[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M78HK52EKS3KB4SW9ATSQ2MX":3,"skill-same:01M78HK52EKS3KB4SW9ATSQ2MX":85},{"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":32,"commitSha":33,"skillDirectory":34,"forksCount":14,"repositoryHtmlUrl":35,"repositoryHomepageUrl":6,"ownerHtmlUrl":36,"ownerBlogUrl":6,"ownerDescription":37,"updatedAt":38,"install":39,"resources":52},"01M78HK52EKS3KB4SW9ATSQ2MX","vpe-advisor",null,"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.","aneeba-pixel\u002Fclaude-skills","aneeba-pixel","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F218186338?v=4","01M78GRXBTG70217SH410P543Y","Aneeba Saeed","01M78GRXCDZ6TA0F8H2CZMDGSS",0,"MIT","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: \"vpe-advisor\"\ndescription: \"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.\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: c-level\n  domain: vp-engineering-leadership\n  updated: 2026-05-13\n  python-tools: delivery_throughput_analyzer.py, eng_hiring_funnel_calculator.py, eng_team_structure_designer.py\n  frameworks: delivery-throughput, hiring-funnel, team-structure, production-discipline\n---\n\n# VP of Engineering Advisor\n\nStrategic engineering operations leadership for startup VPEs and founders without one. **Four decisions, no generic engineering survey:**\n\n1. **Are we delivering at the right throughput?** — DORA 4 metrics + bottleneck identification (where work waits)\n2. **How do we scale the eng hiring funnel?** — funnel math + pipeline gap + time-to-fill discipline\n3. **What's our team structure — and when do we add a tech-lead manager?** — squad\u002Ftribe\u002Fchapter design + manager-trigger\n4. **What's our production discipline?** — on-call rotation, deployment cadence, postmortem culture (reference-only)\n\nThis skill is **NOT a CTO skill**. CTO owns *what to build* (architecture, scaling cliffs, build-vs-buy). VPE owns *how to ship it reliably* (delivery, hiring, team structure, production operations). At early stage these are often the same person; at scale they're distinct roles.\n\nThis skill is **NOT a cs-engineering-lead replacement**. Engineering-lead owns day-to-day incident and on-call coordination. VPE owns the operating model that engineering-lead executes.\n\n## Keywords\n\nVPE, VP of Engineering, VP Engineering, engineering operations, delivery throughput, DORA, deployment frequency, lead time for changes, mean time to recovery, MTTR, change failure rate, cycle time, lead time, throughput, engineering hiring, eng hiring funnel, technical interview, take-home, pair programming, hiring pipeline, time-to-fill, cost-per-hire, ramp time, engineering team structure, squad, tribe, chapter, Spotify model, conway's law, tech lead, engineering manager, EM, span of control, hiring funnel conversion, eng comp, leveling, IC track, manager track, deployment cadence, on-call rotation, postmortem culture, blameless retro\n\n## Quick Start\n\n```bash\n# Decision A: DORA 4 metrics + bottleneck identification\npython scripts\u002Fdelivery_throughput_analyzer.py                          # embedded sprint sample\npython scripts\u002Fdelivery_throughput_analyzer.py path\u002Fto\u002Fsprint_metrics.json\n\n# Decision B: Hiring funnel health + pipeline gap\npython scripts\u002Feng_hiring_funnel_calculator.py                          # embedded 3-quarter sample\npython scripts\u002Feng_hiring_funnel_calculator.py path\u002Fto\u002Ffunnel.json\n\n# Decision C: Team structure recommendation + manager-trigger\npython scripts\u002Feng_team_structure_designer.py                           # embedded 25-engineer sample\npython scripts\u002Feng_team_structure_designer.py path\u002Fto\u002Fteam.json\n```\n\n## Key Questions (ask these first)\n\n- **What's your cycle time, and where does the work spend most of its time waiting?** (If you don't know, you can't improve it.)\n- **How long from commit to production?** (DORA \"lead time for changes\" — best predictor of overall team health.)