[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M77B5GG0MP2ZGKYCD6MBKACG":3,"skill-same:01M77B5GG0MP2ZGKYCD6MBKACG":65},{"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":43,"commitSha":44,"skillDirectory":45,"forksCount":14,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":48,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M77B5GG0MP2ZGKYCD6MBKACG","commercial-forecaster",null,"Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit\u002Fbest-case\u002Fpipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR\u002FGRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance\u002Ffinancial-analysis). NOT strategic CRO hiring\u002Fterritory (see c-level-advisor\u002Fcro-advisor). NOT pricing (see sibling pricing-strategist).","haresh-sai06\u002FSapSynk-AI","haresh-sai06","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F180730984?v=4","01M75NJVH4TXYCE7FW7YVMSVFF","Haresh Sainaath S","01M75NJVHTCW1Z2P6MNCYGNSTJ",0,"MIT","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__commercial-forecaster-2\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: commercial-forecaster\ndescription: \"Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit\u002Fbest-case\u002Fpipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR\u002FGRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance\u002Ffinancial-analysis). NOT strategic CRO hiring\u002Fterritory (see c-level-advisor\u002Fcro-advisor). NOT pricing (see sibling pricing-strategist).\"\nversion: 2.8.0\nauthor: claude-code-skills\nlicense: MIT\ntags: [commercial, forecasting, bookings, arr, nrr, grr, cohort, funnel, pipeline-math]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# commercial-forecaster\n\n## Purpose\n\nHelp Commercial leaders answer three questions at the forecast moment:\n\n1. **What's the commit \u002F best-case \u002F pipe-only number?** (3-tier bookings forecast with disclosed assumptions)\n2. **Which cohorts are leaking, and is the consolidated NRR hiding the leak?** (per-cohort NRR\u002FGRR projection over horizon)\n3. **Which funnel stages are reliable, and which are statistical noise?** (per-stage coefficient-of-variation confidence band)\n\nThe skill recommends **three forecast numbers + an explicit assumption block**. The CRO presents the number, the board sees the assumptions, the theatre dies.\n\n## When to use\n\n- Building the quarterly bookings forecast for the board\n- Preparing the QBR forecast where the CFO will ask \"what's the commit, what's the best-case, what's the pipe-only\"\n- Projecting ARR for next 4-8 quarters using cohort retention data\n- Suspecting a consolidated NRR number is hiding a leaky recent cohort\n- Pipeline-coverage is shrinking and you need to know which stages are still trustworthy\n- You're being asked for a \"single number\" and you need the structured answer that surfaces the assumption\n\n**Do not use for:**\n- Backward-looking financial close + reporting → `finance\u002Ffinancial-analysis`\n- Strategic financial planning (multi-year, scenario, fundraise) → `c-level-advisor\u002Fcfo-advisor`\n- \"Should we hire a VP Sales?\" \u002F territory design \u002F comp plan → `c-level-advisor\u002Fcro-advisor`\n- Setting prices → sibling `pricing-strategist` (projects revenue *at* prices already set)\n- Per-deal discount approval → sibling `deal-desk`\n\n## Workflow\n\n### Step 1 — Intake pipeline + cohort + historical conversion data\n\nFill `assets\u002Fforecast_intake_template.md` (≈ 20 min). Captures: opportunity list with stage\u002Famount\u002Fclose-date\u002Fage\u002Flast-activity; historical stage-to-stage conversion across last 4Q and last 12Q; per-cohort ARR + per-quarter retention + expansion data; funnel stage names with 12-quarter conversion history.\n\n### Step 2 — Run 3-tier bookings forecast\n\n```\nscripts\u002Fbookings_forecaster.py --input intake.json --profile saas --output markdown\n```\n\nOutputs three numbers — **commit**, **best-case**, **pipe-only** — each with the conversion rate applied, the data window used (last-4Q vs. last-12Q weighted 70\u002F30), and the time-to-close probability adjustment. Surfaces variance between commit and pipe-only as the pipeline-risk indicator.