Child skill for converting cold outreach campaign results, replies, objections, and buyer language into a staged diagnosis, a gated stop/iterate/recycle/scale decision, and a learning memo — with sample-size discipline before any rate verdict.
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SKILL.md
Instructions source · Aperçu en lecture seule
name
Cold Email Learning Review
description
Child skill for converting cold outreach campaign results, replies, objections, and buyer language into a staged diagnosis, a gated stop/iterate/recycle/scale decision, and a learning memo — with sample-size discipline before any rate verdict.
skill_type
strategy
altitude
domain
activation
progressive
parent_id
cold_email_engagement_first_outreach
depth
1
preserve_markdown
true
dependencies
[{"id":"cold_email_deliverability_readiness","owns":"Provider/deliverability rules and compliance/placement remediation (its Knowledge block holds the provider requirement matrix — not duplicated here)."},{"id":"cold_email_reply_os","owns":"Reply handling and the one follow-through question per reply; logs and feeds the buyer-language file."},{"id":"cold_email_icp_signal_design","owns":"ICP + signal design and the buying-committee map that decision-dynamics evidence feeds."}]
Use this child skill when a campaign or test has results and the user needs to learn what to do next. The north star is qualified conversations started per unit of market trust consumed — never replies, meetings booked, or emails sent.
This is a strategy skill at domain altitude — the dominant verb is decide (diagnose the one failing layer → run the gate tree → write the memo). It carries an ordered Procedure as a secondary shape and routes narrow sub-problems to sibling skills (see Routing).
Activation
Load this skill when:
A campaign has send, open, reply, positive-reply, meeting, bounce, complaint, or objection data
The user asks whether to scale, stop, recycle, or change the offer
Replies contain useful buyer language
The team needs a learning memo after a test
Judgment
The decision spine — the diagnostic rubric, the sample-size and validity rules, and the stop/iterate/recycle/scale gate tree that the Procedure reasons with. Do not read any rate as a verdict before passing the sample gate below; the vendor benchmark bands it leans on live in Knowledge.
Metric diagnostic table (which metric diagnoses which failure)
Sources: Connor Murray's three-rate diagnostic (practitioner method: track three inputs, not one), extended with Austin Schneider / Instantly deliverability context and 2026 provider-enforcement changes.
Metric
What it diagnoses
If low, suspect (in order)
Delivered rate / bounce rate
List hygiene + domain health
List source quality, verification skipped, domain reputation
Open rate
Subject + preview + sender reputation + placement
2026 note: compliance/placement failure first (auth, complaint rate), then subject/preview, then send timing
Reply rate (all)
Body craft + CTA friction
Passive language, multi-CTA, ask too large, no artifact offer
Positive reply rate
Offer + segment fit
Offer not useful pre-meeting, wrong persona, stale signal
Meeting-booked rate (from positive replies)
Relevance paragraph + reply handling speed
"Why I'm relevant" misses persona priorities; slow reply routing
Meeting held rate
Qualification + expectation set in thread
Front-end offer attracted bad-fit; no pre-call filter
Complaint / opt-out / angry-reply rate
Trust consumed
Segment too broad, cadence too long, fake personalization
Hard rule (Connor Murray): never optimize a single composite "reply rate." Replies include "no," "stop," and "wrong person." Diagnose stage by stage.
Hard rule (Mailshake, State of Cold Email 2025): open rate is "a directional metric, not a definitive one... Use reply rate as your true north, not open rate." Opens are additionally unreliable due to privacy proxies — treat open deltas >10pts as signal, smaller moves as noise.
2026 placement context: Google began server-level rejection (not just spam-foldering) of non-compliant bulk mail in Nov 2025, and Microsoft rejects unauthenticated 5k+/day senders (550 5.7.515). Low opens in 2026 are more likely a compliance/placement failure than a copy failure. Route to → cold_email_deliverability_readiness (its Knowledge block holds the provider rules — do not duplicate them here).
Sample-size and test-validity rules
Validity rules come first because the gate tree is meaningless on an invalid read.