\n- **What's the escape rate?** (Bugs found in production vs caught in CI\u002Fstaging. > 15% = quality discipline broken.)\n- **When did the eng manager last write code?** (Manager-IC ratio is wrong if managers can't review code at all.)\n- **What's the hiring funnel conversion at each stage?** (Source → screen → onsite → offer → accept. The leakage is the answer.)\n- **What's the on-call rotation, and who's on it?** (If the same 3 people are always paged, the operating model is broken.)\n\n## Core Responsibilities\n\n### 1. Delivery Throughput (DORA Metrics)\n\n**The framework:** Google DORA's 4 key metrics (from \"Accelerate\", Forsgren\u002FHumble\u002FKim 2018).\n\n| Metric | What it measures | Elite | High | Medium | Low |\n|---|---|---|---|---|---|\n| **Deployment Frequency** | How often code reaches prod | Multiple\u002Fday | Daily-weekly | Weekly-monthly | \u003C monthly |\n| **Lead Time for Changes** | Commit → production | \u003C 1 hour | 1 day-1 week | 1 week-1 month | > 1 month |\n| **Mean Time to Recovery (MTTR)** | Incident detection → resolved | \u003C 1 hour | \u003C 1 day | 1-7 days | > 7 days |\n| **Change Failure Rate** | % of deploys causing incidents | 0-15% | 16-30% | 16-45% | 46-60% |\n\n**Bottleneck identification — where does work wait?**\n\nCycle time = (PR creation → first review) + (review → approval) + (approval → merge) + (merge → deploy). The longest segment is the bottleneck.\n\nCommon bottlenecks:\n- **PR review queue** (waiting for human reviewers) — fix: reviewer rotation + SLA\n- **Test flakiness** (CI fails intermittently, re-runs needed) — fix: flaky-test budget + quarantine\n- **Deploy gates** (manual approval, change-control board) — fix: progressive delivery + feature flags\n- **Database migrations** (locking, scheduled windows) — fix: zero-downtime migration patterns\n\n**Run** `delivery_throughput_analyzer.py` with sprint data to get DORA verdict + top bottleneck.\n\nSee `references\u002Fdelivery_throughput.md` for the full DORA framework, anti-patterns, and what to fix first.\n\n### 2. Engineering Hiring Funnel\n\n**The trap:** \"We can't find good engineers.\"\n\nThe reality: the funnel has 4-6 stages, each with a conversion rate. Find which stage is leakiest; fix that one. \"Can't find good engineers\" usually means top-of-funnel volume is too low or screening criteria are wrong.\n\n**Standard funnel stages:**\n\n| Stage | Healthy conversion | What it measures |\n|---|---|---|\n| Applied → Sourcer screen | 30-50% | Resume quality |\n| Sourcer → Recruiter screen | 50-70% | Basic fit |\n| Recruiter → Hiring manager | 60-80% | Team fit |\n| Hiring manager → Technical interview | 70-85% | Technical baseline |\n| Technical → Onsite (full loop) | 30-50% | Technical depth |\n| Onsite → Offer | 25-40% | Final go\u002Fno-go |\n| Offer → Accept | 70-90% | Comp + close discipline |\n\n**Funnel math:** to hire N engineers, you need N \u002F (product of all conversion rates) candidates at top of funnel.\n\nExample: 4 hires needed × 100 candidates per stage (assuming 30% × 60% × 70% × 75% × 40% × 35% × 80% = ~0.7% end-to-end) = ~570 candidates at top of funnel.\n\n**Run** `eng_hiring_funnel_calculator.py` with funnel data to compute conversion per stage, time-to-fill, and pipeline gap.\n\nSee `references\u002Fengineering_hiring_funnel.md` for the full funnel framework, common leakage points, and sourcing channel diversification.\n\n### 3. Engineering Team Structure\n\n**The right question:** \"How do we organize people so they can ship without coordination overhead?