\n\n**The assumption block is non-optional.** If you remove it, the forecast becomes theatre.\n\n### Step 3 — Project cohort-level ARR\n\n```\nscripts\u002Fcohort_arr_projector.py --input intake.json --output markdown\n```\n\nComputes per-cohort NRR + GRR over the projection horizon. Flags any cohort whose NRR is declining vs. the trailing-cohort average — these are the leaky cohorts that the consolidated number will hide for 2-3 quarters before the leak surfaces in the topline.\n\nOutput includes the consolidated NRR\u002FGRR trajectory + the cohort heatmap + a leaky-cohort callout.\n\n### Step 4 — Score per-stage funnel confidence\n\n```\nscripts\u002Ffunnel_confidence_scorer.py --input intake.json --output markdown\n```\n\nPer stage: mean conversion %, standard deviation, coefficient of variation (CoV = StDev \u002F Mean), confidence band (HIGH \u003C 10%, MEDIUM 10-25%, LOW 25-50%, VERY LOW > 50%). Recommends treatment per stage: extend-data-window, treat-as-soft-floor, or commit-quality.\n\n### Step 5 — Assemble the forecast deck\n\nTake the 3-tier bookings number + cohort heatmap + funnel confidence into the QBR \u002F board deck. **The assumption block goes on the slide with the number.** If the slide has a single number and no assumption block, the slide is theatre.\n\n## Scripts\n\n- `scripts\u002Fbookings_forecaster.py` — 3-tier bookings forecast (commit \u002F best-case \u002F pipe-only) with disclosed conversion-rate + data-window + weighting block\n- `scripts\u002Fcohort_arr_projector.py` — per-cohort NRR\u002FGRR projection over horizon with leaky-cohort callout\n- `scripts\u002Ffunnel_confidence_scorer.py` — per-stage CoV-based confidence bands with treatment recommendation\n\nAll scripts: stdlib only. `--help` and `--sample` work on all three.\n\n## References\n\n- `references\u002Fsaas_forecasting_canon.md` — Skok, Tunguz, OpenView, BVP, Pacific Crest\u002FKeyBanc, ProfitWell, Patrick Campbell\n- `references\u002Fcohort_analysis_canon.md` — Andrew Chen (a16z), Brian Balfour, Skok, Ramanujam, OpenView, Lenny Rachitsky, Reforge\n- `references\u002Fforecast_anti_patterns.md` — McKinsey, Tunguz, OpenView, MIT Sloan, Bain, Forrester, Pacific Crest\n\n## Assumptions\n\n- **Historical conversion is the prior, not the truth.** Last 4Q is weighted 70%, last 12Q is weighted 30%. The blend captures regime change (recent slowdown) without overfitting to a single bad quarter. Window + weighting are surfaced in every output.\n- **A forecast without a disclosed assumption block is theatre.** This is the skill's hard rule. The CLI refuses to omit the assumption block.\n- **Cohort decomposition reveals leaks 2-3 quarters before the consolidated number does.** Reporting NRR without per-cohort breakdown hides the leak.\n- **CoV (coefficient of variation) is the right discipline for stage confidence.** A stage with mean conversion 40% and stdev 4% (CoV 10%) is HIGH confidence; mean 40% stdev 20% (CoV 50%) is VERY LOW. The same average masks very different reliability.\n- **Industry profile tunes priors, not truth.** Profile shifts default stage-conversion rates by industry; your historical data overrides.\n- **The skill emits three numbers and an assumption block.** The CRO picks the commit number, owns the trade-off, and walks the board through the variance.\n\n## Anti-patterns\n\n- **Single-number forecast with no confidence band.** The board asks for \"the number\"; the discipline is to present three with named assumptions. See `forecast_anti_patterns.md`.\n- **Using last-12-quarter conversion blindly.** Hides recent slowdown. The 70\u002F30 blend on last-4Q vs. last-12Q corrects this.\n- **Reporting NRR without cohort decomposition.** The consolidated number can be flat while a recent cohort is leaking 15 pp; the leak surfaces in the topline 2-3 quarters later. Always decompose.