Agent skill

Product Data Audit

by serejaris in serejaris/personal-corp-os

A skill your agent uses when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours.

MITAuto-check passedData & Analytics

Install Product Data Audit

skills CLI

$ npx skills add serejaris/personal-corp-os --skill product-data-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install serejaris/personal-corp-os product-data-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/product-data-audit .claude/skills/product-data-audit && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "product-data-audit" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/product-data-audit into .claude/skills/product-data-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-data-audit", 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

$skill-installer install https://github.com/serejaris/personal-corp-os/tree/main/skills/product-data-audit

Type 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.

skills CLI

$ npx skills add serejaris/personal-corp-os --skill product-data-audit -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install serejaris/personal-corp-os product-data-audit --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/product-data-audit .agents/skills/product-data-audit && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "product-data-audit" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/product-data-audit into .agents/skills/product-data-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-data-audit", 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.

skills CLI

$ npx skills add serejaris/personal-corp-os --skill product-data-audit -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install serejaris/personal-corp-os product-data-audit --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/product-data-audit .cursor/skills/product-data-audit && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "product-data-audit" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/product-data-audit into .cursor/skills/product-data-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-data-audit", 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

$ gemini skills install https://github.com/serejaris/personal-corp-os.git --path skills/product-data-audit

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add serejaris/personal-corp-os --skill product-data-audit -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install serejaris/personal-corp-os product-data-audit --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/product-data-audit .gemini/skills/product-data-audit && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "product-data-audit" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/product-data-audit into .gemini/skills/product-data-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-data-audit", 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.

GitHub CLI

$ gh skill install serejaris/personal-corp-os product-data-audit

Installs 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).

skills CLI

$ npx skills add serejaris/personal-corp-os --skill product-data-audit -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/product-data-audit .github/skills/product-data-audit && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "product-data-audit" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/product-data-audit into .github/skills/product-data-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-data-audit", 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.

skills CLI

$ npx skills add serejaris/personal-corp-os --skill product-data-audit -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install serejaris/personal-corp-os product-data-audit --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/product-data-audit .opencode/skills/product-data-audit && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "product-data-audit" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/product-data-audit into .opencode/skills/product-data-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "product-data-audit", 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-data-audit
GitHub stars: 229
Token cost: ~1.3k tokens
SKILL.md length: 492 words
Files: 8 (incl. references, assets)
Skills in repo: 36
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours.

Works in 5 steps: Определить объект аудита → Собрать данные → Сгенерировать отчёт → …
Auditing a product
SKILL.md covers Процесс and Типичные ошибки
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Product Data Audit is an agent skill from serejaris/personal-corp-os. Use when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours. Triggers on "аудит продукта", "product audit", "data audit", "аудит данных", "аудит бизнеса", "проанализируй экосистему", "аудит систем".

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files and assets (for example `README.md`, `README.ru.md` and `references/html-design-spec.md`).

It sits in Data & Analytics, covering Data analysis. The repository describes itself as: Personal Corp OS — управление личной компанией через AI-агентов: задачи вне головы, отделы вместо памяти, недельное ретро. Открытые скиллы для Claude Code и Codex. The licence is MIT.

When your agent uses it

Auditing a product
Project ecosystem — analyzing data sources
Implementation contours
Проанализируй экосистему

Example prompts

“product audit”
“data audit”
“/product-data-audit”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

Определить объект аудита
Собрать данные
Сгенерировать отчёт
Сгенерировать HTML
Открыть в браузере

What it can do on your machine

Read from SKILL.md and the folder at commit 95e36c3. 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 (its code samples are dot and bash).
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 Data Audit loads about 1.3k tokens when it runs, and up to ~8.2k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 492 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~75
When it runs · the whole SKILL.md, loaded when a task matches: ~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~8.2k

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 serejaris/personal-corp-os at commit 95e36c3, republished under its MIT licence (© serejaris). 492 words, ~1,299 tokens.

Download SKILL.mdSave it as .claude/skills/product-data-audit/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.

name: product-data-audit
description: Use when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours. Triggers on "аудит продукта", "product audit", "data audit", "аудит данных", "аудит бизнеса", "проанализируй экосистему", "аудит систем".