Sources: Evan Miller, "How Not To Run An A/B Test" (https://www.evanmiller.org/how-not-to-run-an-ab-test.html); Kohavi et al., Trustworthy Online Controlled Experiments ch. 1 (official excerpt) — usable principles: define an Overall Evaluation Criterion, test trustworthiness before interpreting, implement guardrail metrics, beware Twyman's law / carryover effects.
No peeking (Evan Miller). Checking repeatedly and stopping at the first significant result inflates a nominal 5% false-positive rate to as much as 26.1%. Decide sample size in advance; read once at the end.
Fixed-sample formula (Evan Miller): n ≈ 16·(σ²/δ²) per arm, with σ² = p(1−p) for rates. Practical cold-email table (baseline 3% positive-reply rate):
detect 3% → 6% (doubling): ≈ n=560 per arm.
detect 3% → 4.5%: ≈ n=2,100 per arm.
detect 3% → 3.5%: ≈ n=18,000 per arm — i.e., micro-segment campaigns can only detect large effects. If the campaign is <100 sends, treat results as qualitative evidence (buyer language, objection mix), never as a rate comparison.
One variable per test. Holding rules: same CTA across variants, one persona × one signal, same list source per variant, same sending infrastructure per variant — otherwise placement differences masquerade as copy differences.
OEC for this suite (Kohavi's OEC concept applied to the suite's north star): qualified conversations started per unit of market trust consumed — not replies, not meetings booked. The trust-consumed composite is internal methodology, not sourced fact; flag it as such.
Stop / Iterate / Recycle / Scale decision tree
Assembled from: Schneider non-responder recycling, Mailshake bands, Aaron Shepherd volume-as-data and back-end qualification, Close "no response is a response," Predictable Revenue nurture asymmetry. Gate values are defaults to tune per market — internal calibration, not industry standards.
0. TRUST GATE (check first, overrides everything):
complaints >0.3% absolute or >0.1% sustained, bounce >5%,
or spam-foldering evidence → STOP SENDING. Fix sender/list before
reading any copy/offer metric. (Google/Yahoo 0.3% ceiling, 0.1% target;
Mailshake bounce threshold.)
1. SAMPLE GATE: fewer than ~200 delivered per variant, or mixed personas
→ NO RATE VERDICT. Extract buyer language + objections only.
Next action: keep sending or widen the same segment.
2. POSITIVE-REPLY GATE (offer/segment signal):
positive reply ≥3% → candidate to SCALE (step 4).
positive reply 1–3% → ITERATE: change ONE of offer artifact / persona
priorities paragraph / signal freshness. Re-test same segment.
positive reply <1% AND opens ≥30% → offer or segment is wrong:
RECYCLE non-responders into a new campaign with a different
opener + different artifact offer (Schneider replacement for touches 3–7).
positive reply <1% AND opens <20% → packaging or placement problem:
route to deliverability child first, then subject/preview rework.
3. QUALITY GATE (before scaling):
meetings from positive replies <40%, or bad-fit replies >50% of replies
→ fix reply routing / qualification before scale (Shepherd back-end filter).
4. SCALE: widen the same persona × signal, add adjacent micro-segments
(keep each <50–500 recipients per Schneider relevance cliff), add domains
not per-inbox volume. Re-run gates at each expansion.
5. DEAD: two recycle attempts on the same segment with <1% positive reply
and no usable buyer language → declare segment dead, document why,
move budget. Keep the buyer-language file.
Buyer-language extraction worksheet
Sources: Close Hail Mary dead-leads case, Fitzpatrick's Mom Test rules, Moesta's demand-side timeline.
Named alternative/incumbent ("going with [competitor] because…" — the Close Hail Mary case surfaced a previously unknown objection: easier hiring for the bigger-brand CRM).
Timeline state (Moesta): passive looking / active looking / deciding / onboarding / habit. "Not now" is timeline data → nurture, not failure.
Evidence grade (Mom Test): past behavior and current spend = strong; compliments and hypotheticals = weak. Tag compliments as weak unless paired with behavior.
Decision dynamics ("CEO made the call," "loved it but no power") → feeds the buying-committee map in → cold_email_icp_signal_design.
Hail Mary corollary: don't end the conversation at the first reply — the price objection in the Close case only surfaced on the second exchange. One follow-through question per reply minimum.
Procedure
Diagnose → gate → memo. Sequence and intent only; the rubric, thresholds, and gate logic each step leans on live in Judgment above.