\n- **Treating best-case as commit.** The CFO will eat you. Best-case includes weighted-stage opps that have a \u003C 50% time-to-close probability; commit only includes commit-grade stages.\n- **Hiding the assumption block.** The skill refuses; if you remove it manually, you own the theatre.\n- **No leaky-cohort callout.** If `cohort_arr_projector.py` flags a cohort and you suppress the flag in the deck, the leak owns you next quarter.\n- **Ignoring late-stage opp age.** A \"verbal\" deal that's been verbal for 180 days is not a commit. The bookings forecaster downweights stalled opps automatically; do not re-up them by hand.\n- **No pipeline-coverage check.** Industry rule of thumb: forecast > pipeline ÷ 3 is anti-pattern. The tool surfaces the ratio; respect it.\n\n## Distinct from\n\n- **`finance\u002Ffinancial-analysis`** — backward-looking financial close, GAAP\u002FIFRS reporting, variance vs. budget. commercial-forecaster is forward-looking pipeline math.\n- **`c-level-advisor\u002Fcfo-advisor`** — strategic multi-year financial planning, fundraise scenarios, runway. commercial-forecaster is one input to the CFO, not the strategy.\n- **`c-level-advisor\u002Fcro-advisor`** — strategic CRO judgment: \"do we hire a VP Sales?\", territory design, comp plan, when to add a sales engineer. commercial-forecaster is the math the CRO uses; cro-advisor is the judgment the CRO applies.\n- **sibling `pricing-strategist`** — sets the price (model + range). commercial-forecaster *projects revenue at those prices*. Pricing comes first; forecast comes after.\n- **sibling `deal-desk`** — per-deal scoring + discount approval routing. commercial-forecaster aggregates the pipeline that deal-desk operates on day-by-day.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time by `\u002Fcs:grill-commercial` or the orchestrator. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"What conversion rate are you using, and is it last-4Q or last-12Q?\"**\n   Recommended: a 70\u002F30 blend (last-4Q weighted 70%, last-12Q weighted 30%). Last-12Q alone hides recent slowdown; last-4Q alone overfits one bad quarter.\n   Canon: Tomasz Tunguz (Theory Ventures) — forecasting studies show single-window conversion estimates miss regime change at ~3-quarter lag.\n\n2. **\"What's your pipeline coverage ratio, and is your commit above pipeline ÷ 3?\"**\n   Recommended: 3x coverage is the SaaS-industry floor; below 3x means your commit is structurally unsupported.\n   Canon: Pacific Crest \u002F KeyBanc SaaS Survey — top-quartile SaaS companies maintain 3.0-4.5x pipeline coverage against committed bookings.\n\n3. **\"Can you show me NRR by cohort, not just consolidated?\"**\n   Recommended: never report a consolidated NRR without the per-cohort breakdown. Leaky cohorts hide in averages.\n   Canon: Patrick Campbell (ProfitWell) + David Skok — cohort-driven retention decomposition surfaces leaks 2-3 quarters before consolidated NRR moves.\n\n4. **\"What's the variance (CoV) on each stage's conversion rate over the last 12 quarters?\"**\n   Recommended: CoV \u003C 10% → commit-grade; 10-25% → moderate; 25-50% → soft floor only; > 50% → do not use this stage for forecasting.\n   Canon: MIT Sloan forecasting research \u002F Hyndman & Athanasopoulos (*Forecasting: Principles and Practice*) — CoV on the input series predicts forecast accuracy more reliably than mean.\n\n5. **\"How long has each late-stage opp been in late-stage?\"**\n   Recommended: stage-age > 2x the median stage-duration → treat as stalled, exclude from commit, keep in pipe-only.\n   Canon: David Skok (*For Entrepreneurs*) — stalled-opp identification by stage-age is the #1 forecast hygiene practice in top-decile SaaS pipelines.\n\n6. **\"Is your best-case forecast within 30% of your pipe-only?\"**\n   Recommended: if best-case is \u003C 50% of pipe-only, your stage-conversion assumptions are pessimistic and you're sandbagging; if best-case > 80% of pipe-only, you're hockey-sticking.