\"\n\n**Three-axis model (adapted from Spotify, refined by reality):**\n\n- **Squad:** small autonomous team (5-9 engineers) owning a service or product area end-to-end\n- **Chapter:** functional discipline cutting across squads (backend chapter, frontend chapter, etc.) — for skill development, NOT for ownership\n- **Tribe:** group of related squads working toward a shared goal (e.g., \"platform tribe\" = 3 squads on infra)\n\n**When to evolve:**\n\n| Stage | Structure |\n|---|---|\n| 1-5 engineers | One team. No structure. |\n| 6-15 engineers | 2-3 informal pods around major work streams. Founder-CTO can still know everyone. |\n| 16-40 engineers | 4-6 squads. First eng manager hires. Chapter structure emerges for cross-squad skill alignment. |\n| 41-100 engineers | 2-3 tribes (clusters of squads). Director of engineering layer. Chapters are formal. |\n| 100+ engineers | Multiple tribes + group EM\u002Fdirector per tribe. VPE + director(s) + EMs + tech leads. |\n\n**Manager-trigger thresholds:**\n- 5-7 ICs without a manager = first EM hire (or internal promote)\n- 3+ EMs without a director = director hire\n- 8+ teams in one tribe = split the tribe\n\n**Run** `eng_team_structure_designer.py` with team profile for structure recommendation + manager-trigger.\n\nSee `references\u002Feng_team_structure.md` for the full framework, Conway's Law implications, and EM-vs-tech-lead split.\n\n### 4. Production Discipline\n\nProduction discipline is the operating model that lets the team sleep. Four pillars:\n\n- **On-call rotation:** broad enough to avoid burnout (≥ 6 people per rotation; primary + secondary)\n- **Incident response:** runbooks, severity definitions, blameless postmortems\n- **Deployment cadence:** continuous deployment OR scheduled releases; both work; surprise releases don't\n- **SLO discipline:** every customer-facing service has documented SLOs + error budgets (pair with `engineering\u002Fslo-architect\u002F`)\n\nSee `references\u002Fproduction_discipline.md` for the full operating model.\n\n## Workflows\n\n### Workflow 1: Quarterly Delivery Health Review (4 hours)\n**Goal:** Diagnose throughput + identify top bottleneck.\n\n```bash\n# 1. Pull sprint metrics: deployment frequency, lead time, MTTR, change failure rate\npython ..\u002F..\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Fdelivery_throughput_analyzer.py sprint_metrics.json\n# 2. Review DORA verdict per metric\n# 3. Identify top bottleneck (longest wait stage)\n# 4. Cross-check with cs-cto-advisor on architectural causes\n# 5. Output: 90-day fix plan with one bottleneck owned by one engineer\n# 6. Log via \u002Fcs:decide\n```\n\n### Workflow 2: Hiring Funnel Diagnosis (1 day)\n**Goal:** Identify funnel leakage + compute pipeline gap for hiring target.\n\n```bash\n# 1. Pull funnel data from ATS for last 90 days\npython ..\u002F..\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Feng_hiring_funnel_calculator.py funnel.json\n# 2. Identify weakest conversion stage\n# 3. Compute pipeline volume needed for next quarter's hiring target\n# 4. Cross-check with cs-chro-advisor on comp\u002Fleveling competitiveness\n# 5. Cross-check with cs-cfo-advisor on cost-per-hire envelope\n# 6. Output: top-3 fixes + sourcing channel diversification plan\n```\n\n### Workflow 3: Team Structure Audit (1 day)\n**Goal:** Confirm team structure matches headcount + work streams.\n\n```bash\n# 1. Build team.json: headcount, work streams, manager count, IC distribution\npython ..\u002F..\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Feng_team_structure_designer.py team.json\n# 2. Check manager-trigger thresholds (5-7 IC rule)\n# 3. Identify squad sizes outside 5-9 range\n# 4. Cross-check with cs-cto-advisor on Conway's Law alignment\n# 5. Output: structure recommendations + manager hire plan\n```\n\n### Workflow 4: Production Discipline Audit (1 week)\n**Goal:** Confirm operating model can scale through current growth.\n\n1. Inventory: on-call coverage, incident frequency by severity, MTTR trend\n2. Confirm every customer-facing service has SLOs (pair with `engineering\u002Fslo-architect\u002F`)\n3. Review last 5 postmortems — are they blameless? Are action items closed?