Product & Data Audit

Глубокий аудит продукта/бизнеса: данные, системы, решения, узкие места, контуры внедрения. На выходе — интерактивная HTML-визуализация (12 секций). Markdown-версия — опционально по запросу.

Процесс

dot

digraph audit_flow {
    "Определить объект аудита" [shape=box];
    "Собрать данные" [shape=box];
    "Сгенерировать HTML (12 секций)" [shape=box];
    "Открыть в браузере" [shape=box];

    "Определить объект аудита" -> "Собрать данные";
    "Собрать данные" -> "Сгенерировать HTML (12 секций)";
    "Сгенерировать HTML (12 секций)" -> "Открыть в браузере";
}

1. Определить объект аудита

Спросить у пользователя, если неочевидно:

Аудит одного продукта, линейки или всей экосистемы бренда?
Какие файлы/системы читать?

workdir = корень git-репозитория текущего проекта. Если неочевидно — спросить у пользователя до шага 2.

2. Собрать данные

Обнаружить и прочитать источники по категориям (конкретные файлы зависят от проекта):

Архитектура проекта — CLAUDE.md, README, AGENTS.md, .cursor/rules, любые конфиг-файлы агентов
Канонические данные — файлы с ценами, метриками, офферами, аудиторией (искать по содержимому, не по имени)
Стратегия и планы — документы с insights, roadmap, OKR, ретро, планы
Операционные правила — правила в .claude/rules/, playbooks, runbooks
Рабочие документы — docs/, notes/, analysis/ — всё что есть
Управление задачами — GitHub Projects/Issues, Linear, Jira (если доступны через CLI)
Системы и конфиги — боты, CRM, базы, аналитика, мониторинг (конфиги, схемы, .env.example)
Внешние данные — API-запросы, CLI-команды для свежих метрик (если доступны)

Для каждого источника фиксировать: что внутри, дата snapshot, качество, ограничения.

Обнаружение источников: начать с CLAUDE.md / README.md в корне — они обычно описывают структуру проекта и указывают на канонические файлы. Затем ls + glob по корню для обнаружения остального.

Свежесть данных: если snapshot старше 14 дней — добавить [snapshot: YYYY-MM-DD] рядом с числом. Если старше 30 дней — пометить [УСТАРЕЛО: YYYY-MM-DD].

Если канонические файлы не найдены: пометить числовые утверждения НЕИЗВЕСТНО. [источник не найден] и продолжить аудит с доступными данными.

Show full SKILL.md (261 more words)Show less

3. Сгенерировать отчёт

Основной артефакт = HTML ({workdir}/research/product-data-audit.html). Markdown-версия (.md) — опциональна, генерировать только по явному запросу пользователя.

Структура — 12 секций (0–11), см. references/report-structure.md. Секция 0 = диаграмма экосистемы.

Тегирование: каждое утверждение маркировать:

ФАКТ. — подтверждено данными, указать источник
ГИПОТЕЗА. — логичный вывод, требует проверки
НЕИЗВЕСТНО. — слепое пятно, данных нет

Правило числовой конкретики (обязательно):

Каждый ФАКТ должен содержать конкретные числа, если они доступны в источниках
После числа — ссылка на источник в формате [файл:строка] или [система → запрос]
Если число можно получить запросом (SQL, API, file read) — получить, не оставлять "самый маржинальный"
Плохо: "канал X — самый маржинальный"
Хорошо: "канал X — $Y/единица, ~$Z/час при Nч работы [<файл-с-ценами>:<метка>]"
Если число недоступно — явно пометить [число не найдено] вместо голого утверждения

Терминология: русский язык, англицизмы только для устоявшихся стандартов (CRM, API, KPI). См. references/terminology.md.

Рекомендации по отсутствующим артефактам: в секции 7 (контуры внедрения) проверить наличие 18 операционных артефактов из references/missing-artifacts-checklist.md. Отсутствующие — включить как рекомендации с приоритетом и минимальной версией. 4 категории: стратегия (NSM, OKR), AI-native (CLAUDE.md, промпты, runbook), инфраструктура данных (SSOT, определения метрик), governance (журнал решений, эскалация).

4. Сгенерировать HTML

Создать {workdir}/research/product-data-audit.html по дизайн-спецификации из references/html-design-spec.md. Навигация = 12 секций (0–11).

