemetric/vemetric
A skill your agent uses when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.
Agent skill
Retentioneering Product Analytics
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering.
Install Retentioneering Product Analytics
$ npx skills add retentioneering/retentioneering-tools --skill retentioneering-product-analytics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install retentioneering/retentioneering-tools retentioneering-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/retentioneering/retentioneering-tools.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/retentioneering-product-analytics .claude/skills/retentioneering-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 "retentioneering-product-analytics" agent skill from https://github.com/retentioneering/retentioneering-tools/tree/master/.agents/skills/retentioneering-product-analytics into .claude/skills/retentioneering-product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retentioneering-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/retentioneering/retentioneering-tools/tree/master/.agents/skills/retentioneering-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 retentioneering/retentioneering-tools --skill retentioneering-product-analytics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install retentioneering/retentioneering-tools retentioneering-product-analytics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/retentioneering/retentioneering-tools.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/retentioneering-product-analytics .agents/skills/retentioneering-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 "retentioneering-product-analytics" agent skill from https://github.com/retentioneering/retentioneering-tools/tree/master/.agents/skills/retentioneering-product-analytics into .agents/skills/retentioneering-product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retentioneering-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 retentioneering/retentioneering-tools --skill retentioneering-product-analytics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install retentioneering/retentioneering-tools retentioneering-product-analytics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/retentioneering/retentioneering-tools.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/retentioneering-product-analytics .cursor/skills/retentioneering-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 "retentioneering-product-analytics" agent skill from https://github.com/retentioneering/retentioneering-tools/tree/master/.agents/skills/retentioneering-product-analytics into .cursor/skills/retentioneering-product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retentioneering-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/retentioneering/retentioneering-tools.git --path .agents/skills/retentioneering-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 retentioneering/retentioneering-tools --skill retentioneering-product-analytics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install retentioneering/retentioneering-tools retentioneering-product-analytics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/retentioneering/retentioneering-tools.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/retentioneering-product-analytics .gemini/skills/retentioneering-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 "retentioneering-product-analytics" agent skill from https://github.com/retentioneering/retentioneering-tools/tree/master/.agents/skills/retentioneering-product-analytics into .gemini/skills/retentioneering-product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retentioneering-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 retentioneering/retentioneering-tools retentioneering-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 retentioneering/retentioneering-tools --skill retentioneering-product-analytics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/retentioneering/retentioneering-tools.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/retentioneering-product-analytics .github/skills/retentioneering-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 "retentioneering-product-analytics" agent skill from https://github.com/retentioneering/retentioneering-tools/tree/master/.agents/skills/retentioneering-product-analytics into .github/skills/retentioneering-product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retentioneering-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 retentioneering/retentioneering-tools --skill retentioneering-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 retentioneering/retentioneering-tools retentioneering-product-analytics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/retentioneering/retentioneering-tools.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/retentioneering-product-analytics .opencode/skills/retentioneering-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 "retentioneering-product-analytics" agent skill from https://github.com/retentioneering/retentioneering-tools/tree/master/.agents/skills/retentioneering-product-analytics into .opencode/skills/retentioneering-product-analytics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "retentioneering-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
retentioneering-product-analytics- GitHub stars
- 927
- Token cost
- ~1.6k tokens
- SKILL.md length
- 591 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 2
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering.
- Works in 6 steps: Environment → Inspect the data BEFORE choosing methods → Frame the product question, then pick… → …
- The user provides CSV
- SKILL.md covers Objective, Bundled references (read on…, Required event-log semantics and Workflow
- Runs Python scripts from its folder; calls python
What it does
Retentioneering Product Analytics is an agent skill from 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. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event…
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/analysis-recipes.md`, `references/api-map.md` and `references/gotchas-and-validation.md`). Compatibility notes: Requires Python = 3.10 and Retentioneering 5.x. Designed for local CSV, Parquet, and pandas event logs. Network access is not required for local analysis.
It sits in Data & Analytics, covering Customer journey mapping, Product analytics and DataFrames. It works with pandas, Python and Model Context Protocol. The repository describes itself as: Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and…. The licence is Apache-2.0.
When your agent uses it
- The user provides CSV
- Database event data containing user
- Timestamp columns
- Asks why users convert
Example prompts
- “/retentioneering-product-analytics”
Requirements
- Python 3
- Compatibility (from SKILL.md): Requires Python >= 3.10 and Retentioneering 5.x. Designed for local CSV, Parquet, and pandas event logs. Network access is not required for local analysis.
