firefrost-gaming/claude-skills-reference

Files

Reza Rezvani 2f57ef8948 feat(agenthub): add AgentHub plugin with cross-domain examples, SEO optimization, and docs site fixes

- AgentHub: 13 files updated with non-engineering examples (content drafts,
  research, strategy) — engineering stays primary, cross-domain secondary
- AgentHub: 7 slash commands, 5 Python scripts, 3 references, 1 agent,
  dry_run.py validation (57 checks)
- Marketplace: agenthub entry added with cross-domain keywords, engineering
  POWERFUL updated (25→30), product (12→13), counts synced across all configs
- SEO: generate-docs.py now produces keyword-rich <title> tags and meta
  descriptions using SKILL.md frontmatter — "Claude Code Skills" in site_name
  propagates to all 276 HTML pages
- SEO: per-domain title suffixes (Agent Skill for Codex & OpenClaw, etc.),
  slug-as-title cleanup, domain label stripping from titles
- Broken links: 141→0 warnings — new rewrite_skill_internal_links() converts
  references/, scripts/, assets/ links to GitHub source URLs; skills/index.md
  phantom slugs fixed (6 marketing, 7 RA/QM)
- Counts synced: 204 skills, 266 tools, 382 refs, 16 agents, 17 commands,
  21 plugins — consistent across CLAUDE.md, README.md, docs/index.md,
  marketplace.json, getting-started.md, mkdocs.yml
- Platform sync: Codex 163 skills, Gemini 246 items, OpenClaw compatible

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-17 12:10:46 +01:00

4.1 KiB

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title, description

title	description
Product Analytics — Agent Skill for Product Teams	Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw.

Product Analytics

:material-lightbulb-outline: Product :material-identifier: `product-analytics` :material-github: Source

Install: claude /plugin install product-skills

Define, track, and interpret product metrics across discovery, growth, and mature product stages.

When To Use

Use this skill for:

Metric framework selection (AARRR, North Star, HEART)
KPI definition by product stage (pre-PMF, growth, mature)
Dashboard design and metric hierarchy
Cohort and retention analysis
Feature adoption and funnel interpretation

Workflow

Select metric framework

AARRR for growth loops and funnel visibility
North Star for cross-functional strategic alignment
HEART for UX quality and user experience measurement

Define stage-appropriate KPIs

Pre-PMF: activation, early retention, qualitative success
Growth: acquisition efficiency, expansion, conversion velocity
Mature: retention depth, revenue quality, operational efficiency

Design dashboard layers

Executive layer: 5-7 directional metrics
Product health layer: acquisition, activation, retention, engagement
Feature layer: adoption, depth, repeat usage, outcome correlation

Run cohort + retention analysis

Segment by signup cohort or feature exposure cohort
Compare retention curves, not single-point snapshots
Identify inflection points around onboarding and first value moment

Interpret and act

Connect metric movement to product changes and release timeline
Distinguish signal from noise using period-over-period context
Propose one clear product action per major metric risk/opportunity

KPI Guidance By Stage

Pre-PMF

Activation rate
Week-1 retention
Time-to-first-value
Problem-solution fit interview score

Growth

Funnel conversion by stage
Monthly retained users
Feature adoption among new cohorts
Expansion / upsell proxy metrics

Mature

Net revenue retention aligned product metrics
Power-user share and depth of use
Churn risk indicators by segment
Reliability and support-deflection product metrics

Dashboard Design Principles

Show trends, not isolated point estimates.
Keep one owner per KPI.
Pair each KPI with target, threshold, and decision rule.
Use cohort and segment filters by default.
Prefer comparable time windows (weekly vs weekly, monthly vs monthly).

See:

references/metrics-frameworks.md
references/dashboard-templates.md

Cohort Analysis Method

Define cohort anchor event (signup, activation, first purchase).
Define retained behavior (active day, key action, repeat session).
Build retention matrix by cohort week/month and age period.
Compare curve shape across cohorts.
Flag early drop points and investigate journey friction.

Retention Curve Interpretation

Sharp early drop, low plateau: onboarding mismatch or weak initial value.
Moderate drop, stable plateau: healthy core audience with predictable churn.
Flattening at low level: product used occasionally, revisit value metric.
Improving newer cohorts: onboarding or positioning improvements are working.

Tooling

`scripts/metrics_calculator.py`

CLI utility for:

Retention rate calculations by cohort age
Cohort table generation
Basic funnel conversion analysis

Examples:

python3 scripts/metrics_calculator.py retention events.csv
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay

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