Modeling Activation Metrics
PostHog/posthog
Build reusable activation models — an activation-rate metric and a per-user/per-account activated flag — on either PostHog data-warehouse views (HogQL) or an external dbt project.
Define a metric in a semantic layer so it means one thing everywhere.
Install Metric Semantic Layer
$ npx skills add mohitagw15856/pm-claude-skills --skill metric-semantic-layer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mohitagw15856/pm-claude-skills metric-semantic-layer --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/metric-semantic-layer .claude/skills/metric-semantic-layer && 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 "metric-semantic-layer" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/metric-semantic-layer into .claude/skills/metric-semantic-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metric-semantic-layer", 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/metric-semantic-layerType 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 metric-semantic-layer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mohitagw15856/pm-claude-skills metric-semantic-layer --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/metric-semantic-layer .agents/skills/metric-semantic-layer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "metric-semantic-layer" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/metric-semantic-layer into .agents/skills/metric-semantic-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metric-semantic-layer", 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 metric-semantic-layer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mohitagw15856/pm-claude-skills metric-semantic-layer --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/metric-semantic-layer .cursor/skills/metric-semantic-layer && 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 "metric-semantic-layer" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/metric-semantic-layer into .cursor/skills/metric-semantic-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metric-semantic-layer", 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/metric-semantic-layer--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 metric-semantic-layer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mohitagw15856/pm-claude-skills metric-semantic-layer --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/metric-semantic-layer .gemini/skills/metric-semantic-layer && 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 "metric-semantic-layer" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/metric-semantic-layer into .gemini/skills/metric-semantic-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metric-semantic-layer", 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 metric-semantic-layerInstalls 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 metric-semantic-layer -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/metric-semantic-layer .github/skills/metric-semantic-layer && 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 "metric-semantic-layer" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/metric-semantic-layer into .github/skills/metric-semantic-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metric-semantic-layer", 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 metric-semantic-layer -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 metric-semantic-layer --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/metric-semantic-layer .opencode/skills/metric-semantic-layer && 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 "metric-semantic-layer" agent skill from https://github.com/mohitagw15856/pm-claude-skills/tree/main/skills/metric-semantic-layer into .opencode/skills/metric-semantic-layer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "metric-semantic-layer", 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
metric-semantic-layer- GitHub stars
- 1.4k
- Token cost
- ~956 tokens
- SKILL.md length
- 457 words
- Files
- 1
- Skills in repo
- 1,348
- Repo updated
- First seen
- Licence
- MIT
At a glance
Define a metric in a semantic layer so it means one thing everywhere.
- Asked to define a metric
- SKILL.md covers Required Inputs, Output Format, Quality Checks and Anti-Patterns, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Build a semantic layer / metrics layer entry
What it does
Metric Semantic Layer is an agent skill from mohitagw15856/pm-claude-skills. Define a metric in a semantic layer so it means one thing everywhere. Use when asked to define a metric, build a semantic layer / metrics layer entry, stop 'revenue means three things' problems, or write a metric definition for dbt MetricFlow / Cube / LookML. Produces a metric definition — exact formula, the base measure & aggregation, dimensions, filters, grain, edge cases, and a tool-ready spec.
Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data pipelines and ETL and Product metrics. It works with dbt. 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 define a metric
- Build a semantic layer / metrics layer entry
- Stop revenue means three things problems
- Write a metric definition for dbt MetricFlow / Cube / LookML
Example prompts
- “revenue means three things”
- “/metric-semantic-layer”
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
Metric Semantic Layer loads about 956 tokens when it runs. Until then it costs about 106 tokens; SKILL.md has 457 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). 457 words, ~956 tokens.
Download SKILL.mdSave it as
.claude/skills/metric-semantic-layer/SKILL.md (or your agent's skills folder).- name
- metric-semantic-layer
- description
- Define a metric in a semantic layer so it means one thing everywhere. Use when asked to define a metric, build a semantic layer / metrics layer entry, stop 'revenue means three things' problems, or write a metric definition for dbt MetricFlow / Cube / LookML. Produces a metric definition — exact formula, the base measure & aggregation, dimensions, filters, grain, edge cases, and a tool-ready spec.
Metric Semantic Layer Skill
"Active users" means three different things in three dashboards — that's the problem a semantic layer solves: define each metric once, precisely, and every tool reads the same definition. This skill writes that definition — the exact formula, base measure, allowed dimensions, default filters, and the edge cases that usually cause drift — in a tool-ready form (dbt MetricFlow / Cube / LookML).
Required Inputs
Ask for these only if they aren't already provided:
- The metric — its name and the business question it answers.
- The base data — the model/table and the column(s) it's computed from.
- The aggregation — sum, count, count distinct, average, ratio.
- Dimensions & filters — how it can be sliced, and any default filters (exclude test accounts, internal users, refunds).
- Tool — dbt MetricFlow, Cube, LookML, or tool-agnostic.
Output Format
Metric: [metric_name]
1. Definition (plain English) — one sentence a non-analyst understands, and the precise version ("count of distinct user_ids with ≥1 qualifying event in the period, excluding internal/test accounts").
