Data Analysis Standard
mohitagw15856/pm-claude-skills
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study.
Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям.
Install Pm Metrics
$ npx skills add serejaris/personal-corp-os --skill pm-metrics -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install serejaris/personal-corp-os pm-metrics --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/serejaris/personal-corp-os.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pm-metrics .claude/skills/pm-metrics && 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 "pm-metrics" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-metrics into .claude/skills/pm-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-metrics", 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/serejaris/personal-corp-os/tree/main/skills/pm-metricsType 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 serejaris/personal-corp-os --skill pm-metrics -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install serejaris/personal-corp-os pm-metrics --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pm-metrics .agents/skills/pm-metrics && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pm-metrics" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-metrics into .agents/skills/pm-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-metrics", 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 serejaris/personal-corp-os --skill pm-metrics -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install serejaris/personal-corp-os pm-metrics --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pm-metrics .cursor/skills/pm-metrics && 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 "pm-metrics" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-metrics into .cursor/skills/pm-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-metrics", 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/serejaris/personal-corp-os.git --path skills/pm-metrics--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 serejaris/personal-corp-os --skill pm-metrics -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install serejaris/personal-corp-os pm-metrics --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pm-metrics .gemini/skills/pm-metrics && 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 "pm-metrics" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-metrics into .gemini/skills/pm-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-metrics", 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 serejaris/personal-corp-os pm-metricsInstalls 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 serejaris/personal-corp-os --skill pm-metrics -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pm-metrics .github/skills/pm-metrics && 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 "pm-metrics" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-metrics into .github/skills/pm-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-metrics", 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 serejaris/personal-corp-os --skill pm-metrics -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install serejaris/personal-corp-os pm-metrics --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/serejaris/personal-corp-os.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pm-metrics .opencode/skills/pm-metrics && 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 "pm-metrics" agent skill from https://github.com/serejaris/personal-corp-os/tree/main/skills/pm-metrics into .opencode/skills/pm-metrics/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pm-metrics", 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
pm-metrics- GitHub stars
- 229
- Token cost
- ~3k tokens
- SKILL.md length
- 1,217 words
- Files
- 4 (incl. assets)
- Skills in repo
- 36
- Repo updated
- First seen
- Licence
- MIT
At a glance
Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям.
- Works in 9 steps: Data integrity check → North Star metric system → Growth metric analysis → …
- Анализ удержания
- SKILL.md covers Inputs, Step 1 — Data integrity check, Step 2 — North Star metric… and Step 3 — Growth metric analysis, plus 12 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Pm Metrics is an agent skill from serejaris/personal-corp-os. Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям. Включает декомпозицию North Star (L1/L2), диагностику retention-кривых, анализ воронки, разбор A/B-экспериментов, проверку соответствия OKR и фреймворк атрибуции аномалий. User-invoked only — do NOT auto-trigger. Triggers on /pm-metrics, "обзор метрик", "разбор воронки", "анализ удержания", "ретеншн", "A/B результаты", "review metrics", "DAU analysis", "retention analysis", "funnel analysis", "metric anomaly".
Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including assets (for example `README.md` and `README.ru.md`).
It sits in Data & Analytics, covering Product analytics, OKRs and executive reporting and Root cause 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
- Анализ удержания
- Retention analysis
- Funnel analysis
Example prompts
- “A/B результаты”
- “review metrics”
- “DAU analysis”
- “/pm-metrics”
Workflow steps
9 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 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 markdown).
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
Pm Metrics loads about 3k tokens when it runs. Until then it costs about 129 tokens; SKILL.md has 1,217 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 serejaris/personal-corp-os at commit 95e36c3, republished under its MIT licence (© serejaris). 1,217 words, ~2,963 tokens.
Download SKILL.mdSave it as
.claude/skills/pm-metrics/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- pm-metrics
- description
- Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям. Включает декомпозицию North Star (L1/L2), диагностику retention-кривых, анализ воронки, разбор A/B-экспериментов, проверку соответствия OKR и фреймворк атрибуции аномалий. User-invoked only — do NOT auto-trigger. Triggers on /pm-metrics, "обзор метрик", "разбор воронки", "анализ удержания", "ретеншн", "A/B результаты", "review metrics", "DAU analysis", "retention analysis", "funnel analysis", "metric anomaly".
pm-metrics — Product metrics review
Part of the Personal Corp framework — running a one-person business through AI agents. Systematically review product metrics, identify trend changes, locate root causes, output action recommendations. Includes North Star decomposition, retention diagnostics, funnel methodology, and A/B experiment reading.
