[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M751F02RK0PEJ9GZ82DP20DN":3,"skill-same:01M751F02RK0PEJ9GZ82DP20DN":47},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":29,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M751F02RK0PEJ9GZ82DP20DN","prospecting-metrics",null,"Use when you need to turn outbound prospecting into a measurable, backsolvable funnel instead of guesswork - for quota planning, activity targets, funnel diagnosis, and A\u002FB test sizing. Triggers include \"how many emails do I need to send\", \"backsolve my quota\", \"what's a good reply rate\", \"how many meetings per 100 contacts\", \"my outbound isn't converting\", \"where is my funnel breaking\", \"is this A\u002FB test significant\", \"how many contacts to hit pipeline target\", \"what activity do I need this week\", and \"what conversion rates should I expect for cold outbound\". Workflow: define the canonical funnel, plug in your real (or benchmark) conversion rates, backsolve required weekly activity from a quota, diagnose which stage is broken, and size tests honestly. Do NOT use for writing the actual messages - use [[cold-email-craft]]; Do NOT use for designing the multi-step cadence itself - use [[outreach-sequence-designer]]; Do NOT use for fixing inbox placement - use [[cold-email-deliverability]].","SkillMedev\u002Fskills","SkillMedev","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F298078254?v=4","01M74ZSGTQ918CB0VXW4EG3RMX","SkillMe","01M74ZSGVBT497NA95CBT8JTY4",25,"MIT","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fprospecting-metrics\u002FSKILL.md",[],[],"2026-11-11 18:15:31",false,"---\nname: prospecting-metrics\ndescription: \"Use when you need to turn outbound prospecting into a measurable, backsolvable funnel instead of guesswork - for quota planning, activity targets, funnel diagnosis, and A\u002FB test sizing. Triggers include \\\"how many emails do I need to send\\\", \\\"backsolve my quota\\\", \\\"what's a good reply rate\\\", \\\"how many meetings per 100 contacts\\\", \\\"my outbound isn't converting\\\", \\\"where is my funnel breaking\\\", \\\"is this A\u002FB test significant\\\", \\\"how many contacts to hit pipeline target\\\", \\\"what activity do I need this week\\\", and \\\"what conversion rates should I expect for cold outbound\\\". Workflow: define the canonical funnel, plug in your real (or benchmark) conversion rates, backsolve required weekly activity from a quota, diagnose which stage is broken, and size tests honestly. Do NOT use for writing the actual messages - use [[cold-email-craft]]; Do NOT use for designing the multi-step cadence itself - use [[outreach-sequence-designer]]; Do NOT use for fixing inbox placement - use [[cold-email-deliverability]].\"\n---\n\n# Measure & Backsolve Your Prospecting Funnel\n\nOutbound is a math problem wearing a vibes costume. Every rep \"feels\" like things are working or not; almost none can tell you how many contacts it takes to book one held meeting, or how many contacts per week they need to hit quota. That gap is where pipeline targets quietly die. The core insight: a funnel is a chain of multiplied conversion rates, so a target at the bottom *dictates* the activity at the top - you don't guess your way to quota, you backsolve it.\n\nThe common trap is optimizing the wrong stage. Reps obsess over open rates (now nearly meaningless post-Apple Mail Privacy Protection, which pre-fetches and inflates opens) while ignoring that their *positive* reply rate is 0.4% because they're emailing the wrong persona. Measure the stages that actually predict revenue - positive reply rate, meetings-held-per-100-contacts, contact-to-SQO - and treat the rest as diagnostics, not goals.\n\n## When to use this skill\n- You have a pipeline or meetings quota and need to know the weekly activity (contacts, sequences) required to hit it.\n- Your funnel is underperforming and you need to localize *which* stage is broken and what that points to.\n- You want to project outcomes from a planned activity level (\"if I send 500\u002Fweek, what do I get?\").\n- You're about to call an A\u002FB test a winner and want to know if you have the sample size to.