Gather the raw counts, not just rates: sent, delivered, bounces, opens, replies, positive replies, meetings booked, meetings held, complaints, opt-outs, angry replies. Separate by variant and persona. Confirm the mode — recruiting, PR, and investor outreach use their own bands, never sales bands.
Run the diagnostic table in Judgment above, stage by stage: delivered/bounce → opens → replies → positive replies → meetings booked → meetings held → trust cost. Name exactly one most-likely failing layer (sender | segment | offer | body | proof | cadence | reply-handling). In 2026, treat low opens as a compliance/placement suspect before a copy suspect.
Check sample validity BEFORE concluding anything, using the sample-size and test-validity rules in Judgment above: below ~200 delivered per variant (or mixed personas) there is no rate verdict — downgrade to qualitative evidence only. If the test was monitored mid-flight and stopped on a "significant" result, flag the read as peeking-inflated and do not treat it as confirmed.
Extract buyer language and objection patterns with the worksheet: verbatim phrases, named incumbents, timeline state, evidence grade, decision dynamics. Negative replies count.
Run the gate tree in order — trust gate first (it overrides everything), then sample gate, positive-reply gate, quality gate — and decide: stop, iterate, recycle, scale, or dead. State which gate fired and why.
Propose the next test with exactly one variable changed, holding CTA, persona × signal, list source, and sending infrastructure constant. Pre-commit the sample size per arm from the fixed-sample table.
Write the learning memo using the template, including the sample verdict, trust cost, and nurture adds.
Routing
Ownership map for the narrow sub-problems this skill hands off. It diagnoses and decides; these siblings own what it routes to.
Nurture accounting (Predictable Revenue): assume only ~5% of created opportunities buy within 90 days; 95% of outbound value is the nurture pipeline; the channel takes 12–18 months to mature. A memo with zero meetings but real nurture adds and verbatim buyer language is a partial win, not a failure.
Policy
Do not optimize a single composite reply rate — diagnose stage by stage.
Do not scale from tiny or mixed samples, and issue no rate verdict below the sample gate (~200 delivered per variant); below ~100 sends, results are qualitative evidence only.
Do not peek: no early stops on mid-flight "significance" — repeated checking inflates a 5% false-positive rate to as much as 26.1% (Evan Miller). Pre-commit sample size; read once at the end.
Trust gate overrides all other reads: complaints >0.3% (or >0.1% sustained), bounce >5%, or spam-foldering evidence means stop sending and fix sender/list before interpreting copy or offer metrics.
Do not treat opens as buying intent, and treat open-rate deltas under 10pts as noise.
Do not ignore negative replies, opt-outs, or complaints — they are trust-cost data and buyer-language data.
Vendor benchmarks (Mailshake, Cognism, Lavender, Gem, Schneider) are directional only — triangulate before treating any band as governing, and never grade one mode against another mode's bands.
Never optimize for emails sent — the north star is qualified conversations started per unit of market trust consumed.
Do not declare a segment dead from a no-meetings result alone — count nurture adds first, and only after two failed recycle attempts with no usable buyer language.
Knowledge
Volatile, voluminous, and dated: the vendor benchmark bands and the internal trust-cost proxy. Every benchmark here is vendor data — the caveat next to each number is part of the number. Do not read any rate as a verdict before passing the sample gate in Judgment.
Benchmark bands (all directional-vendor; triangulate before treating as governing)
Methodology status governs how hard each number may be leaned on:
Methodology stated (usable as directional bands): Mailshake (508-respondent survey, self-reported), Cognism (internal-team dataset with counts, labeled "directional, not diagnostic").
Sample stated, selection bias not characterized (keep directional): Lavender.
Methodology NOT stated (named-practitioner patterns only, never governing thresholds): Gem's 32% recruiting reply rate, Schneider's touch-decay percentages, infrastructure economics claims.
Mailshake State of Cold Email 2025 — [PRIMARY] (508 self-reporting senders, survey early 2025; methodology stated; self-reported caveat applies)
Most common reply-rate band: 1–4%. Only ~16% of senders exceed 5% reply rate.
Most common open-rate band: 10–30%; few exceed 40%.
