Pm Metrics
serejaris/personal-corp-os
Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study.
Install Data Analysis Standard
$ npx skills add mohitagw15856/pm-claude-skills --skill data-analysis-standard -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-analysis-standard --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/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-analysis-standard .claude/skills/data-analysis-standard && 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 "data-analysis-standard" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-analysis-standard into .claude/skills/data-analysis-standard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-standard", 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/mohitagw15856/pm-claude-skills/tree/main/skills/data-analysis-standardType 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 mohitagw15856/pm-claude-skills --skill data-analysis-standard -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-analysis-standard --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-analysis-standard .agents/skills/data-analysis-standard && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-analysis-standard" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-analysis-standard into .agents/skills/data-analysis-standard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-standard", 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 mohitagw15856/pm-claude-skills --skill data-analysis-standard -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-analysis-standard --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-analysis-standard .cursor/skills/data-analysis-standard && 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 "data-analysis-standard" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-analysis-standard into .cursor/skills/data-analysis-standard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-standard", 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/mohitagw15856/pm-claude-skills.git --path skills/data-analysis-standard--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 mohitagw15856/pm-claude-skills --skill data-analysis-standard -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-analysis-standard --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-analysis-standard .gemini/skills/data-analysis-standard && 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 "data-analysis-standard" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-analysis-standard into .gemini/skills/data-analysis-standard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-standard", 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 mohitagw15856/pm-claude-skills data-analysis-standardInstalls 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 mohitagw15856/pm-claude-skills --skill data-analysis-standard -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-analysis-standard .github/skills/data-analysis-standard && 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 "data-analysis-standard" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-analysis-standard into .github/skills/data-analysis-standard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-standard", 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 mohitagw15856/pm-claude-skills --skill data-analysis-standard -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mohitagw15856/pm-claude-skills data-analysis-standard --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-analysis-standard .opencode/skills/data-analysis-standard && 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 "data-analysis-standard" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/data-analysis-standard into .opencode/skills/data-analysis-standard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-standard", 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
data-analysis-standard- GitHub stars
- 1.4k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 756 words
- Files
- 4 (incl. references)
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study.
- Works in 4 steps: What changed? (describe the metric and… → Why did it change? (root cause —… → So what? (business or product impact) → …
- Asked to analyse product metrics
- SKILL.md covers Analysis Framework: The…, Metric Triage Template, Funnel Analysis Structure and Cohort Analysis Guidelines, plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Analysis Standard is an agent skill from mohitagw15856/pm-claude-skills. Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/analysis-integrity.md`, `references/worked-example.md` and `templates/analysis-writeup.md`).
It sits in Data & Analytics, covering Data analysis, Product analytics and Root cause analysis. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.
When your agent uses it
- Asked to analyse product metrics
- Investigate a drop in conversion
- Explain a data change to stakeholders
- Find the root cause of a metric movement
Example prompts
- “/data-analysis-standard”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- What changed? (describe the metric and its movement)
- Why did it change? (root cause — segment, funnel step, cohort, channel)
- So what? (business or product impact)
- Now what? (recommended action with confidence level)
What it can do on your machine
Read from SKILL.md and the folder at commit 1cbf1f0. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
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
Data Analysis Standard loads about 1.7k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 756 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 756 words, ~1,652 tokens.
Download SKILL.mdSave it as
.claude/skills/data-analysis-standard/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- data-analysis-standard
- description
- Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Data Analysis Standard Skill
Turn raw numbers into product decisions. Structure every analysis with a clear question, methodology, finding, and recommended action.
Analysis Framework: The 4-Question Method
Every analysis starts here:
- What changed? (describe the metric and its movement)
- Why did it change? (root cause — segment, funnel step, cohort, channel)
- So what? (business or product impact)
- Now what? (recommended action with confidence level)
Never deliver data without answering all four. A chart with no narrative is not an analysis.
Metric Triage Template
Use when a metric has moved unexpectedly:
METRIC: [Name]
MOVEMENT: [X% change over Y period]
BASELINE: [What was normal]
SEGMENTATION CHECK:
- By platform (iOS / Android / Web)?
- By user cohort (new / returning / power users)?
- By acquisition channel?
- By geography?
- By plan/tier?
ROOT CAUSE HYPOTHESIS:
1. [Most likely explanation] — Evidence: [data point]
2. [Alternative explanation] — Evidence: [data point]
3. [Ruling out] — Eliminated because: [reason]
CONCLUSION: [Single sentence answer to "why did this change?"]
CONFIDENCE: [High / Medium / Low] — based on [data available]Funnel Analysis Structure
| Stage | Metric | Current | Benchmark/Target | Drop-off % | Notes |
|---|---|---|---|---|---|
| [Top of funnel] | [Users] | [N] | [N] | — | |
| [Step 2] | [Users] | [N] | [N] | [X%] | |
| [Step 3] | [Users] | [N] | [N] | [X%] | |
| [Conversion] | [Users] | [N] | [N] | [X%] |
Biggest drop-off: [Step X → Step Y] — Hypothesis: [reason] Recommended investigation: [specific query or test]
Cohort Analysis Guidelines
Always define:
- Cohort definition: [What groups users — signup week, first action, plan type]
- Retention metric: [What counts as retained — login, core action, revenue]
- Retention window: [D1, D7, D30, W4, M3, etc.]
Output a cohort retention table and annotate:
- Baseline retention for each cohort
- Cohorts that over/underperform and why (feature launch? campaign? seasonal?)
