| name | cold-email-weekly-rhythm |
| description | Operational playbook for running cold email continuously. Prescribes what to do on Monday / Wednesday / Friday / biweekly / monthly / quarterly. Use this as your ongoing cadence once your first campaign is live. What separates hobbyist from top 1% operators isn't tooling — it's consistency. This skill is the schedule. |
Cold Email Weekly Rhythm
Having 29 cold email skills doesn't help if you don't run them on a schedule. This is that schedule.
This is a pure playbook. No scripts, no automatic reminders. You put the rhythm on your own calendar (Step 1 below) and run the prescribed skill at the prescribed time. That's the entire system.
Step 1 (required before using this skill): Put the rhythm on your calendar
Open Google Calendar / Outlook / Apple Reminders / whatever you actually look at every day. Create these as recurring events. Copy the titles and cadences exactly.
| Event title | Cadence |
|---|
| Cold email: Monday deliverability audit | Every Monday, 9:00 am |
| Cold email: Wednesday positive-reply sweep | Every Wednesday, 10:00 am |
| Cold email: Friday campaign retrospectives | Every Friday, 3:00 pm |
| Cold email: Inbox rotation | Every other Monday, 11:00 am |
| Cold email: Monthly spam placement test | 1st of each month, 10:00 am |
| Cold email: Quarterly experiment review | First Monday of each quarter, 1:00 pm |
Do not skip Step 1. The difference between hobbyist and top-1% cold email operators is that the top-1% operators actually run these tasks on their declared cadence, week after week. Your calendar is the accountability system. This skill doesn't have a built-in reminder — intentionally — because if it did and it broke, your ops would silently fail.
Monday — Deliverability audit (15 min)
Run:
/email-deliverability-audit --days=7
(If using the scoped version: audit-performance.ts --campaign-ids=<active campaign ids> --days=7)
Review:
- Fleet reply rate over last 7 days — must be ≥1% (the 1% rule)
- Flagged campaigns (
flag_low_reply = TRUE)
- Flagged inboxes (
flag_high_bounce = TRUE)
Action:
- If any campaign failed the 1% rule: run
/deliverability-incident-response → triage decision tree
- If bounce rate spiked above 2%: pause the offending campaign immediately, then triage
- If everything clean: log the check in a weekly journal, close the tab
Wednesday — Positive-reply sweep (30-60 min depending on volume)
Run:
/positive-reply-scoring
Review:
- Any
positive_interested or positive_soft replies — these are leads wanting to engage
- Any
positive_referral replies — these are people saying "talk to Jane instead"
- Any
negative_hostile replies — red flag, investigate
Action:
- Respond to every
positive_interested reply within 30 seconds of seeing it. Do not batch these — a reply feeling like it took minutes to return converts 3× better than one that took hours.
- For referrals: reach out to the referred person within 24h, mention the referrer by name.
- For hostile: apologize, remove from all lists, investigate why they were flagged for hostility (often signals bad targeting).
Time management: if you have >50 positive replies per week, you're at the scale where you should hand off to a dedicated closer/AE. Do that handoff Wednesday morning so they can work replies by Wednesday afternoon.
Friday — Campaign retrospectives (20 min per campaign)
Identify campaigns hitting their 21-day mark this week. (21 days is the minimum for reply-rate signal to stabilize.)
For each one:
-
Run /positive-reply-scoring --campaign-id=<id>
-
Compare to previous campaigns' positive reply rate baselines
-
Decide:
- Winner (positive reply rate ≥2×baseline): keep running, consider scaling (clone to more inboxes)
- Middling (near baseline): iterate on copy or list — run
/experiment-design Monday to plan the next variant
- Loser (<50% of baseline): kill it. Document why in the experiment log.
-
Log the result in profiles/<slug>/experiments/<YYYY-MM-DD>-<campaign>.json:
positive_reply_rate, reply_rate, bounce_rate- Your decision (keep / iterate / kill) + reasoning
- Hypothesis for next iteration (if iterating)
Critical: do not skip the log. Over a quarter, these logs become the input for /experiment-design quarterly reviews. Without the history, you can't learn across campaigns.
Every other Monday — Inbox rotation (30 min)
Run:
npx tsx scripts/list-health.ts --all --out=health-$(date +%Y-%m-%d).csv
Review the CSV:
- Any inboxes with reputation "bad"?
- Any inboxes with
is_warmup_blocked: true?
- Any inboxes with <5 sends/day despite being in active campaigns?
Action:
npx tsx scripts/tag-inboxes.ts --ids=X,Y,Z --add-tag=retired --remove-tag=active
npx tsx scripts/set-warmup.ts --mode=disable --ids=X,Y,Z
npx tsx scripts/tag-inboxes.ts --ids=A,B,C --add-tag=active --remove-tag=insurance
npx tsx scripts/set-warmup.ts --mode=disable --ids=A,B,C
If insurance pool is getting thin (<5 inboxes available to rotate in): kick off a new domain purchase cycle via /zapmail-domain-setup-public. It takes ~2 weeks from purchase to sendable, so you need to start early.
Monthly (1st of the month) — Spam placement test (25 min active, test runs ~20 min)
Run: the Smart Delivery spam placement test from /email-deliverability-audit:
npx tsx scripts/run-spam-test.ts --campaign-id=<highest volume active campaign> --senders=100
(Will use G Suite + Office365 provider pools. 100 senders is a sweet spot — large enough for statistical signal, small enough to avoid API stalls.)
Review:
- Overall inbox placement % — target: ≥85%
- Spam filter triggers — which filters fired, which senders affected
Action:
- ≥90% inbox placement: great. Keep doing what you're doing.
- 80-90%: yellow. Look at spam-filter-details. Start fixing the highest-frequency trigger next week.
- <80%: red. Pause the campaign, run
/deliverability-incident-response, fix before sending more.
Quarterly (first Monday of each quarter) — Experiment review (90 min)
Read all experiments/*.json from the last quarter. Identify patterns:
- Which campaigns had the highest positive reply rate?
- Which list sources produced the best leads?
- Which copy angles resonated (Observation → Implication? Pain? Social proof?)
- Which ICP archetypes converted best?
Output: a 1-page Q retrospective saved to profiles/<slug>/retrospectives/<YYYY>-Q<N>.md with:
- Top 3 campaigns + what made them work
- Bottom 3 campaigns + what to avoid
- 3-5 hypotheses for next quarter's experiments
- Adjust ICP in
client-profile.yaml if the data suggests a different segment performs better
Use the retrospective as input to design next quarter's experiments via /experiment-design.
What to skip
You do NOT need to:
- Check Smartlead every day (Wednesday sweep catches everything important)
- Obsess over daily reply-rate fluctuations (wait for 7-day averages)
- Read every positive reply in real time — set up notifications if you want immediacy, but the Wednesday sweep is the system
Daily pokes at your cold email stack are a procrastination pattern, not a performance pattern.
What to do next
This skill IS the loop. Your next action is the next calendar event on your list.
Or if you haven't run a campaign yet: skip this skill entirely. Come back after your first campaign hits the 7-day mark.
Related skills
/email-deliverability-audit — the Monday and Monthly tasks/positive-reply-scoring — the Wednesday and Friday tasks/smartlead-inbox-manager — the biweekly inbox rotation/deliverability-incident-response — when the Monday audit flags something/experiment-design — the quarterly retrospective feeds into this/zapmail-domain-setup-public — when insurance pool runs low