\n- You need realistic benchmark ranges to sanity-check whether a rate is good, bad, or fantasy.\n\n## The funnel (and what each rate roughly is)\n\nThese are **rough industry ranges for cold B2B outbound, not promises** - yours depend on ICP, offer, and channel. Use them only to sanity-check, then replace with your own data ASAP.\n\n| Stage | Transition | Rough cold-email range |\n|---|---|---|\n| Contacts loaded | → delivered | 92-99% (rest = bounces) |\n| Delivered | → opened | *de-emphasize; MPP inflates it* |\n| Delivered | → reply (any) | 1-5% |\n| Reply | → positive reply | 20-35% of replies |\n| Positive reply | → meeting booked | 50-70% |\n| Meeting booked | → meeting held | 65-85% (no-shows kill you) |\n| Meeting held | → SQO | 40-60% |\n| SQO | → closed-won | 15-30% |\n\nThe ratios that actually matter (track these weekly):\n1. **Positive reply rate** = positive replies \u002F delivered. The cleanest signal of targeting + copy quality. Healthy cold: ~0.5-1.5%.\n2. **Meetings-held per 100 contacts** = your single best efficiency number. Cold typically lands 0.5-2 per 100.\n3. **Meeting-held rate** = held \u002F booked. Below ~70% means a scheduling\u002Fconfirmation problem, not a top-of-funnel one.\n4. **Contact-to-SQO** = SQOs \u002F contacts. The number you backsolve quota from.\n\n## The workflow\n1. **Define your funnel** end to end with the stages above. Drop stages you can't reliably measure (opens) rather than fake them.\n2. **Populate conversion rates** - use your trailing 60-90 days if you have ≥ ~300 contacts of history; otherwise start with the benchmark ranges and label them ASSUMPTION.\n3. **Backsolve the quota.** Start from the bottom target (meetings held, SQOs, or pipeline $) and divide back up through each rate to get required contacts, then ÷ weeks to get weekly activity.\n4. **Sanity-check feasibility.** Required contacts\u002Fweek ÷ realistic per-rep capacity (~250-500 fresh cold contacts\u002Fweek\u002Frep at quality) = headcount or rate-improvement needed. If the number is absurd, the fix is conversion rate, not more sends.\n5. **Diagnose breaks** against benchmarks (see failure modes) and route to the right sibling skill.\n6. **Test one variable**, sized for significance, before declaring anything.\n\n## Funnel model template\n\n```\nTARGET (pick one, per quarter):\n  meetings_held_target = ___   OR   pipeline_target_$ = ___ , avg_deal_$ = ___\nweeks = 13\n\nCONVERSION RATES (yours or benchmark - LABEL which):\n  deliverability        = 0.97\n  reply_rate            = 0.030   # replies \u002F delivered\n  positive_of_replies   = 0.28\n  book_of_positive      = 0.60\n  held_of_booked        = 0.75\n  sqo_of_held           = 0.50\n  win_of_sqo            = 0.22\n  avg_deal_$            = 18000\n\nDERIVED:\n  contacts_per_held_meeting = 1 \u002F (deliverability*reply_rate*positive_of_replies*book_of_positive*held_of_booked)\n  required_contacts = (target chained back up) ; weekly = required_contacts \u002F weeks\n```\n\n## WORKED EXAMPLE - backsolve from a pipeline number\n\nTarget: **$1,000,000 in new pipeline this quarter (13 weeks)**, avg deal $18k → defined as SQO value, so **SQOs needed = 1,000,000 \u002F 18,000 = 55.6 → 56 SQOs.**\n\nWalk *up* the funnel, dividing by each rate:\n- Held meetings = 56 \u002F 0.50 (sqo_of_held) = **112**\n- Booked meetings = 112 \u002F 0.75 (held_of_booked) = **149.3 → 150**\n- Positive replies = 112 \u002F (0.75 × 0.60) = 112 \u002F 0.45 = **248.9 → 249**\n- Total replies = 249 \u002F 0.28 = **889**\n- Delivered = 889 \u002F 0.030 = **29,630**\n- Contacts loaded = 29,630 \u002F 0.97 = **30,546**\n\nSo: **≈ 30,550 contacts \u002F 13 weeks ≈ 2,350 contacts\u002Fweek.** At ~400 quality cold contacts\u002Fweek\u002Frep, that's **~6 reps** - or you fix conversion. Note the leverage: lifting positive-of-replies from 0.28 → 0.40 cuts required contacts by ~30% with zero extra sends. That's why copy\u002Ftargeting beats volume almost every time.\n\nHeadline efficiency: contacts_per_held_meeting = 1 \u002F (0.97×0.030×0.28×0.60×0.75) = 1 \u002F 0.00367 = **~273 contacts per held meeting.