Bounce: nearly half of senders report 2–5%; 15% exceed 6%. "Bounce rates above 5% signal risky list hygiene and can cripple sender reputation." Monitor weekly.
69% of respondents said performance declined YoY (spam filtering + AI-content fatigue).
Top performers who personalize 1:1 report 2–3x higher reply rates; only 5% of senders personalize every email individually.
High-volume senders (1,000+/mo) were not more likely to generate more leads than low/mid volume senders.
Austin Schneider / Instantly — [practitioner] (named-practitioner pattern; methodology not stated — never a governing threshold)
Industry-average reply rates fell ~5% → ~1% after AI spam filters (Gmail 2024, Microsoft 2025); bulk-sender inbox placement fell 10–27% Q1-2024→Q1-2025.
Campaigns <50 recipients: 5.8% reply vs >1,000 recipients: 2.1% — the relevance ceiling.
Touch effects: follow-up #1 +49% replies; email 3 −20% (2026 vs +9% in 2023); email 4+ −55% and trains filters to flag the sender as bulk.
Cognism State of Outbound 2026 — [PRIMARY] (internal Cognism team data; methodology stated: 149,376 emails, 451,895 calls, 39,679 meetings; their own label: "directional, not diagnostic")
SDR cold-call answered rate 13.3% vs AE warm-call 14.4% — list quality and timing, not volume, drive connection.
Channel task mix in their top motion: calls 57%, LinkedIn 27%, email 15% — email-only learning loops under-read the market when the motion is multichannel.
~70% of SDR calls came from sequences; structure drives consistency, personalization drives meetings.
A-grade emails to operations: 5.4% reply (58% lift over baseline). Finance: 79% lift for A-grade, but only 6.1% of finance emails earn an A. Technical buyers baseline ≈ 5.2%.
Use: quality lift is real and measurable per persona — when a campaign underperforms, grade the email quality before declaring the segment dead.
Gem — [practitioner] (vendor, recruiting mode; methodology not stated)
Average 32% reply rate for a 4-stage recruiting sequence — recruiting benchmarks are an order of magnitude above sales benchmarks; never grade recruiting campaigns against sales bands, and never let recruiting bands leak into sales-mode guidance (mode quarantine).
Validation-phase gates for an outbound system: open rate >40%, meeting acceptance >80%, ~20% of opportunities "go deep," SDR task on-time execution >90%.
Nurture asymmetry: assume only ~5% of created opportunities buy within 90 days; 95% of outbound value is the nurture pipeline; the channel takes 12–18 months to mature.
Implication: a campaign is not "dead" because it produced no near-term meetings — count nurture-pipeline adds.
Trust-cost proxy set — [internal-default] (internal methodology — not sourced fact)
No external source measures "market trust consumed." The proxy set is: complaint rate, opt-out rate, negative-reply share, spam-foldering trend. The component thresholds come from deliverability and benchmark sources above; the composite is internal BuildOS methodology. Flag it as such whenever it appears in output.
Examples
Worked Example
Condensed from a full campaign review; the input is in evals.md Task 1. Match this order — trust gate, sample gate, stage diagnostics, ONE layer, gate decision, memo — and never skip the gates to get to the verdict.
Input: Sales mode, one variant, one segment (heads of data, Series B SaaS × first-data-engineer hire signal), offering a "pipeline health checklist." 400 sent · 0.9% bounce · ~38% opens · 2 replies · 1 positive · 0 meetings · 0 complaints · 1 opt-out. Replies: "Sure — send the checklist over… the pipeline alerting situation is rough" and "We already use Monte Carlo for this. Not interested." User's ask: "38% opens — subject's working, double the volume next month?"
Gate 1 — sample (before ANY rate verdict): 400 sent − ~4 bounces ≈ 396 delivered, single variant, single persona × signal ≥ ~200/variant floor → rate-readable, with the qualifier that n≈400 detects only large effects (per the fixed-sample table).