Inputs
| Field | Required | Notes |
|---|---|---|
| Metric data | yes | Excel / CSV / pasted table / verbal description |
| Cycle | no | Weekly / monthly / quarterly review; default weekly |
| Focus | no | Full review / single-metric anomaly / experiment readout |
| Business context | no | Releases, campaigns, incidents in the period |
Mode: full data → complete review; single-metric change → focused anomaly analysis.
Step 1 — Data integrity check
- Confirm time coverage (current vs comparison period)
- Confirm metric coverage (which North Star / L1 / L2 are present)
- Flag missing critical data
Step 2 — North Star metric system
Decomposition: North Star → L1 → L2.
L1 dimensions:
- User growth: DAU/WAU/MAU, new, returning
- User engagement: core action frequency, session length, feature reach
- User retention: D1 / D7 / D30
- Conversion efficiency: signup → activation → paid step-by-step rates
- Business value: paid rate, ARPU, LTV
- Satisfaction: NPS, complaint rate, ratings
North Star selection guide:
| Product type | Recommended NSM | Typical L1 |
|---|---|---|
| Social / community | Weekly active posters | DAU/MAU ratio, interactions per user, D7 retention |
| Tools / productivity | Weekly users completing core task | Task completion rate, frequency, feature reach |
| E-commerce | Weekly transacting users | GMV, AOV, repeat rate, conversion |
| Content / media | Weekly content-consumption time | Time per user, completion rate, return rate |
| SaaS / B2B | Weekly active teams | Team penetration, feature depth, renewal rate |
Step 3 — Growth metric analysis
Definitions:
- DAU: distinct users with valid action that day
- WAU: distinct users active ≥ 1 day in 7
- MAU: distinct users active ≥ 1 day in 30
- DAU/MAU ratio (stickiness): > 0.5 very high, 0.3-0.5 high, 0.2-0.3 medium, < 0.2 low
User segmentation:
| Type | Definition | Focus |
|---|---|---|
| New | First-time user | Channel quality, activation rate |
| Active retained | Active in both periods | Depth, feature reach |
| Returning | Inactive last period, active this | Return reason, secondary retention |
| Churned | Active last period, inactive this | Churn cause, win-back potential |
| Dormant | Inactive multiple periods | Possibly permanent loss |
Growth identity: This-period MAU = prev-period retained + new + returning − churned
Step 4 — Retention analysis
Definitions:
- D1: % of new users who return on day 2
- D7: % of new users who return on day 8
- D30: % of new users who return on day 31
Retention benchmarks:
| Product type | D1 | D7 | D30 | Note |
|---|---|---|---|---|
| Social / messaging | > 70% | > 50% | > 35% | High-frequency essential |
| Tools | > 40% | > 25% | > 15% | "Use and leave" pattern |
| Content / news | > 35% | > 20% | > 10% | Many alternatives, lower retention |
| E-commerce | > 25% | > 15% | > 8% | Low-frequency, watch repeat rate instead |
| Games | > 40% | > 20% | > 10% | High variance by genre |
| SaaS / B2B | > 60% | > 45% | > 30% | High switching cost, higher baseline |
Retention-curve diagnosis:
- Steep drop (D1 → D7 loses > 60%): activation experience broken — users didn't find value
- Slow decay (D7 → D30 keeps falling, doesn't level): no long-term hook
- L-shape (levels off after D7): healthy, core user base formed
- Bounce-back (sudden uptick on a specific day): cyclical use pattern (e.g. weekday-only)
Retention segmentation:
- By channel: organic vs paid retention gap
- By behavior: completed activation vs not
- By cohort month: compare month-over-month curves to gauge product improvement
Step 5 — Conversion funnel analysis
Funnel construction:
- Define start and end points (e.g. homepage visit → payment success)
- Split into key intermediate steps (each step = a user decision point)
- Per-step rate = arriving at next / arriving at this
Funnel framework:
| Step | Action | Output |
|---|---|---|
| Draw | List steps + rates | Full funnel view |
| Identify bottleneck | Find lowest-rate step | Optimization focus |
| Benchmark | Compare history / industry / competitor | Gap quantification |
| Segment | By channel / device / user type | Locate problem cohort |
| Hypothesize | Why is the bottleneck there? | Optimization direction |
| Experiment | Propose A/B test | Action plan |
Common funnels:
- Acquisition: impression → click → install/signup → activation
- Activation: signup → onboarding done → core action first-trigger
- Payment: browse → cart → order → pay success
- Sharing: trigger → share click → recipient open → recipient conversion
Step 6 — A/B experiment readout
| Dimension | Standard | Note |
|---|---|---|
| Statistical significance | p < 0.05 | p > 0.05 → inconclusive, don't decide |
| Effect size | Lift > MDE | Significant but tiny lift may not be worth it |
| Sample size | Reaches pre-set N | "Significant" without N is unreliable |
| Duration | Covers ≥ 1-2 full weeks | Avoid weekday/weekend bias |
| AA check | Pre-period baselines match | Mismatch → split assignment is broken |
Decision framework:
- Significant + large effect → ship to all
- Significant + small effect → weigh long-term value vs cost
- Not significant → don't ship; investigate (wrong hypothesis? sample? execution?)