**\n\n## Runnable artifact - funnel solver (Node, no deps)\n\nSave as `funnel.mjs`. Forward mode projects outcomes from activity; backsolve mode computes required activity from a target.\n\n```js\n#!\u002Fusr\u002Fbin\u002Fenv node\n\u002F\u002F Usage:\n\u002F\u002F   node funnel.mjs backsolve --pipeline 1000000 --deal 18000 --weeks 13\n\u002F\u002F   node funnel.mjs backsolve --held 112 --weeks 13\n\u002F\u002F   node funnel.mjs forward   --contacts-week 2350 --weeks 13\n\u002F\u002F Override any rate, e.g. --reply_rate 0.04 --positive_of_replies 0.40\nconst RATES = {\n  deliverability: 0.97, reply_rate: 0.030, positive_of_replies: 0.28,\n  book_of_positive: 0.60, held_of_booked: 0.75, sqo_of_held: 0.50,\n  win_of_sqo: 0.22, avg_deal: 18000,\n};\nconst a = process.argv.slice(2);\nconst mode = a[0];\nconst opt = (k, d) => { const i = a.indexOf(`--${k}`); return i > -1 ? Number(a[i + 1]) : d; };\nfor (const k of Object.keys(RATES)) RATES[k] = opt(k, RATES[k]);\nconst weeks = opt(\"weeks\", 13);\nconst r = RATES;\nconst c2held = r.deliverability * r.reply_rate * r.positive_of_replies * r.book_of_positive * r.held_of_booked;\nconst fmt = (n) => Number.isInteger(n) ? n : Math.ceil(n);\nconst money = (n) => \"$\" + Math.round(n).toLocaleString();\n\nif (mode === \"backsolve\") {\n  let held = opt(\"held\", null);\n  if (held == null) {\n    const pipeline = opt(\"pipeline\", null);\n    const sqos = pipeline != null ? pipeline \u002F r.avg_deal : opt(\"sqos\", 56);\n    held = sqos \u002F r.sqo_of_held;\n  }\n  const booked   = held \u002F r.held_of_booked;\n  const positive = booked \u002F r.book_of_positive;\n  const replies  = positive \u002F r.positive_of_replies;\n  const delivered= replies \u002F r.reply_rate;\n  const contacts = delivered \u002F r.deliverability;\n  const sqos = held * r.sqo_of_held, won = sqos * r.win_of_sqo;\n  console.log(\"=== BACKSOLVE ===\");\n  console.log(\"contacts\u002Fheld meeting:\", c2held ? (1 \u002F c2held).toFixed(0) : \"n\u002Fa\");\n  console.table({\n    contacts_loaded:{total:fmt(contacts), per_week:fmt(contacts\u002Fweeks)},\n    delivered:{total:fmt(delivered)}, replies:{total:fmt(replies)},\n    positive_replies:{total:fmt(positive)}, meetings_booked:{total:fmt(booked)},\n    meetings_held:{total:fmt(held)}, sqos:{total:fmt(sqos)},\n    closed_won:{total:fmt(won)}, pipeline:{total:money(sqos*r.avg_deal)},\n  });\n} else if (mode === \"forward\") {\n  const cw = opt(\"contacts-week\", 400);\n  const contacts = cw * weeks;\n  const delivered = contacts * r.deliverability;\n  const replies = delivered * r.reply_rate;\n  const positive = replies * r.positive_of_replies;\n  const booked = positive * r.book_of_positive;\n  const held = booked * r.held_of_booked;\n  const sqos = held * r.sqo_of_held;\n  const won = sqos * r.win_of_sqo;\n  console.log(\"=== FORWARD PROJECTION ===\");\n  console.table({\n    contacts:{total:fmt(contacts)}, delivered:{total:fmt(delivered)},\n    replies:{total:fmt(replies)}, positive_replies:{total:fmt(positive)},\n    meetings_booked:{total:fmt(booked)}, meetings_held:{total:fmt(held)},\n    sqos:{total:fmt(sqos)}, closed_won:{total:fmt(won)},\n    pipeline:{total:money(sqos*r.avg_deal)},\n  });\n} else {\n  console.log(\"modes: backsolve | forward (see header for flags)\");\n}\n```\n\n## A\u002FB testing without lying to yourself\nCold-outbound reply rates are low, so you need *big* samples to detect small lifts. Rough intuition: to reliably tell a 2% reply rate from a 4% one you need on the order of ~1,000+ sends per variant; to split-hair a 2.0% vs 2.4% you need several thousand. **Never call a winner on 40 sends** - at a 2% base rate that's an expected 0.8 replies; the \"winner\" is noise. Rules: change exactly one variable (subject OR first line OR CTA, never all three), run both variants concurrently to the same ICP slice (channel\u002Ftiming\u002Flist quality confound everything), and pre-commit the sample size. Hand sequence-structure tests to [[outreach-sequence-designer]].\n\n## Deliverable\n\nProduce a funnel model the team can rerun: the filled funnel template with every rate labeled MEASURED or ASSUMPTION, the backsolve from the quarter's target down to required contacts\u002Fweek (with the feasibility check against per-rep capacity), the headline contacts-per-held-meeting number, and the `funnel.mjs` artifact configured with those rates so the model updates in one command when the real data lands.