Stage diagnostics (never a composite reply rate — replies include "no"):
Stage
Value
Read
Delivered
~99.1%
List hygiene + domain healthy
Opens
~38%
Above Mailshake's common 10–30% band (508-sender self-reported survey — directional); privacy-proxy-inflated, directional, NOT buying intent
Replies (all)
2/396 ≈ 0.5%
Low — but diagnose the next stage before blaming body copy
Positive reply
1/396 ≈ 0.25%
The diagnostic stage: positive-reply rate reads offer + segment fit
Meetings
0
Not a death sentence — see nurture accounting
2026 placement check: opens ≥30% and bounce <1% → compliance/placement is ruled out; no deliverability routing.
Diagnosis (one layer):offer — checklist not useful enough pre-meeting to this persona (segment is the secondary suspect). Evidence: positive 0.25% (<1%) while opens ≥30% — the funnel dies exactly where the offer is judged.
Buyer language (worksheet, verbatim):"the pipeline alerting situation is rough" — pain language for the next opener; behavior (asked for the artifact) = strong-ish evidence. "We already use Monte Carlo" — named incumbent, current spend = strong evidence the segment buys the category; objection class: already-solved/competitor. Hail Mary corollary: one follow-through learning question owed on the Monte Carlo reply (route via → cold_email_reply_os).
Gate decision: Positive-reply gate fired on the <1% positive AND opens ≥30% branch → RECYCLE non-responders into a new campaign with a different opener + different artifact offer (Schneider replacement for touches 3–7). The scale ask is refused: opens are not the gate metric. Gate values are internal calibration defaults, not industry standards.
Learning memo (filled template):
# Learning Memo — DataPilot heads-of-data v1 — 2026-06-11
Mode / segment / signal: sales; heads of data, Series B SaaS × first-data-eng hire; n≈396 delivered
Test variable (exactly one): n/a — single-variant baseline
Funnel: delivered 99.1% | open ~38% | reply 0.5% | positive reply 0.25% | meetings 0 | held 0
Trust cost: bounces 0.9% | complaints 0% | opt-outs 1 | angry 0 — internal proxy composite: low
Sample verdict: rate-readable (≈396 ≥ ~200/variant, single persona; large effects only)
Diagnosis (one layer): offer — positive <1% with opens ≥30%; placement and list ruled out
Buyer language (verbatim): - "the pipeline alerting situation is rough" - "We already use Monte Carlo for this"
Objection mix: already-solved/competitor ×1 (Monte Carlo)
Winning / losing lines: subject/preview earning opens (keep) | checklist offer not converting (replace)
Decision: recycle — positive-reply gate (<1% positive AND opens ≥30%); do NOT scale on opens
Next test (one variable): swap artifact to an alert-noise teardown built on the verbatim pain; hold CTA,
persona × signal, list source, sending infra; pre-commit ≈560/arm (3%→6% detection), read once at end
Nurture adds: 1 (positive replier — artifact sent, conversation live)
Nurture accounting: 0 meetings ≠ dead (Predictable Revenue: ~5% of opportunities buy within 90 days; 95% of outbound value is nurture). One live conversation + a named incumbent + verbatim pain language = a partial win, documented.
Provenance
Known gaps (do not paper over)
The trust-consumed composite is internal methodology (see the trust-cost proxy set in Knowledge).
No source covers time-decay of learning (how long a memo stays valid). Until sourced, re-validate any memo older than one provider-enforcement cycle before reusing its verdicts.
Notes
Single-shell skill: the former reference modules (metric diagnostics/benchmarks and decision gates/learning memo) are folded inline — re-slotted across the Judgment (diagnostics, sample rules, gate tree, worksheet), Knowledge (benchmark bands, trust-cost proxy), and Contract (learning memo template) blocks — because both fire on every campaign review — diagnose → gate → memo is the primary job. Shell is ~280 lines / ~25KB incl. the worked example, acceptable per the hook_craft single-shell precedent (no conditional seam).
Primary sources: Connor Murray (stage-by-stage rate diagnostics), Mailshake State of Cold Email 2025 (508-sender survey, self-reported), Cognism State of Outbound 2026 ("directional, not diagnostic"), Lavender benchmark (directional-vendor), Austin Schneider / Instantly (practitioner patterns), Predictable Revenue (validation gates, nurture asymmetry), Evan Miller + Kohavi (experiment validity).
Maintainers: enrichment lineage lives at docs/research/youtube-library/cold-email-children-enrichment-plan-2026-06-10.md (not available at runtime).