Workflow steps
6 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 fda32f2. 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:
python
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.
Compatibility
Requires Python >= 3.10 and Retentioneering 5.x. Designed for local CSV, Parquet, and pandas event logs. Network access is not required for local analysis.
From compatibility in the SKILL.md frontmatter.
Context cost
Retentioneering Product Analytics loads about 1.6k tokens when it runs, and up to ~8k if it reads all its reference files. Until then it costs about 139 tokens; SKILL.md has 591 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 retentioneering/retentioneering-tools at commit fda32f2, republished under its Apache-2.0 licence (© retentioneering). 591 words, ~1,560 tokens.
Download SKILL.mdSave it as
.claude/skills/retentioneering-product-analytics/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- retentioneering-product-analytics
- description
- Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.
- compatibility
- Requires Python >= 3.10 and Retentioneering 5.x. Designed for local CSV, Parquet, and pandas event logs. Network access is not required for local analysis.
- license
- Apache-2.0
- metadata.author
- retentioneering
- metadata.version
- 1.0.0
- metadata.package
- retentioneering
- metadata.category
- data-analysis
- metadata.keywords
- clickstream, event log, user paths, customer journey, product analytics, funnel analysis, retention, churn, behavioral segmentation, transition graph, step…
- metadata.homepage
- https://retentioneering.com
- metadata.documentation
- https://retentioneering.com/docs
- metadata.repository
- https://github.com/retentioneering/retentioneering-tools
Retentioneering product analytics
Objective
Turn event-level behavioral data into a reproducible answer to a product question — why users convert, churn, loop, or abandon — using user trajectories, transitions, funnels, and behavioral segments.
Do not merely generate visualizations. Connect each output to the question, separate observation from interpretation, and never present path correlations as causal effects.
Bundled references (read on demand, not upfront)
| File | Read it when |
|---|---|
references/api-map.md | before writing any Retentioneering call — verified signatures, argument conventions, return shapes for 5.x |
references/analysis-recipes.md | after the question is clear — 10 field-tested patterns (R1–R10) with skeletons and pitfalls |
references/gotchas-and-validation.md | before executing (API gotchas G1–G10) and before presenting (integrity checklist B1–B10) |
scripts/inspect_event_log.py | step 2 — automated data profiling and schema suggestion |
Required event-log semantics
Minimum: a path identifier (user or session), an event name, a timestamp (or a reliable order column — see gotcha G2 for order-only data). Useful extras: session id, segment attributes (device, source, plan), event properties, conversion labels.
Workflow
1. Environment
- Confirm the package:
python -c "import retentioneering; print(retentioneering.__version__)". Expect 5.x; this skill's API map is version-verified for 5.0 — on a different major version, trust installed docstrings over the map. - Locate the event data (CSV / Parquet / frames in existing code). Never modify inputs.
- Do not invent methods: anything not in
references/api-map.mdmust be verified against the installed package before use.
2. Inspect the data BEFORE choosing methods
Run scripts/inspect_event_log.py <path> [--sep ...] (or replicate its checks inline for
in-memory frames). It profiles columns, infers the user/event/timestamp mapping, checks
timestamp parseability, duplicates, per-path ordering, path-length distribution, and
emits artifacts/data-profile.json plus a ready-to-paste Eventstream(...) schema.
Report to the user before proceeding: inferred mapping, row/user/event-type counts, covered period, and any red flags (nulls in key columns, timestamp ties, suspected bots or ultra-long paths, order-only timestamps). Confirm the mapping if inference is ambiguous.
3. Frame the product question, then pick the SMALLEST recipe
Map the question to a recipe in references/analysis-recipes.md:
navigation structure/loops → transition graph (R1/R6) · before/after an anchor →
step matrix (R3) · ordered conversion flow → funnel (R1/R2) · what winners do
differently → diff on a funnel-stage segment (R2) · heterogeneous users → clustering
without target leakage (R5) · between two funnel levels → truncate micro-journey (R4) ·
intervention timing → time-to-outcome (R7) · cross-segment scan → segment overview (R8) ·
value of a fix → Markov what-if (R9, advanced).
Combine recipes only when each addition resolves a distinct uncertainty.
Show full SKILL.md (210 more words)Show less
4. Execute reproducibly
- Prefer a rerunnable script (or a notebook executed top-to-bottom) over ad-hoc cells.
- Write artifacts to a dedicated output directory (
artifacts/by default). - Log every filtering rule and its row/path impact; never silently drop data (integrity item B2).