\n\n## Quality bar\n\n- Every conversion rate is labeled as measured (with the trailing window and sample size) or as a benchmark assumption.\n- The backsolve chain is shown stage by stage, not just the final contacts number.\n- The weekly activity requirement is checked against realistic rep capacity (~250-500 quality contacts\u002Fweek\u002Frep) and the gap, if any, is named.\n- No goal or test is gated on open rates.\n- Any A\u002FB claim carries its per-variant sample size.\n\n## Common failure modes\n- **Low delivery (\u003C90%) \u002F bounces high.** Not a copy problem - it's infrastructure. Domain reputation, warmup, list hygiene. Go to [[cold-email-deliverability]]. Sending more into a deliverability hole just burns the domain faster.\n- **Low reply rate (\u003C1%) with good delivery.** Wrong people or wrong message. Re-check ICP\u002Fpersona fit ([[icp-persona-builder]]); then the message itself ([[cold-email-craft]]). Don't add volume on top of a broken message.\n- **Decent replies, low *positive* reply rate.** You're reaching humans but the offer\u002Frelevance misses - usually targeting drift or a generic value prop. [[icp-persona-builder]] + [[cold-email-craft]].\n- **Positive replies that don't become booked meetings.** Slow\u002Fambiguous follow-up or weak CTA. Tighten the booking step in [[outreach-sequence-designer]].\n- **Meeting-held rate \u003C70%.** No-shows. This is a scheduling\u002Fconfirmation problem (reminders, calendar holds, shorter book-to-meeting gap), not top-of-funnel - adding contacts won't fix it.\n- **Held but no SQOs.** Qualification problem: you're booking the wrong meetings. Tighten ICP and pre-call qualification, not volume.\n- **Optimizing opens.** Stop. Post-MPP, opens are inflated and noisy; never gate a test or a goal on them. Use reply-based metrics.\n- **Lagging-only review.** Closed-won lags weeks. Review *leading* indicators weekly (contacts sent, reply rate, positive reply rate, meetings booked); review *lagging* (SQO, win rate, pipeline $) monthly\u002Fquarterly. A sane cadence: weekly activity-and-reply standup, monthly conversion-rate recalibration, quarterly quota backsolve.\n","# Measure & Backsolve Your Prospecting Funnel\n\nOutbound is a math problem wearing a vibes costume. Every rep \"feels\" like things are working or not; almost none can tell you how many contacts it takes to book one held meeting, or how many contacts per week they need to hit quota. That gap is where pipeline targets quietly die. The core insight: a funnel is a chain of multiplied conversion rates, so a target at the bottom *dictates* the activity at the top - you don't guess your way to quota, you backsolve it.\n\nThe common trap is optimizing the wrong stage. Reps obsess over open rates (now nearly meaningless post-Apple Mail Privacy Protection, which pre-fetches and inflates opens) while ignoring that their *positive* reply rate is 0.4% because they're emailing the wrong persona. Measure the stages that actually predict revenue - positive reply rate, meetings-held-per-100-contacts, contact-to-SQO - and treat the rest as diagnostics, not goals.\n\n## When to use this skill\n- You have a pipeline or meetings quota and need to know the weekly activity (contacts, sequences) required to hit it.\n- Your funnel is underperforming and you need to localize *which* stage is broken and what that points to.\n- You want to project outcomes from a planned activity level (\"if I send 500\u002Fweek, what do I get?\").\n- You're about to call an A\u002FB test a winner and want to know if you have the sample size to.\n- You need realistic benchmark ranges to sanity-check whether a rate is good, bad, or fantasy.\n\n## The funnel (and what each rate roughly is)\n\nThese are **rough industry ranges for cold B2B outbound, not promises** - yours depend on ICP, offer, and channel. Use them only to sanity-check, then replace with your own data ASAP.