\n   Canon: McKinsey research on forecast bias + OpenView SaaS benchmarks — most teams operate in one of two failure modes: sandbagging (commit \u003C\u003C earnings) or hockey-sticking (commit >> earnings).\n\n7. **\"What assumption block accompanies the number on the board slide?\"**\n   Recommended: every forecast number on a board slide names (a) the conversion rate, (b) the data window, (c) the weighting choice, (d) the pipeline-coverage ratio. No assumption block = the slide is theatre.\n   Canon: Bain & Company commercial-forecasting practice + Forrester pipeline-coverage research — undisclosed-assumption forecasts have 2.3x higher variance against actuals than disclosed-assumption forecasts.\n\nWalk depth-first. Lock 1-3 before opening 4-7. After all 7 are answered, invoke `bookings_forecaster.py` → `cohort_arr_projector.py` → `funnel_confidence_scorer.py` in sequence.\n","# commercial-forecaster\n\n## Purpose\n\nHelp Commercial leaders answer three questions at the forecast moment:\n\n1. **What's the commit \u002F best-case \u002F pipe-only number?** (3-tier bookings forecast with disclosed assumptions)\n2. **Which cohorts are leaking, and is the consolidated NRR hiding the leak?** (per-cohort NRR\u002FGRR projection over horizon)\n3. **Which funnel stages are reliable, and which are statistical noise?** (per-stage coefficient-of-variation confidence band)\n\nThe skill recommends **three forecast numbers + an explicit assumption block**. The CRO presents the number, the board sees the assumptions, the theatre dies.\n\n## When to use\n\n- Building the quarterly bookings forecast for the board\n- Preparing the QBR forecast where the CFO will ask \"what's the commit, what's the best-case, what's the pipe-only\"\n- Projecting ARR for next 4-8 quarters using cohort retention data\n- Suspecting a consolidated NRR number is hiding a leaky recent cohort\n- Pipeline-coverage is shrinking and you need to know which stages are still trustworthy\n- You're being asked for a \"single number\" and you need the structured answer that surfaces the assumption\n\n**Do not use for:**\n- Backward-looking financial close + reporting → `finance\u002Ffinancial-analysis`\n- Strategic financial planning (multi-year, scenario, fundraise) → `c-level-advisor\u002Fcfo-advisor`\n- \"Should we hire a VP Sales?\" \u002F territory design \u002F comp plan → `c-level-advisor\u002Fcro-advisor`\n- Setting prices → sibling `pricing-strategist` (projects revenue *at* prices already set)\n- Per-deal discount approval → sibling `deal-desk`\n\n## Workflow\n\n### Step 1 — Intake pipeline + cohort + historical conversion data\n\nFill `assets\u002Fforecast_intake_template.md` (≈ 20 min). Captures: opportunity list with stage\u002Famount\u002Fclose-date\u002Fage\u002Flast-activity; historical stage-to-stage conversion across last 4Q and last 12Q; per-cohort ARR + per-quarter retention + expansion data; funnel stage names with 12-quarter conversion history.\n\n### Step 2 — Run 3-tier bookings forecast\n\n```\nscripts\u002Fbookings_forecaster.py --input intake.json --profile saas --output markdown\n```\n\nOutputs three numbers — **commit**, **best-case**, **pipe-only** — each with the conversion rate applied, the data window used (last-4Q vs. last-12Q weighted 70\u002F30), and the time-to-close probability adjustment. Surfaces variance between commit and pipe-only as the pipeline-risk indicator.\n\n**The assumption block is non-optional.** If you remove it, the forecast becomes theatre.\n\n### Step 3 — Project cohort-level ARR\n\n```\nscripts\u002Fcohort_arr_projector.py --input intake.json --output markdown\n```\n\nComputes per-cohort NRR + GRR over the projection horizon. Flags any cohort whose NRR is declining vs. the trailing-cohort average — these are the leaky cohorts that the consolidated number will hide for 2-3 quarters before the leak surfaces in the topline.\n\nOutput includes the consolidated NRR\u002FGRR trajectory + the cohort heatmap + a leaky-cohort callout.