- Use
sample_paths(frac=, random_state=)for stable subsamples; stochastic steps get explicit seeds. - Record lineage: save
processed.recipe()and the package version intoartifacts/run-metadata.json— any artifact must be regenerable from raw data viaEventstream.from_recipe(raw_df, recipe).
5. Validate before presenting
Work through references/gotchas-and-validation.md section B. Non-negotiables:
every percentage names its denominator; population filters are disclosed with counts;
survivorship and exposure confounds addressed; no outcome leakage into features;
small cells flagged with n; caption numbers come from headless *_data twins;
visuals agree with tables.
6. Interpret and deliver
Structure the final answer as:
- Observed — numbers with denominators and n.
- Interpretation — what it likely means.
- Alternative explanations — selection, structure, censoring.
- Product hypotheses — each with the metric an experiment would move.
- Suggested next analyses / A-B tests.
- Limitations.
Deliverables: analysis script or executed notebook; artifacts/data-profile.json;
artifacts/metrics.csv (key tables); interactive HTML exports via
widget.export_html(..., title=, analysis=) — write analysis= captions AFTER
conclusions are final; artifacts/summary.md (mapping, filters, assumptions, versions,
findings, limitations, next steps); artifacts/run-metadata.json (versions, parameters,
seeds, recipe() lineage).
© retentioneering, Apache-2.0. 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 4 other files (scripts, references) in .agents/skills/retentioneering-product-analytics of retentioneering/retentioneering-tools.
- SKILL.md
- references/analysis-recipes.md
- references/api-map.md
- references/gotchas-and-validation.md
- scripts/inspect_event_log.py
Open the folder on GitHubat commit fda32f2
Compare with similar skills
Retentioneering 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 |
|---|---|---|---|---|---|---|
| Retentioneering Product Analytics this skillretentioneering/retentioneering-tools | 927 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Chdb Datastorevemetric/vemetric | 395 | 2 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| CSV Data Summarizercoffeefuelbump/csv-data-summarizer-claude-skill | 468 | 2 repos | ~1.4k | Automated safety check: Pass | None | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | 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 | 33k | 12 repos | ~1.6k | Automated safety check: Pass | MIT |
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Questions about Retentioneering Product Analytics
What does Retentioneering Product Analytics do?
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Retentioneering Product Analytics is an agent skill from 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.
When should I use Retentioneering Product Analytics?
Retentioneering Product Analytics fits situations like: the user provides CSV; database event data containing user; timestamp columns; asks why users convert.
How do I install Retentioneering Product Analytics in Claude Code?
Run `npx skills add retentioneering/retentioneering-tools --skill retentioneering-product-analytics -a claude-code`. Or copy the skill folder (.agents/skills/retentioneering-product-analytics in retentioneering/retentioneering-tools) into .claude/skills/retentioneering-product-analytics in your project. Claude Code loads it when a task matches its description.
How do I install Retentioneering Product Analytics in Codex?
Run `npx skills add retentioneering/retentioneering-tools --skill retentioneering-product-analytics -a codex`. Or copy the skill folder (.agents/skills/retentioneering-product-analytics in retentioneering/retentioneering-tools) into .agents/skills/retentioneering-product-analytics in your project. Codex loads it when a task matches its description.
Can I use Retentioneering 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 retentioneering/retentioneering-tools --skill retentioneering-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/retentioneering-product-analytics, .gemini/skills/retentioneering-product-analytics, .github/skills/retentioneering-product-analytics and .opencode/skills/retentioneering-product-analytics in your project.
What does Retentioneering Product Analytics need to run?
Going by SKILL.md and its folder, Retentioneering Product Analytics needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires Python >= 3.10 and Retentioneering 5.x. Designed for local CSV, Parquet, and pandas event logs. Network access is not required for local analysis. .
Does Retentioneering 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 Retentioneering 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 Retentioneering Product Analytics use?
Retentioneering Product Analytics is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Retentioneering Product Analytics use?
About 1.6k tokens (SKILL.md is roughly 6.2k 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 6.4k tokens, read only when the agent opens those files.
What are the alternatives to Retentioneering Product Analytics?
Skills that share tags, products or a category with Retentioneering Product Analytics: Chdb Datastore (vemetric/vemetric, 395 stars), CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars), Pandas Pro (Jeffallan/claude-skills, 12k stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Retentioneering Product Analytics?
retentioneering (a GitHub organization) maintains it in retentioneering/retentioneering-tools, which has 927 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 7, 2026.
Source: retentioneering/retentioneering-tools on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.