\n\n| Stage | Transition | Rough cold-email range |\n|---|---|---|\n| Contacts loaded | → delivered | 92-99% (rest = bounces) |\n| Delivered | → opened | *de-emphasize; MPP inflates it* |\n| Delivered | → reply (any) | 1-5% |\n| Reply | → positive reply | 20-35% of replies |\n| Positive reply | → meeting booked | 50-70% |\n| Meeting booked | → meeting held | 65-85% (no-shows kill you) |\n| Meeting held | → SQO | 40-60% |\n| SQO | → closed-won | 15-30% |\n\nThe ratios that actually matter (track these weekly):\n1. **Positive reply rate** = positive replies \u002F delivered. The cleanest signal of targeting + copy quality. Healthy cold: ~0.5-1.5%.\n2. **Meetings-held per 100 contacts** = your single best efficiency number. Cold typically lands 0.5-2 per 100.\n3. **Meeting-held rate** = held \u002F booked. Below ~70% means a scheduling\u002Fconfirmation problem, not a top-of-funnel one.\n4. **Contact-to-SQO** = SQOs \u002F contacts. The number you backsolve quota from.\n\n## The workflow\n1. **Define your funnel** end to end with the stages above. Drop stages you can't reliably measure (opens) rather than fake them.\n2. **Populate conversion rates** - use your trailing 60-90 days if you have ≥ ~300 contacts of history; otherwise start with the benchmark ranges and label them ASSUMPTION.\n3. **Backsolve the quota.** Start from the bottom target (meetings held, SQOs, or pipeline $) and divide back up through each rate to get required contacts, then ÷ weeks to get weekly activity.\n4. **Sanity-check feasibility.** Required contacts\u002Fweek ÷ realistic per-rep capacity (~250-500 fresh cold contacts\u002Fweek\u002Frep at quality) = headcount or rate-improvement needed. If the number is absurd, the fix is conversion rate, not more sends.\n5. **Diagnose breaks** against benchmarks (see failure modes) and route to the right sibling skill.\n6. **Test one variable**, sized for significance, before declaring anything.\n\n## Funnel model template\n\n```\nTARGET (pick one, per quarter):\n  meetings_held_target = ___   OR   pipeline_target_$ = ___ , avg_deal_$ = ___\nweeks = 13\n\nCONVERSION RATES (yours or benchmark - LABEL which):\n  deliverability        = 0.97\n  reply_rate            = 0.030   # replies \u002F delivered\n  positive_of_replies   = 0.28\n  book_of_positive      = 0.60\n  held_of_booked        = 0.75\n  sqo_of_held           = 0.50\n  win_of_sqo            = 0.22\n  avg_deal_$            = 18000\n\nDERIVED:\n  contacts_per_held_meeting = 1 \u002F (deliverability*reply_rate*positive_of_replies*book_of_positive*held_of_booked)\n  required_contacts = (target chained back up) ; weekly = required_contacts \u002F weeks\n```\n\n## WORKED EXAMPLE - backsolve from a pipeline number\n\nTarget: **$1,000,000 in new pipeline this quarter (13 weeks)**, avg deal $18k → defined as SQO value, so **SQOs needed = 1,000,000 \u002F 18,000 = 55.6 → 56 SQOs.**\n\nWalk *up* the funnel, dividing by each rate:\n- Held meetings = 56 \u002F 0.50 (sqo_of_held) = **112**\n- Booked meetings = 112 \u002F 0.75 (held_of_booked) = **149.3 → 150**\n- Positive replies = 112 \u002F (0.75 × 0.60) = 112 \u002F 0.45 = **248.9 → 249**\n- Total replies = 249 \u002F 0.28 = **889**\n- Delivered = 889 \u002F 0.030 = **29,630**\n- Contacts loaded = 29,630 \u002F 0.97 = **30,546**\n\nSo: **≈ 30,550 contacts \u002F 13 weeks ≈ 2,350 contacts\u002Fweek.** At ~400 quality cold contacts\u002Fweek\u002Frep, that's **~6 reps** - or you fix conversion. Note the leverage: lifting positive-of-replies from 0.28 → 0.40 cuts required contacts by ~30% with zero extra sends. That's why copy\u002Ftargeting beats volume almost every time.\n\nHeadline efficiency: contacts_per_held_meeting = 1 \u002F (0.97×0.030×0.28×0.60×0.75) = 1 \u002F 0.00367 = **~273 contacts per held meeting.**\n\n## Runnable artifact - funnel solver (Node, no deps)\n\nSave as `funnel.mjs`. Forward mode projects outcomes from activity; backsolve mode computes required activity from a target.\n\n```js\n#!