- Metric conflict (A up, B down) → weigh, prioritize North Star
Common pitfalls:
- Reading results too early (before reaching N)
- Looking only at primary metric, not guardrails
- Multiple peeks → false positives
- Ignoring novelty effect (early data inflated)
Show full SKILL.md (486 more words)Show less
Step 7 — OKR alignment check
| Check | Healthy | Anomaly signal |
|---|---|---|
| Coverage | Every KR has ≥ 1 trackable metric | A KR with no measurable proxy |
| Consistency | Metric direction matches KR target | Metric up but KR no progress |
| Pacing | Linear pacing ≥ 50% by mid-quarter | Severely behind schedule |
| Attribution | Metric movement attributable to team action | Metric improved due to industry tailwind, not team |
OKR progress table:
| OKR | KR metric | Target | Current | Progress % | Trend | Risk |
|---|---|---|---|---|---|---|
| {O1} | {KR1} | {target} | {current} | {X%} | Up/flat/down | On-track / at-risk / severe |
Step 8 — Anomaly attribution
When a metric moves anomalously, work the framework:
- Quantify: how much, starting when?
- Decompose: segment by channel / region / version / cohort to localize
- Time-align: what happened around the inflection? (release, campaign, incident, competitor move)
- Eliminate: rule out causes one by one until the most likely root remains
- Cross-check: verify the attribution via other metrics
Common causes:
| Category | Pattern | Verification |
|---|---|---|
| Release | Inflection aligns with deploy time | Compare per-version |
| Campaign | Up during campaign, drops after | Compare per-channel |
| Tech incident | Sudden drop + recovery | Check error logs and uptime |
| External | Industry-wide change | Compare with competitor / industry data |
| Channel mix | One channel changed dramatically | Per-channel decomposition |
| Seasonality | Same as YoY | Look at last year's same period |
Step 9 — Generate review report
# Product Metrics Review
**Period:** {date range}
**Product:** {name}
**Type:** {weekly / monthly / quarterly}
## 1. Health Overview
| Layer | Metric | Current | Previous | MoM | Target | Status |
|---|---|---|---|---|---|---|
| North Star | {} | {} | {} | {±X%} | {} | OK / warn / alert |
| L1 | {} | {} | {} | {±X%} | {} | OK / warn / alert |
**Overall judgment:** {one-sentence summary}
## 2. User Growth
- DAU: {value}, MoM {change}
- MAU: {value}, DAU/MAU = {stickiness}
- Composition: new {X}% / retained {Y}% / returning {Z}%
## 3. Retention
| Metric | Current | Previous | Benchmark | Assessment |
|---|---|---|---|---|
## 4. Funnel
| Step | Users | Rate | MoM | Bottleneck? |
|---|---|---|---|---|
**Bottleneck diagnosis:** {description}
## 5. Experiments / Feature Effects
| Experiment | Primary metric Δ | Significance | Conclusion |
|---|---|---|---|
## 6. OKR Progress
| KR | Target | Current | Progress | Risk |
|---|---|---|---|---|
## 7. Anomaly Attribution
| Anomaly | Magnitude | Start | Attribution | Confidence |
|---|---|---|---|---|
## 8. Key Insights
1. {insight 1: finding + data + meaning}
2. {insight 2}
3. {insight 3}
## 9. Action Recommendations
| Priority | Action | Linked metric | Expected impact | Owner |
|---|---|---|---|---|Review cadence
| Type | Frequency | Time | Audience | Focus |
|---|---|---|---|---|
| Weekly | Every Monday | 15-30 min | PM | NSM + anomalies + experiments |
| Monthly | Month start | 30-60 min | Product team | All L1 + retention + funnel + OKR pacing |
| Quarterly | Quarter end | 60-90 min | Product + ops + eng | Strategy review + OKR scoring + next-quarter plan |
Quality bar
- Metric definitions clear — every metric has a calculation note
- Data has comparisons — current always compared to previous, YoY, or target
- Attribution evidenced — no causation from correlation alone
- Recommendations actionable — owner-assignable
- Limitations tagged — call out small samples or data quality issues
Common analysis pitfalls
| Pitfall | Symptom | Fix |
|---|---|---|
| Simpson's paradox | Total goes up while every segment goes down | Always segment, never just look at totals |
| Survivorship bias | Only retained users analyzed, churned ignored | Compare retained vs churned behavior |
| Vanity metric | Cumulative signups only ever grow, not decision-useful | Use active metrics (DAU/WAU) instead |