5. Открыть в браузере

bash

open {workdir}/research/product-data-audit.html
# Если `open` недоступна — вывести абсолютный путь для ручного открытия

Типичные ошибки

Ошибка	Как избежать
Англицизмы при наличии русского аналога	Проверять references/terminology.md
Факт без источника	Каждый `ФАКТ.` ссылается на файл/систему
Факт без числа	Если число доступно — получить и указать. "Самый маржинальный" → "$Y/единица, ~$Z/час [источник]"
Stale данные без маркировки	Snapshot > 14 дней → `[snapshot: дата]`, > 30 дней → `[УСТАРЕЛО: дата]`
Смешение фактов и гипотез	Не приписывать уверенность неподтверждённому
Нет диаграммы экосистемы	Секция 0 обязательна: SVG с 4 слоями и потоками между нодами
Нет секции "Неизвестное"	Слепые пятна важнее фактов для решений

Files

SKILL.md and 7 other files (references, assets) in skills/product-data-audit of serejaris/personal-corp-os.

SKILL.md
README.md
README.ru.md
assets/illustration.png
references/html-design-spec.md
references/missing-artifacts-checklist.md
references/report-structure.md
references/terminology.md

Open the folder on GitHubat commit 95e36c3

Compare with similar skills

Product Data Audit 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.

Product Data Audit compared with similar skills
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Product Data Audit this skillserejaris/personal-corp-os	229	—	~1.3k	Automated safety check: Pass	MIT	3 days ago
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Excel and CSV Data Analysisbytedance/deer-flow	84k	4 repos	~2.2k	Automated safety check: Pass	MIT	today
Exploratory Data AnalysisOleafly/Oleafly	212	2 repos	~3.4k	Automated safety check: Notes	MIT	today
Pandas ProJeffallan/claude-skills	12k	1 repos	~1.5k	Automated safety check: Pass	MIT	7 days ago
Python Executorcortega26/chile-hub	113	2 repos	~1.5k	Automated safety check: Pass	MIT	today

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Questions about Product Data Audit

What does Product Data Audit do?

A skill your agent uses when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours. Product Data Audit is an agent skill from serejaris/personal-corp-os. Use when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours.

When should I use Product Data Audit?

Product Data Audit fits situations like: auditing a product; project ecosystem — analyzing data sources; implementation contours; Проанализируй экосистему.

How do I install Product Data Audit in Claude Code?

Run `npx skills add serejaris/personal-corp-os --skill product-data-audit -a claude-code`. Or copy the skill folder (skills/product-data-audit in serejaris/personal-corp-os) into .claude/skills/product-data-audit in your project. Claude Code loads it when a task matches its description.

How do I install Product Data Audit in Codex?

Run `npx skills add serejaris/personal-corp-os --skill product-data-audit -a codex`. Or copy the skill folder (skills/product-data-audit in serejaris/personal-corp-os) into .agents/skills/product-data-audit in your project. Codex loads it when a task matches its description.

Can I use Product Data Audit 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 serejaris/personal-corp-os --skill product-data-audit -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-data-audit, .gemini/skills/product-data-audit, .github/skills/product-data-audit and .opencode/skills/product-data-audit in your project.

What does Product Data Audit need to run?

SKILL.md names no scripts, command-line tools or credentials: Product Data Audit is instructions for the agent only.

Does Product Data Audit 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 Data Audit 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 Product Data Audit use?

Product Data Audit 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 Data Audit use?

About 1.3k tokens (SKILL.md is roughly 5.2k 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 6.9k tokens, read only when the agent opens those files.

What are the alternatives to Product Data Audit?

Skills that share tags, products or a category with Product Data Audit: Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 84k stars), Exploratory Data Analysis (Oleafly/Oleafly, 212 stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Data Audit?

serejaris (a GitHub user) maintains it in serejaris/personal-corp-os, which has 229 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on October 7, 2026.

Source: serejaris/personal-corp-os on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Product Data Audit

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Product & Data Audit

Процесс

1. Определить объект аудита

2. Собрать данные

3. Сгенерировать отчёт

4. Сгенерировать HTML

5. Открыть в браузере

Типичные ошибки

Files

Compare with similar skills

Similar skills

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Related

Questions about Product Data Audit