\u002Fusr\u002Fbin\u002Fenv node\n\u002F\u002F Usage:\n\u002F\u002F   node funnel.mjs backsolve --pipeline 1000000 --deal 18000 --weeks 13\n\u002F\u002F   node funnel.mjs backsolve --held 112 --weeks 13\n\u002F\u002F   node funnel.mjs forward   --contacts-week 2350 --weeks 13\n\u002F\u002F Override any rate, e.g. --reply_rate 0.04 --positive_of_replies 0.40\nconst RATES = {\n  deliverability: 0.97, reply_rate: 0.030, positive_of_replies: 0.28,\n  book_of_positive: 0.60, held_of_booked: 0.75, sqo_of_held: 0.50,\n  win_of_sqo: 0.22, avg_deal: 18000,\n};\nconst a = process.argv.slice(2);\nconst mode = a[0];\nconst opt = (k, d) => { const i = a.indexOf(`--${k}`); return i > -1 ? Number(a[i + 1]) : d; };\nfor (const k of Object.keys(RATES)) RATES[k] = opt(k, RATES[k]);\nconst weeks = opt(\"weeks\", 13);\nconst r = RATES;\nconst c2held = r.deliverability * r.reply_rate * r.positive_of_replies * r.book_of_positive * r.held_of_booked;\nconst fmt = (n) => Number.isInteger(n) ? n : Math.ceil(n);\nconst money = (n) => \"$\" + Math.round(n).toLocaleString();\n\nif (mode === \"backsolve\") {\n  let held = opt(\"held\", null);\n  if (held == null) {\n    const pipeline = opt(\"pipeline\", null);\n    const sqos = pipeline != null ? pipeline \u002F r.avg_deal : opt(\"sqos\", 56);\n    held = sqos \u002F r.sqo_of_held;\n  }\n  const booked   = held \u002F r.held_of_booked;\n  const positive = booked \u002F r.book_of_positive;\n  const replies  = positive \u002F r.positive_of_replies;\n  const delivered= replies \u002F r.reply_rate;\n  const contacts = delivered \u002F r.deliverability;\n  const sqos = held * r.sqo_of_held, won = sqos * r.win_of_sqo;\n  console.log(\"=== BACKSOLVE ===\");\n  console.log(\"contacts\u002Fheld meeting:\", c2held ? (1 \u002F c2held).toFixed(0) : \"n\u002Fa\");\n  console.table({\n    contacts_loaded:{total:fmt(contacts), per_week:fmt(contacts\u002Fweeks)},\n    delivered:{total:fmt(delivered)}, replies:{total:fmt(replies)},\n    positive_replies:{total:fmt(positive)}, meetings_booked:{total:fmt(booked)},\n    meetings_held:{total:fmt(held)}, sqos:{total:fmt(sqos)},\n    closed_won:{total:fmt(won)}, pipeline:{total:money(sqos*r.avg_deal)},\n  });\n} else if (mode === \"forward\") {\n  const cw = opt(\"contacts-week\", 400);\n  const contacts = cw * weeks;\n  const delivered = contacts * r.deliverability;\n  const replies = delivered * r.reply_rate;\n  const positive = replies * r.positive_of_replies;\n  const booked = positive * r.book_of_positive;\n  const held = booked * r.held_of_booked;\n  const sqos = held * r.sqo_of_held;\n  const won = sqos * r.win_of_sqo;\n  console.log(\"=== FORWARD PROJECTION ===\");\n  console.table({\n    contacts:{total:fmt(contacts)}, delivered:{total:fmt(delivered)},\n    replies:{total:fmt(replies)}, positive_replies:{total:fmt(positive)},\n    meetings_booked:{total:fmt(booked)}, meetings_held:{total:fmt(held)},\n    sqos:{total:fmt(sqos)}, closed_won:{total:fmt(won)},\n    pipeline:{total:money(sqos*r.avg_deal)},\n  });\n} else {\n  console.log(\"modes: backsolve | forward (see header for flags)\");\n}\n```\n\n## A\u002FB testing without lying to yourself\nCold-outbound reply rates are low, so you need *big* samples to detect small lifts. Rough intuition: to reliably tell a 2% reply rate from a 4% one you need on the order of ~1,000+ sends per variant; to split-hair a 2.0% vs 2.4% you need several thousand. **Never call a winner on 40 sends** - at a 2% base rate that's an expected 0.8 replies; the \"winner\" is noise. Rules: change exactly one variable (subject OR first line OR CTA, never all three), run both variants concurrently to the same ICP slice (channel\u002Ftiming\u002Flist quality confound everything), and pre-commit the sample size. Hand sequence-structure tests to [[outreach-sequence-designer]].\n\n## Deliverable\n\nProduce a funnel model the team can rerun: the filled funnel template with every rate labeled MEASURED or ASSUMPTION, the backsolve from the quarter's target down to required contacts\u002Fweek (with the feasibility check against per-rep capacity), the headline contacts-per-held-meeting number, and the `funnel.mjs` artifact configured with those rates so the model updates in one command when the real data lands.\n\n## Quality bar\n\n- Every conversion rate is labeled as measured (with the trailing window and sample size) or as a benchmark assumption.\n- The backsolve chain is shown stage by stage, not just the final contacts number.