\n\n### Step 4 — Score per-stage funnel confidence\n\n```\nscripts\u002Ffunnel_confidence_scorer.py --input intake.json --output markdown\n```\n\nPer stage: mean conversion %, standard deviation, coefficient of variation (CoV = StDev \u002F Mean), confidence band (HIGH \u003C 10%, MEDIUM 10-25%, LOW 25-50%, VERY LOW > 50%). Recommends treatment per stage: extend-data-window, treat-as-soft-floor, or commit-quality.\n\n### Step 5 — Assemble the forecast deck\n\nTake the 3-tier bookings number + cohort heatmap + funnel confidence into the QBR \u002F board deck. **The assumption block goes on the slide with the number.** If the slide has a single number and no assumption block, the slide is theatre.\n\n## Scripts\n\n- `scripts\u002Fbookings_forecaster.py` — 3-tier bookings forecast (commit \u002F best-case \u002F pipe-only) with disclosed conversion-rate + data-window + weighting block\n- `scripts\u002Fcohort_arr_projector.py` — per-cohort NRR\u002FGRR projection over horizon with leaky-cohort callout\n- `scripts\u002Ffunnel_confidence_scorer.py` — per-stage CoV-based confidence bands with treatment recommendation\n\nAll scripts: stdlib only. `--help` and `--sample` work on all three.\n\n## References\n\n- `references\u002Fsaas_forecasting_canon.md` — Skok, Tunguz, OpenView, BVP, Pacific Crest\u002FKeyBanc, ProfitWell, Patrick Campbell\n- `references\u002Fcohort_analysis_canon.md` — Andrew Chen (a16z), Brian Balfour, Skok, Ramanujam, OpenView, Lenny Rachitsky, Reforge\n- `references\u002Fforecast_anti_patterns.md` — McKinsey, Tunguz, OpenView, MIT Sloan, Bain, Forrester, Pacific Crest\n\n## Assumptions\n\n- **Historical conversion is the prior, not the truth.** Last 4Q is weighted 70%, last 12Q is weighted 30%. The blend captures regime change (recent slowdown) without overfitting to a single bad quarter. Window + weighting are surfaced in every output.\n- **A forecast without a disclosed assumption block is theatre.** This is the skill's hard rule. The CLI refuses to omit the assumption block.\n- **Cohort decomposition reveals leaks 2-3 quarters before the consolidated number does.** Reporting NRR without per-cohort breakdown hides the leak.\n- **CoV (coefficient of variation) is the right discipline for stage confidence.** A stage with mean conversion 40% and stdev 4% (CoV 10%) is HIGH confidence; mean 40% stdev 20% (CoV 50%) is VERY LOW. The same average masks very different reliability.\n- **Industry profile tunes priors, not truth.** Profile shifts default stage-conversion rates by industry; your historical data overrides.\n- **The skill emits three numbers and an assumption block.** The CRO picks the commit number, owns the trade-off, and walks the board through the variance.\n\n## Anti-patterns\n\n- **Single-number forecast with no confidence band.** The board asks for \"the number\"; the discipline is to present three with named assumptions. See `forecast_anti_patterns.md`.\n- **Using last-12-quarter conversion blindly.** Hides recent slowdown. The 70\u002F30 blend on last-4Q vs. last-12Q corrects this.\n- **Reporting NRR without cohort decomposition.** The consolidated number can be flat while a recent cohort is leaking 15 pp; the leak surfaces in the topline 2-3 quarters later. Always decompose.\n- **Treating best-case as commit.** The CFO will eat you. Best-case includes weighted-stage opps that have a \u003C 50% time-to-close probability; commit only includes commit-grade stages.\n- **Hiding the assumption block.** The skill refuses; if you remove it manually, you own the theatre.\n- **No leaky-cohort callout.** If `cohort_arr_projector.py` flags a cohort and you suppress the flag in the deck, the leak owns you next quarter.\n- **Ignoring late-stage opp age.** A \"verbal\" deal that's been verbal for 180 days is not a commit. The bookings forecaster downweights stalled opps automatically; do not re-up them by hand.\n- **No pipeline-coverage check.** Industry rule of thumb: forecast > pipeline ÷ 3 is anti-pattern. The tool surfaces the ratio; respect it.