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mergisimergisi/awesome-hermes-agent/skills/weekly-mrr-digest/SKILL.md

weekly-mrr-digest

Skill2 starsChanged 7 months agoMITView on GitHub

Pull Stripe + Mixpanel + GA4 once a week, compute MRR / churn / ARPU / activation, send a digest to Slack with anomalies flagged.

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The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Every Monday at 9am (or whenever you schedule it):

    line 16
  2. Distinctive:

    Time-zone confusion: all metrics computed in the timezone you configure.

    line 128
  3. Very specific:

    Cohort definition drift: if you change activation_event after running for 2+ weeks, historical baselines will look wrong.

    line 129

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Weekly MRR Digest

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.

What this skill does

Every Monday at 9am (or whenever you schedule it):

  1. Pull from data sources — Stripe (subscriptions, invoices, refunds), Mixpanel/Amplitude/PostHog (events, cohorts), GA4 (traffic, attribution).
  2. Compute the core SaaS metrics:
    • MRR (current week, +/- vs last week, MoM)
    • Net new MRR (new subs minus churned)
    • Churn rate (last 30d, last 7d)
    • ARPU
    • Signup → paid conversion (last 14d, last 90d trailing)
    • Activation rate (you define what activation means in the config)
    • Top traffic sources by signups, by paid conversion
  3. Run anomaly detection — z-score against trailing 30-day baseline. Flag any metric 2σ+ off in either direction.
  4. Add narrative context — "MRR dipped because 3 annual subs churned this week. Two were on the deprecated $19 tier. The third left a feedback note (link)."
  5. Surface 1-3 action items — based on what's anomalous, suggest the most leveraged thing to investigate this week.
  6. Send to Slack — to the channel you specify, formatted as Slack mrkdwn with metric blocks and a TL;DR at the top.

What this skill does NOT do

  • It does NOT replace a real data analyst — for ad-hoc questions ("why did churn spike in Brazil?"), you still need someone who can write SQL.
  • It does NOT build dashboards. For real-time visibility, use Mixpanel/Amplitude's native dashboards or pair with a separate dashboard skill.
  • It does NOT predict future MRR. Forecasting is a separate problem; this skill is descriptive analytics, not predictive.
  • It does NOT replace Stripe Sigma for deep financial analysis.

How to invoke

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-digest

Or with custom date range:

/weekly-mrr-digest --since 2026-04-01 --until 2026-04-30

Configuration

data_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"

Models

This is mostly structured queries with narrative summarization on top:

  • Recommended: Claude Sonnet 4.6 for the narrative + anomaly explanations. The numbers come from APIs; the model writes the digest text.
  • For raw query work: Haiku 4.5 is fine and ~10x cheaper.
  • Total weekly cost: ~$0.40-1.20 in API spend for a typical solo SaaS at $1K-50K MRR (mostly the narrative pass).

Sample digest output

📊 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.

Pairing

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.

Failure modes to watch

  • API rate limits: Mixpanel raw export rate-limits at 60 req/hour. The skill batches sensibly, but for very high event volumes, set mixpanel.use_jql: true (faster) or run from a VPS in the same region.
  • Time-zone confusion: all metrics computed in the timezone you configure. If your Stripe data is UTC and your Slack channel expects local time, the digest will note the mismatch but won't auto-convert.
  • Cohort definition drift: if you change activation_event after running for 2+ weeks, historical baselines will look wrong. Reset the baseline window after any definition change.

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