\n- The weekly activity requirement is checked against realistic rep capacity (~250-500 quality contacts\u002Fweek\u002Frep) and the gap, if any, is named.\n- No goal or test is gated on open rates.\n- Any A\u002FB claim carries its per-variant sample size.\n\n## Common failure modes\n- **Low delivery (\u003C90%) \u002F bounces high.** Not a copy problem - it's infrastructure. Domain reputation, warmup, list hygiene. Go to [[cold-email-deliverability]]. Sending more into a deliverability hole just burns the domain faster.\n- **Low reply rate (\u003C1%) with good delivery.** Wrong people or wrong message. Re-check ICP\u002Fpersona fit ([[icp-persona-builder]]); then the message itself ([[cold-email-craft]]). Don't add volume on top of a broken message.\n- **Decent replies, low *positive* reply rate.** You're reaching humans but the offer\u002Frelevance misses - usually targeting drift or a generic value prop. [[icp-persona-builder]] + [[cold-email-craft]].\n- **Positive replies that don't become booked meetings.** Slow\u002Fambiguous follow-up or weak CTA. Tighten the booking step in [[outreach-sequence-designer]].\n- **Meeting-held rate \u003C70%.** No-shows. This is a scheduling\u002Fconfirmation problem (reminders, calendar holds, shorter book-to-meeting gap), not top-of-funnel - adding contacts won't fix it.\n- **Held but no SQOs.** Qualification problem: you're booking the wrong meetings. Tighten ICP and pre-call qualification, not volume.\n- **Optimizing opens.** Stop. Post-MPP, opens are inflated and noisy; never gate a test or a goal on them. Use reply-based metrics.\n- **Lagging-only review.** Closed-won lags weeks. Review *leading* indicators weekly (contacts sent, reply rate, positive reply rate, meetings booked); review *lagging* (SQO, win rate, pipeline $) monthly\u002Fquarterly. A sane cadence: weekly activity-and-reply standup, monthly conversion-rate recalibration, quarterly quota backsolve.\n",{"name":5,"description":7},"main","97a13607d669157a4a16d5ba79b77f208319f7b9","skills\u002Fprospecting-metrics",3,"https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills","https:\u002F\u002Fskillme.dev","https:\u002F\u002Fgithub.com\u002FSkillMedev","MCP-native catalog of Claude Agent Skills. Browse, recommend, install, and auto-apply skills in chat. Open source, every skill free and clonable on GitHub.","2026-10-14 03:19:31",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add SkillMedev\u002Fskills --skill prospecting-metrics",{"tool":39,"command":40},"bunx","bunx skills add SkillMedev\u002Fskills --skill prospecting-metrics",{"tool":42,"command":43},"pnpm","pnpm dlx skills add SkillMedev\u002Fskills --skill prospecting-metrics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fprospecting-metrics\u002FSKILL.md",true,[],[48,58,65,72,79,86],{"skillNo":49,"name":50,"nameZh":51,"description":52,"descriptionZh":53,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":54,"categories":55,"keywords":56,"publishedAt":57},"01M75271B256DF3FX2VG5VRYEE","zone-2-cardio-plan","有氧运动计划","Use when someone asks to build a steady-state cardio plan, train an aerobic base or Zone 2, structure easy or endurance weekly cardio, train by heart-rate zones, or improve resting heart rate, VO2, or endurance. Builds a heart-rate-zone aerobic training block with session structure, modality choice, and progression. General fitness guidance, not medical advice. Do NOT use to design a strength or lifting program (use Strength Training Plan), high-intensity interval conditioning, HIIT, or circuits (use Conditioning & HIIT Program), or a mobility\u002Fflexibility routine (use Mobility Routine).","当有人要求制定稳态有氧训练计划、进行有氧基础训练或达到第二区间、安排轻松或耐力型的每周有氧训练、按心率区间训练，或提高静息心率、最大摄氧量或耐力时，可以使用此方案。该方案构建了一个包含训练计划结构、训练方式选择和进度安排的心率区间有氧训练模块。本方案仅提供一般健身指导，不构成医疗建议。