\n\n## Distinct from\n\n- **`finance\u002Ffinancial-analysis`** — backward-looking financial close, GAAP\u002FIFRS reporting, variance vs. budget. commercial-forecaster is forward-looking pipeline math.\n- **`c-level-advisor\u002Fcfo-advisor`** — strategic multi-year financial planning, fundraise scenarios, runway. commercial-forecaster is one input to the CFO, not the strategy.\n- **`c-level-advisor\u002Fcro-advisor`** — strategic CRO judgment: \"do we hire a VP Sales?\", territory design, comp plan, when to add a sales engineer. commercial-forecaster is the math the CRO uses; cro-advisor is the judgment the CRO applies.\n- **sibling `pricing-strategist`** — sets the price (model + range). commercial-forecaster *projects revenue at those prices*. Pricing comes first; forecast comes after.\n- **sibling `deal-desk`** — per-deal scoring + discount approval routing. commercial-forecaster aggregates the pipeline that deal-desk operates on day-by-day.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time by `\u002Fcs:grill-commercial` or the orchestrator. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"What conversion rate are you using, and is it last-4Q or last-12Q?\"**\n   Recommended: a 70\u002F30 blend (last-4Q weighted 70%, last-12Q weighted 30%). Last-12Q alone hides recent slowdown; last-4Q alone overfits one bad quarter.\n   Canon: Tomasz Tunguz (Theory Ventures) — forecasting studies show single-window conversion estimates miss regime change at ~3-quarter lag.\n\n2. **\"What's your pipeline coverage ratio, and is your commit above pipeline ÷ 3?\"**\n   Recommended: 3x coverage is the SaaS-industry floor; below 3x means your commit is structurally unsupported.\n   Canon: Pacific Crest \u002F KeyBanc SaaS Survey — top-quartile SaaS companies maintain 3.0-4.5x pipeline coverage against committed bookings.\n\n3. **\"Can you show me NRR by cohort, not just consolidated?\"**\n   Recommended: never report a consolidated NRR without the per-cohort breakdown. Leaky cohorts hide in averages.\n   Canon: Patrick Campbell (ProfitWell) + David Skok — cohort-driven retention decomposition surfaces leaks 2-3 quarters before consolidated NRR moves.\n\n4. **\"What's the variance (CoV) on each stage's conversion rate over the last 12 quarters?\"**\n   Recommended: CoV \u003C 10% → commit-grade; 10-25% → moderate; 25-50% → soft floor only; > 50% → do not use this stage for forecasting.\n   Canon: MIT Sloan forecasting research \u002F Hyndman & Athanasopoulos (*Forecasting: Principles and Practice*) — CoV on the input series predicts forecast accuracy more reliably than mean.\n\n5. **\"How long has each late-stage opp been in late-stage?\"**\n   Recommended: stage-age > 2x the median stage-duration → treat as stalled, exclude from commit, keep in pipe-only.\n   Canon: David Skok (*For Entrepreneurs*) — stalled-opp identification by stage-age is the #1 forecast hygiene practice in top-decile SaaS pipelines.\n\n6. **\"Is your best-case forecast within 30% of your pipe-only?\"**\n   Recommended: if best-case is \u003C 50% of pipe-only, your stage-conversion assumptions are pessimistic and you're sandbagging; if best-case > 80% of pipe-only, you're hockey-sticking.\n   Canon: McKinsey research on forecast bias + OpenView SaaS benchmarks — most teams operate in one of two failure modes: sandbagging (commit \u003C\u003C earnings) or hockey-sticking (commit >> earnings).\n\n7. **\"What assumption block accompanies the number on the board slide?\"**\n   Recommended: every forecast number on a board slide names (a) the conversion rate, (b) the data window, (c) the weighting choice, (d) the pipeline-coverage ratio. No assumption block = the slide is theatre.\n   Canon: Bain & Company commercial-forecasting practice + Forrester pipeline-coverage research — undisclosed-assumption forecasts have 2.3x higher variance against actuals than disclosed-assumption forecasts.