- Trend direction across cohorts (improving / declining / stable)
Stakeholder Analysis Output Format
[Analysis Title] — [Date]
Question being answered: [Specific question in plain English] Time period: [Date range] Data source: [Where data comes from]
Finding:
[1–2 sentence plain-English summary of what the data shows]
Key chart / table: [Include or describe]
Root cause: [Best explanation with evidence]
Confidence level: [High / Medium / Low] — [reason]
Recommended action:
- [Immediate action — owner, timeline]
- [Investigation needed — what to check next]
- [Monitoring — what metric to watch and at what cadence]
What this analysis does NOT tell us: [Important caveat — what data is missing or what can't be concluded]
Required Inputs
Ask the user for these if not provided:
- Metric or question being investigated
- Time period (what changed, from when to when)
- Data available (which segments, sources, or queries you have access to)
- Business context (what decision this analysis informs)
- Audience (who will read this — exec / team / data team)
Deeper Materials
This skill ships with support files — use them when they are available:
references/analysis-integrity.md— Analysis Integrity: the Checks Between Query and Conclusion. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.templates/analysis-writeup.md— a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.
Show full SKILL.md (341 more words)Show less
Scoring Rubric (0–40)
Score any output of this skill before handing it over; 32+ is ship-quality.
| Dimension | 0 | 5 | 10 |
|---|---|---|---|
| Four-question completeness | Describes what changed and stops | Covers what/why but "so what / now what" are thin | All four answered with proportionate depth; the "now what" is decision-ready |
| Evidence behind the root cause | Root cause asserted from intuition | One supporting data point, alternatives unexamined | Root cause tested against at least one rival explanation, with the discriminating evidence shown |
| Uncertainty honesty | Reads as certain; no confidence statement | Confidence stated but not justified | Confidence level justified, and "what the data cannot tell us" names the real blind spots, not token ones |
| Actionability | Findings with no action | Action named but ownerless or dateless | Recommended action has an owner, a timeline, and a stated expected effect worth checking later |
Quality Checks
- Analysis answers all 4 questions: what changed, why, so what, now what
- Root cause has evidence (not just hypothesis)
- Confidence level is stated and justified
- What the data cannot tell us is explicitly named
- Recommended action includes an owner and timeline
Anti-Patterns
- Do not present correlations as causation — always state the distinction explicitly
- Do not report a metric movement without stating the time window and comparison baseline
- Do not skip the "so what" — raw observations without recommended actions are incomplete analysis
- Do not overstate confidence — label hypotheses clearly and note what data would be needed to confirm them
- Do not ignore segment breakdowns — aggregate metrics can mask opposing trends in sub-segments
Guidelines
- Always state what the data cannot tell you — never oversell confidence
- Correlations are not causation — flag this every time
- If the user has no baseline, recommend establishing one before drawing conclusions
- Recommend the simplest chart for each finding: bar for comparison, line for trends, scatter for correlation, table for detailed breakdowns
- Always specify the time window — "conversion dropped" is meaningless without "from X to Y over Z period"
Example Trigger Phrases
- "Analyse product metrics."
- "Investigate a drop in conversion."
- "Explain a data change to stakeholders."
- "Find the root cause of a metric movement."
© mohitagw15856, 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 (references) in skills/data-analysis-standard of mohitagw15856/pm-claude-skills.
- SKILL.md
- references/analysis-integrity.md
- references/worked-example.md
- templates/analysis-writeup.md
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
Data Analysis Standard 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 |
|---|---|---|---|---|---|---|
| Data Analysis Standard this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Pm Metricsserejaris/personal-corp-os | 229 | — | ~3k | Automated safety check: Pass | MIT | |
| Feature Analytics Instrumentation Plannermistralai/mistral-vibe | 5.1k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Anomaly Investigationgaasher/Agent-Loop-Skills | 174 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Analytics Interpretationgustavscirulis/snapgrid | 116 | 1 repos | ~4.1k | Automated safety check: Pass | Custom licence | |
| 13 Data Analysis Globalminhnv0807/ai-business-skills | 609 | — | ~4k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Data Analysis Standard
What does Data Analysis Standard do?
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Data Analysis Standard is an agent skill from mohitagw15856/pm-claude-skills. Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study.
When should I use Data Analysis Standard?
Data Analysis Standard fits situations like: asked to analyse product metrics; investigate a drop in conversion; explain a data change to stakeholders; find the root cause of a metric movement.
How do I install Data Analysis Standard in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-analysis-standard -a claude-code`. Or copy the skill folder (skills/data-analysis-standard in mohitagw15856/pm-claude-skills) into .claude/skills/data-analysis-standard in your project. Claude Code loads it when a task matches its description.
How do I install Data Analysis Standard in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-analysis-standard -a codex`. Or copy the skill folder (skills/data-analysis-standard in mohitagw15856/pm-claude-skills) into .agents/skills/data-analysis-standard in your project. Codex loads it when a task matches its description.
Can I use Data Analysis Standard 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 mohitagw15856/pm-claude-skills --skill data-analysis-standard -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-analysis-standard, .gemini/skills/data-analysis-standard, .github/skills/data-analysis-standard and .opencode/skills/data-analysis-standard in your project.
What does Data Analysis Standard need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Analysis Standard is instructions for the agent only.
Does Data Analysis Standard 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 Data Analysis Standard 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 Data Analysis Standard use?
Data Analysis Standard 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 Data Analysis Standard use?
About 1.7k tokens (SKILL.md is roughly 6.6k 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 2.3k tokens, read only when the agent opens those files.
What are the alternatives to Data Analysis Standard?
Skills that share tags, products or a category with Data Analysis Standard: Pm Metrics (serejaris/personal-corp-os, 229 stars), Feature Analytics Instrumentation Planner (mistralai/mistral-vibe, 5.1k stars), Anomaly Investigation (gaasher/Agent-Loop-Skills, 174 stars) and Analytics Interpretation (gustavscirulis/snapgrid, 116 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Analysis Standard?
mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.
Source: mohitagw15856/pm-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.