| Time-window trap | Comparison window happens to be an outlier | Cross-validate across multiple windows |
| Goodhart's law | Target becomes a metric, stops measuring well | Set guardrails to prevent gaming |
Red lines
- No fabricated data — missing data → tag "missing", don't extrapolate
- Don't conflate correlation with causation — attribution must say "highly correlated" or "confirmed causal"
- Don't over-read small swings — small fluctuation → tag "within normal noise"
- Don't ignore negatives — flag risks even when overall is up
When input is incomplete
- Single metric only → focus on that anomaly, no full review
- No history → snapshot only, tag "no baseline, recommend establishing tracking"
- Verbal description → analyze based on description, tag "recommend exact data for verification"
- No targets → use industry benchmarks, suggest team set explicit targets
Related skills
/pm-feedback— pair quantitative anomaly with qualitative voice-of-customer/pm-prioritize— adjust priority based on metric findings/pm-roadmap— adjust roadmap based on OKR pacing
© serejaris, 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 (assets) in skills/pm-metrics of serejaris/personal-corp-os.
- SKILL.md
- README.md
- README.ru.md
- assets/illustration.png
Open the folder on GitHubat commit 95e36c3
Compare with similar skills
Pm Metrics 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 |
|---|---|---|---|---|---|---|
| Pm Metrics this skillserejaris/personal-corp-os | 229 | — | ~3k | Automated safety check: Pass | MIT | |
| Data Analysis Standardmohitagw15856/pm-claude-skills | 1.4k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Analytics Strategyrampstackco/claude-skills | 945 | — | ~2.4k | Automated safety check: Pass | MIT | |
| Business Metrics Calculatornimrodfisher/data-analytics-skills | 470 | — | ~668 | Automated safety check: Pass | MIT | |
| Product Metrics Dashboard Designphuryn/pm-skills | 27k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Lens Metricsjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~2.8k | Automated safety check: Notes | MIT |
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Questions about Pm Metrics
What does Pm Metrics do?
Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям. Pm Metrics is an agent skill from serejaris/personal-corp-os. Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям.
When should I use Pm Metrics?
Pm Metrics fits situations like: Анализ удержания; retention analysis; funnel analysis.
How do I install Pm Metrics in Claude Code?
Run `npx skills add serejaris/personal-corp-os --skill pm-metrics -a claude-code`. Or copy the skill folder (skills/pm-metrics in serejaris/personal-corp-os) into .claude/skills/pm-metrics in your project. Claude Code loads it when a task matches its description.
How do I install Pm Metrics in Codex?
Run `npx skills add serejaris/personal-corp-os --skill pm-metrics -a codex`. Or copy the skill folder (skills/pm-metrics in serejaris/personal-corp-os) into .agents/skills/pm-metrics in your project. Codex loads it when a task matches its description.
Can I use Pm Metrics 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 pm-metrics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pm-metrics, .gemini/skills/pm-metrics, .github/skills/pm-metrics and .opencode/skills/pm-metrics in your project.
What does Pm Metrics need to run?
SKILL.md names no scripts, command-line tools or credentials: Pm Metrics is instructions for the agent only.
Does Pm Metrics 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 Pm Metrics 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 Pm Metrics use?
Pm Metrics 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 Pm Metrics use?
About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Pm Metrics?
Skills that share tags, products or a category with Pm Metrics: Data Analysis Standard (mohitagw15856/pm-claude-skills, 1.4k stars), Analytics Strategy (rampstackco/claude-skills, 945 stars), Business Metrics Calculator (nimrodfisher/data-analytics-skills, 470 stars) and Product Metrics Dashboard Design (phuryn/pm-skills, 27k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pm Metrics?
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.