\n\nWalk depth-first. Lock 1-3 before opening 4-7. After all 7 are answered, invoke `bookings_forecaster.py` → `cohort_arr_projector.py` → `funnel_confidence_scorer.py` in sequence.\n",{"name":5,"tags":24,"author":34,"license":15,"version":35,"description":7,"compatible_tools":36},[25,26,27,28,29,30,31,32,33],"commercial","forecasting","bookings","arr","nrr","grr","cohort","funnel","pipeline-math","claude-code-skills","2.8.0",[37,38,39,40,41,42],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","835716bfbede9677a779d77bfd04befb81c069ac","skills\u002Fclaude__commercial-forecaster-2","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI","https:\u002F\u002Fgithub.com\u002Fharesh-sai06","https:\u002F\u002Fharesh-sainaath.live\u002F","I am a Student pursuing Artificial Intelligence and Machine Learning at Sri Krishna College of Technology,Coimbatore.","2026-06-13 22:42:28",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add haresh-sai06\u002FSapSynk-AI --skill commercial-forecaster",{"tool":57,"command":58},"bunx","bunx skills add haresh-sai06\u002FSapSynk-AI --skill commercial-forecaster",{"tool":60,"command":61},"pnpm","pnpm dlx skills add haresh-sai06\u002FSapSynk-AI --skill commercial-forecaster","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__commercial-forecaster-2\u002FSKILL.md",true,[],[66,73,80,87,94,101],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":72,"publishedAt":19},"01M77GQ5PRFQMV3QBDAQPCSXJB","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__youtube-full-2\u002FSKILL.md",[],[],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":19},"01M77GPXXAB3Y8ZNG919MD4703","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__x-twitter-growth-2\u002FSKILL.md",[],[],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":19},"01M77GPSS7AFYH90Y5S6XHRXZW","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__write-a-skill-2\u002FSKILL.md",[],[],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":19},"01M77GPKB6HJS2X5XK03649C6R","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__workflow-builder-2\u002FSKILL.md",[],[],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":19},"01M77GP31EFXE1KS38XBZ1J9MN","webinar-marketing","When the user wants to plan, promote, run, or improve a webinar or virtual event to generate and convert demand. Use when the user mentions 'webinar,' 'virtual event,' 'online event,' 'live demo,' 'virtual summit,' 'workshop,' 'masterclass,' 'fireside chat,' 'roundtable,' 'registration funnel,' 'show-up rate,' 'attendance rate,' 'webinar promotion,' 'webinar follow-up,' or 'on-demand webinar.' Also use when they have a webinar that isn't converting — low registrations, low show-up, or attendees who don't buy — and want to diagnose and fix it. Covers the full funnel: registration, promotion, show-up, live engagement, live-to-close, and post-event nurture. Distinct from launch-strategy (full product launches) and email-sequence (lifecycle nurture) — this is the end-to-end webinar\u002Fevent motion. NOT for in-person field events logistics, and NOT for generic lifecycle email (use email-sequence).","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__webinar-marketing-2\u002FSKILL.md",[],[],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":19},"01M77GNNA22HWXX6CEQQ3JY2YB","vpe-advisor","VP of Engineering advisory for startups: delivery throughput (DORA 4 metrics + bottleneck identification), engineering hiring funnel (sourcing → screen → onsite → offer conversion + time-to-fill + pipeline gap), engineering team structure (squad\u002Ftribe\u002Fchapter design + tech-lead manager-trigger thresholds), and production discipline (on-call, deployment cadence, postmortem culture). Use when sprint velocity is dropping, eng hiring is broken, team structure is unclear, or deciding when to add a tech-lead manager. NOT a CTO skill (which owns architecture) — VPE owns delivery operations and how the team ships.","https:\u002F\u002Fgithub.com\u002Fharesh-sai06\u002FSapSynk-AI\u002Fblob\u002Fmain\u002Fskills\u002Fclaude__vpe-advisor-2\u002FSKILL.md",[],[]]