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Get Started Free →Pull Stripe + Mixpanel + GA4 once a week, compute MRR / churn / ARPU / activation, send a digest to Slack with anomalies flagged.
.claude/skills/mergisi-weekly-mrr-digest/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -26% | 0% |
A weekly metrics report for solo founders and small SaaS teams who don't have a data analyst yet. Pulls Stripe, Mixpanel (or Amplitude / PostHog), and GA4, computes the core SaaS numbers, runs anomaly detection on each one, and sends a plain-English digest to Slack every Monday at 9am.
Every Monday at 9am (or whenever you schedule it):
Scheduled (recommended): use Hermes' built-in cron.
/cron weekly "every Monday at 9am, run /weekly-mrr-digest and post to #standups"Manual run:
/weekly-mrr-digestOr with custom date range:
/weekly-mrr-digest --since 2026-04-01 --until 2026-04-30yamldata_sources: stripe: api_key_env: STRIPE_SECRET_KEY include_test_mode: false mixpanel: project_id: "12345" api_secret_env: MIXPANEL_API_SECRET ga4: property_id: "987654321" service_account_json_env: GA4_SA_JSON slack: channel: "#metrics" bot_token_env: SLACK_BOT_TOKEN metrics: activation_event: "first_query_run" # what counts as "activated" trial_period_days: 7 baseline_window_days: 30 anomaly_z_score: 2.0 schedule: cron: "0 9 * * 1" # Monday 9am timezone: "America/New_York"
This is mostly structured queries with narrative summarization on top:
📊 Weekly MRR Digest — Week of 2026-04-29
TL;DR: MRR up 1.8% WoW. Anomaly: signup spike Tuesday (+47% vs baseline)
likely from a HN front-page mention. Investigate: activation dropped 14
percentage points after the Apr 24 deploy.
MRR: $3,847 (+1.8% WoW, +12% MoM)
• Net new: +$46 (3 new $216, 2 churned $170)
• Active subs: 247
Conversion (signup → paid, trailing 14d): 4.2%
• Industry SaaS median at your ARR: 2-5%, you're median+
Churn (last 30d monthly): 5.4%
• Industry SaaS 60-75th percentile, top quartile <4%
🚨 Anomaly: Activation rate dropped from 38% → 24% over the past 5 days.
• Likely cause: deploy on Apr 24 changed the onboarding flow.
• Recommend: rollback or fix before more cohort flows through.
Top action item: investigate activation drop before more users hit it.This skill is the analyst piece of a 3-agent data crew. Pair with a dashboard-builder skill (coming soon) and a market-context skill (coming soon) for full coverage.
For the full crew: see crewclaw.com/use-cases/data-analytics-team.
mixpanel.use_jql: true (faster) or run from a VPS in the same region.activation_event after running for 2+ weeks, historical baselines will look wrong. Reset the baseline window after any definition change.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 17,682 | 12,944 | -27% | 1 | 1 | 0% | 3,212 | 3,832 | +19% | 0 | 0 | — |
case-02 | fail→fail | 26,683 | 10,891 | -59% | 1 | 1 | 0% | 2,405 | 3,382 | +41% | 0 | 0 | — |
case-03 | fail→pass | 14,261 | 14,760 | +3% | 1 | 1 | 0% | 2,473 | 4,007 | +62% | 0 | 0 | — |
case-04 | fail→fail | 14,327 | 12,406 | -13% | 1 | 1 | 0% | 2,913 | 3,963 | +36% | 0 | 0 | — |
case-05 | fail→fail | 35,111 | 15,222 | -57% | 1 | 1 | 0% | 5,217 | 4,392 | -16% | 0 | 0 | — |
case-06 | fail→fail | 26,900 | 27,230 | +1% | 1 | 1 | 0% | 6,170 | 7,634 | +24% | 0 | 0 | — |
case-07 | fail→pass | 8,028 | 3,941 | -51% | 1 | 1 | 0% | 1,676 | 2,277 | +36% | 0 | 0 | — |
case-08 | pass→pass | 13,693 | 11,653 | -15% | 1 | 1 | 0% | 2,310 | 3,308 | +43% | 0 | 0 | — |
case-09 | fail→pass | 5,849 | 1,555 | -73% | 1 | 1 | 0% | 1,011 | 1,714 | +70% | 0 | 0 | — |
case-10 | fail→pass | 5,799 | 1,752 | -70% | 1 | 1 | 0% | 1,129 | 1,785 | +58% | 0 | 0 | — |
case-11 | fail→pass | 14,637 | 2,694 | -82% | 1 | 1 | 0% | 2,574 | 1,913 | -26% | 0 | 0 | — |
case-12 | pass→pass | 9,271 | 3,562 | -62% | 1 | 1 | 0% | 1,604 | 2,068 | +29% | 0 | 0 | — |
case-13 | pass→pass | 11,406 | 9,458 | -17% | 1 | 1 | 0% | 1,900 | 3,122 | +64% | 0 | 0 | — |
case-14 | fail→pass | 12,654 | 6,092 | -52% | 1 | 1 | 0% | 2,197 | 2,616 | +19% | 0 | 0 | — |
case-15 | pass→pass | 7,686 | 2,297 | -70% | 1 | 1 | 0% | 1,166 | 1,836 | +57% | 0 | 0 | — |
case-16 | pass→pass | 5,326 | 41,412 | +678% | 1 | 1 | 0% | 914 | 1,883 | +106% | 0 | 0 | — |
case-17 | pass→pass | 9,277 | 11,192 | +21% | 1 | 1 | 0% | 1,888 | 3,601 | +91% | 0 | 0 | — |
case-18 | pass→pass | 5,287 | 1,950 | -63% | 1 | 1 | 0% | 1,022 | 1,790 | +75% | 0 | 0 | — |
case-19 | fail→pass | 6,312 | 1,819 | -71% | 1 | 1 | 0% | 1,087 | 1,742 | +60% | 0 | 0 | — |
case-20 | fail→pass | 12,835 | 7,519 | -41% | 1 | 1 | 0% | 2,014 | 2,051 | +2% | 0 | 0 | — |
case-21 | pass→pass | 3,819 | 1,894 | -50% | 1 | 1 | 0% | 623 | 1,740 | +179% | 0 | 0 | — |
case-22 | pass→pass | 10,210 | 1,809 | -82% | 1 | 1 | 0% | 1,659 | 1,682 | +1% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/3/2026 | +45% |
Other measured skills in the registry, with their headline benchmark lift.