请勿用于设计力量或举重训练计划（请使用力量训练计划）、高强度间歇训练 (HIIT) 或循环训练（请使用体能训练和 HIIT 计划），或灵活性\u002F柔韧性训练（请使用灵活性训练计划）。","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fzone-2-cardio-plan\u002FSKILL.md",[],[],"2026-11-11 18:27:18",{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":57},"01M7526VP1QBBKYBE17S1WR86Y","youtube-script-writer","Writes retention-engineered long-form YouTube scripts on a hook\u002Fbody\u002Fpayoff architecture - a first-30-seconds hook that survives the retention cliff, a beat-sheet body with mini-hooks and bridges, timed pattern interrupts, and a payoff that over-delivers - with B-roll and editing cues marked inline. Use when someone says \"script my next video\", \"my retention graph dies at the start\", \"tighten this draft\", or \"outline a video on X\". Do NOT use for the 3-second opener of TikTok, Reels, or Shorts - use video-hook-writer instead; do NOT use for spoken voiceover synced to on-screen tutorial actions - use narration-script instead; do NOT use for a sales demo built on a buyer's discovery notes - use demo-script instead; do NOT use to design the thumbnail and title - use thumbnail-concept instead.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fyoutube-script-writer\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":57},"01M7526P2T0WKMHDXS4Q58237T","win-back-campaign","Designs segmented win-back email sequences for dormant users - dormancy tiers, a three-beat sequence with a single proportional incentive, and a suppression rule - and measures success by re-activation, not opens. Use when someone says \"our users went quiet\", \"design a win-back campaign\", \"how do I re-engage lapsed customers\", \"should we email people who haven't logged in for months\", or lifecycle messaging has stopped landing with a lapsed segment. Do NOT use for recovering an unfinished checkout - use abandoned-cart-sequence instead. Do NOT use for diagnosing and fixing why active customers cancel - use churn-reduction instead. Do NOT use for building always-on onboarding or nurture flows - use email-drip-builder instead.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwin-back-campaign\u002FSKILL.md",[],[],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":57},"01M7526HF2RCDJ50P93WRZRHVV","wikipedia-style","Writes neutral, verifiable, encyclopedic prose following Wikipedia's core content policies and manual of style - NPOV, inline citations, notability-backed claims, and a strict ban on promotional language. Use when someone asks \"write this like a Wikipedia article\", \"draft a Wikipedia page for this company or person\", \"make this neutral and encyclopedic\", or \"why does my article draft keep getting rejected\". Do NOT use for persuasive or opinion writing - use op-ed-writer instead; for thesis-driven academic papers, use academic-essay; for verifying claims in existing text, use fact-checker.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwikipedia-style\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":57},"01M7526D43CTGRKGKNQ2GB0KVX","whitepapers","Structures and writes authoritative B2B whitepapers - a standalone executive summary, a data-sized problem statement, methodology, evidence-driven findings, recommendations, and a soft call to action - flagging every claim that needs a real citation. Use when a marketer asks \"write a whitepaper on this topic\", \"turn our research into a lead-gen asset\", \"make this credible enough for executives\", or a draft reads like a product pitch instead of analysis. Do NOT use for customer-story-driven proof - use case-study-builder instead; for policy audiences, use policy-brief.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwhitepapers\u002FSKILL.md",[],[],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":57},"01M75268423Y3K50SY5WC2NX2G","white-space-analysis","Finds unmet market gaps by ranking jobs-to-be-done on importance versus satisfaction, overlaying them against current solutions and workarounds, and validating that each gap is real, winnable, and worth winning. Use when someone asks \"where are the gaps in this market\", \"what's underserved here\", \"where should we play next\", or is hunting for product, feature, or market-entry opportunities. Do NOT use for profiling competitors and building battle cards - use competitive-intelligence instead; for putting a dollar figure on an identified opportunity - use market-sizing instead; for extracting jobs-to-be-done from raw customer interviews - use jtbd-extractor instead.","https:\u002F\u002Fgithub.com\u002FSkillMedev\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fwhite-space-analysis\u002FSKILL.md",[],[]]