2. Formula — the exact calculation: base measure · aggregation · numerator/denominator (for ratios).
3. Grain & time — the time grain it's reported at, the date column it's anchored to, and how partial periods are handled.
4. Dimensions — the dimensions it can be sliced by (and any it must not be — non-additive metrics break when summed across the wrong dimension).
5. Default filters — what's always excluded (test/internal/refunds) so every consumer gets the same number.
6. Edge cases — null handling, late-arriving data, deduplication, currency/timezone, and additivity (can it be summed across days? across segments?). This section is where metric drift is prevented.
7. Tool-ready spec — the YAML/LookML for the chosen tool (MetricFlow metrics: / Cube measures: / LookML measure:), ready to commit.
Show full SKILL.md (185 more words)Show less
Quality Checks
- Has both a plain-English and an exact definition
- States the base measure, aggregation, and (for ratios) numerator/denominator
- Default filters are explicit, so every tool returns the same number
- Additivity is addressed (which dimensions it can/can't be summed across)
- Edge cases (nulls, dedup, timezone, late data) are handled
- A tool-ready spec is provided, not just prose
Anti-Patterns
- Do not leave the definition fuzzy — "active users" without the exact rule is how three dashboards disagree
- Do not omit default filters — if one tool counts test accounts and another doesn't, the metric is broken
- Do not ignore additivity — summing a non-additive metric (like a distinct count) across days gives a wrong number
- Do not define metrics in BI tools instead of the semantic layer — that's how definitions fork
- Do not skip timezone/null/dedup edge cases — they cause the subtle, hard-to-find discrepancies
Based On
Semantic-layer / metrics-layer practice (dbt MetricFlow, Cube, LookML) — single-source metric definitions with explicit grain, filters, and additivity.
Example Trigger Phrases
- "Define a metric."
- "Build a semantic layer / metrics layer entry."
- "Stop 'revenue means three things' problems."
- "Write a metric definition for dbt MetricFlow / Cube / LookML."
© 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
Just SKILL.md in skills/metric-semantic-layer of mohitagw15856/pm-claude-skills.
Open the folder on GitHubat commit 1cbf1f0
Compare with similar skills
Metric Semantic Layer 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 |
|---|---|---|---|---|---|---|
| Metric Semantic Layer this skillmohitagw15856/pm-claude-skills | 1.4k | — | ~956 | Automated safety check: Pass | MIT | |
| Modeling Activation MetricsPostHog/posthog | 40k | — | ~1.4k | Automated safety check: Pass | Custom licence | |
| Modeling Product Usage MetricsPostHog/posthog | 40k | — | ~1.3k | Automated safety check: Pass | Custom licence | |
| Modeling Warehouse FoundationsPostHog/posthog | 40k | — | ~2.1k | Automated safety check: Pass | Custom licence | |
| Dbt Databricks PR Readydatabricks/dbt-databricks | 380 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Mz Dbt ReleaseMaterializeInc/materialize | 6.4k | — | ~1.2k | Automated safety check: Pass | Custom licence |
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Questions about Metric Semantic Layer
What does Metric Semantic Layer do?
Define a metric in a semantic layer so it means one thing everywhere. Metric Semantic Layer is an agent skill from mohitagw15856/pm-claude-skills. Define a metric in a semantic layer so it means one thing everywhere.
When should I use Metric Semantic Layer?
Metric Semantic Layer fits situations like: asked to define a metric; build a semantic layer / metrics layer entry; stop revenue means three things problems; write a metric definition for dbt MetricFlow / Cube / LookML.
How do I install Metric Semantic Layer in Claude Code?
Run `npx skills add mohitagw15856/pm-claude-skills --skill metric-semantic-layer -a claude-code`. Or copy the skill folder (skills/metric-semantic-layer in mohitagw15856/pm-claude-skills) into .claude/skills/metric-semantic-layer in your project. Claude Code loads it when a task matches its description.
How do I install Metric Semantic Layer in Codex?
Run `npx skills add mohitagw15856/pm-claude-skills --skill metric-semantic-layer -a codex`. Or copy the skill folder (skills/metric-semantic-layer in mohitagw15856/pm-claude-skills) into .agents/skills/metric-semantic-layer in your project. Codex loads it when a task matches its description.
Can I use Metric Semantic Layer 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 metric-semantic-layer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/metric-semantic-layer, .gemini/skills/metric-semantic-layer, .github/skills/metric-semantic-layer and .opencode/skills/metric-semantic-layer in your project.
What does Metric Semantic Layer need to run?
SKILL.md names no scripts, command-line tools or credentials: Metric Semantic Layer is instructions for the agent only.
Does Metric Semantic Layer 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 Metric Semantic Layer 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 Metric Semantic Layer use?
Metric Semantic Layer 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 Metric Semantic Layer use?
About 956 tokens (SKILL.md is roughly 3.8k 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 Metric Semantic Layer?
Skills that share tags, products or a category with Metric Semantic Layer: Modeling Activation Metrics (PostHog/posthog, 40k stars), Modeling Product Usage Metrics (PostHog/posthog, 40k stars), Modeling Warehouse Foundations (PostHog/posthog, 40k stars) and Dbt Databricks PR Ready (databricks/dbt-databricks, 380 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Metric Semantic Layer?
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.