\n4. Cross-check deployment cadence against DORA verdict\n5. Output: production-discipline maturity score + 90-day improvement plan\n\n## Output Standards\n\n```\n**Bottom Line:** [one sentence — decision and rationale]\n**The Decision:** [one of: throughput | hiring | structure | production]\n**The Evidence:** [numbers from the tool, not adjectives]\n**How to Act:** [3 concrete next steps]\n**Your Decision:** [the call only the founder\u002FCTO can make]\n```\n\n## Adjacent Skills\n\n- `..\u002Fcto-advisor\u002F` — Architecture, scaling cliffs, tech debt strategy (CTO decides what to build; VPE decides how to ship)\n- `..\u002Fchro-advisor\u002F` — Hiring systems (ladders, bands, leveling rubrics company-wide); VPE owns eng-specific funnel execution\n- `..\u002Fcoo-advisor\u002F` — Operating cadence company-wide; VPE owns eng-specific cadence\n- `..\u002F..\u002F..\u002Fengineering\u002Fslo-architect\u002F` — SLO design (tactical; VPE owns the policy that SLOs are required)\n- `..\u002F..\u002F..\u002Fengineering\u002Fchaos-engineering\u002F` — Chaos experiment design (tactical resilience)\n- `..\u002F..\u002F..\u002Fengineering\u002Ffeature-flags-architect\u002F` — Progressive delivery (tactical deployment)\n- `..\u002F..\u002F..\u002Fengineering\u002Fkubernetes-operator\u002F` — K8s operator pattern (tactical infra)\n- `cs-engineering-lead` agent — Day-to-day incident + on-call coordination (VPE owns the operating model that engineering-lead executes)\n\n## References\n\n- [delivery_throughput.md](references\u002Fdelivery_throughput.md) — Full DORA framework + 4 common bottlenecks + what to fix first + anti-patterns\n- [engineering_hiring_funnel.md](references\u002Fengineering_hiring_funnel.md) — 7-stage funnel + conversion benchmarks + common leakage + sourcing channel diversification + technical interview design\n- [eng_team_structure.md](references\u002Feng_team_structure.md) — Squad\u002Fchapter\u002Ftribe model + headcount-to-structure map + Conway's Law + EM-vs-tech-lead split + span-of-control\n- [production_discipline.md](references\u002Fproduction_discipline.md) — On-call rotation design + incident response + blameless postmortem culture + deployment cadence + SLO discipline integration\n\n---\n\n**Version:** 1.0.0\n**Status:** Production Ready\n","# VP of Engineering Advisor\n\nStrategic engineering operations leadership for startup VPEs and founders without one. **Four decisions, no generic engineering survey:**\n\n1. **Are we delivering at the right throughput?** — DORA 4 metrics + bottleneck identification (where work waits)\n2. **How do we scale the eng hiring funnel?** — funnel math + pipeline gap + time-to-fill discipline\n3. **What's our team structure — and when do we add a tech-lead manager?** — squad\u002Ftribe\u002Fchapter design + manager-trigger\n4. **What's our production discipline?** — on-call rotation, deployment cadence, postmortem culture (reference-only)\n\nThis skill is **NOT a CTO skill**. CTO owns *what to build* (architecture, scaling cliffs, build-vs-buy). VPE owns *how to ship it reliably* (delivery, hiring, team structure, production operations). At early stage these are often the same person; at scale they're distinct roles.\n\nThis skill is **NOT a cs-engineering-lead replacement**. Engineering-lead owns day-to-day incident and on-call coordination. VPE owns the operating model that engineering-lead executes.\n\n## Keywords\n\nVPE, VP of Engineering, VP Engineering, engineering operations, delivery throughput, DORA, deployment frequency, lead time for changes, mean time to recovery, MTTR, change failure rate, cycle time, lead time, throughput, engineering hiring, eng hiring funnel, technical interview, take-home, pair programming, hiring pipeline, time-to-fill, cost-per-hire, ramp time, engineering team structure, squad, tribe, chapter, Spotify model, conway's law, tech lead, engineering manager, EM, span of control, hiring funnel conversion, eng comp, leveling, IC track, manager track, deployment cadence, on-call rotation, postmortem culture, blameless retro\n\n## Quick Start\n\n```bash\n# Decision A: DORA 4 metrics + bottleneck identification\npython scripts\u002Fdelivery_throughput_analyzer.py                          # embedded sprint sample\npython scripts\u002Fdelivery_throughput_analyzer.py path\u002Fto\u002Fsprint_metrics.json\n\n# Decision B: Hiring funnel health + pipeline gap\npython scripts\u002Feng_hiring_funnel_calculator.py                          # embedded 3-quarter sample\npython scripts\u002Feng_hiring_funnel_calculator.py path\u002Fto\u002Ffunnel.json\n\n# Decision C: Team structure recommendation + manager-trigger\npython scripts\u002Feng_team_structure_designer.py                           # embedded 25-engineer sample\npython scripts\u002Feng_team_structure_designer.py path\u002Fto\u002Fteam.json\n```\n\n## Key Questions (ask these first)\n\n- **What's your cycle time, and where does the work spend most of its time waiting?** (If you don't know, you can't improve it.)\n- **How long from commit to production?** (DORA \"lead time for changes\" — best predictor of overall team health.)\n- **What's the escape rate?** (Bugs found in production vs caught in CI\u002Fstaging. > 15% = quality discipline broken.)\n- **When did the eng manager last write code?** (Manager-IC ratio is wrong if managers can't review code at all.)\n- **What's the hiring funnel conversion at each stage?** (Source → screen → onsite → offer → accept. The leakage is the answer.)\n- **What's the on-call rotation, and who's on it?** (If the same 3 people are always paged, the operating model is broken.)\n\n## Core Responsibilities\n\n### 1. Delivery Throughput (DORA Metrics)\n\n**The framework:** Google DORA's 4 key metrics (from \"Accelerate\", Forsgren\u002FHumble\u002FKim 2018).\n\n| Metric | What it measures | Elite | High | Medium | Low |\n|---|---|---|---|---|---|\n| **Deployment Frequency** | How often code reaches prod | Multiple\u002Fday | Daily-weekly | Weekly-monthly | \u003C monthly |\n| **Lead Time for Changes** | Commit → production | \u003C 1 hour | 1 day-1 week | 1 week-1 month | > 1 month |\n| **Mean Time to Recovery (MTTR)** | Incident detection → resolved | \u003C 1 hour | \u003C 1 day | 1-7 days | > 7 days |\n| **Change Failure Rate** | % of deploys causing incidents | 0-15% | 16-30% | 16-45% | 46-60% |\n\n**Bottleneck identification — where does work wait?**\n\nCycle time = (PR creation → first review) + (review → approval) + (approval → merge) + (merge → deploy). The longest segment is the bottleneck.\n\nCommon bottlenecks:\n- **PR review queue** (waiting for human reviewers) — fix: reviewer rotation + SLA\n- **Test flakiness** (CI fails intermittently, re-runs needed) — fix: flaky-test budget + quarantine\n- **Deploy gates** (manual approval, change-control board) — fix: progressive delivery + feature flags\n- **Database migrations** (locking, scheduled windows) — fix: zero-downtime migration patterns\n\n**Run** `delivery_throughput_analyzer.py` with sprint data to get DORA verdict + top bottleneck.\n\nSee `references\u002Fdelivery_throughput.md` for the full DORA framework, anti-patterns, and what to fix first.\n\n### 2. Engineering Hiring Funnel\n\n**The trap:** \"We can't find good engineers.\"\n\nThe reality: the funnel has 4-6 stages, each with a conversion rate. Find which stage is leakiest; fix that one. \"Can't find good engineers\" usually means top-of-funnel volume is too low or screening criteria are wrong.\n\n**Standard funnel stages:**\n\n| Stage | Healthy conversion | What it measures |\n|---|---|---|\n| Applied → Sourcer screen | 30-50% | Resume quality |\n| Sourcer → Recruiter screen | 50-70% | Basic fit |\n| Recruiter → Hiring manager | 60-80% | Team fit |\n| Hiring manager → Technical interview | 70-85% | Technical baseline |\n| Technical → Onsite (full loop) | 30-50% | Technical depth |\n| Onsite → Offer | 25-40% | Final go\u002Fno-go |\n| Offer → Accept | 70-90% | Comp + close discipline |\n\n**Funnel math:** to hire N engineers, you need N \u002F (product of all conversion rates) candidates at top of funnel.\n\nExample: 4 hires needed × 100 candidates per stage (assuming 30% × 60% × 70% × 75% × 40% × 35% × 80% = ~0.7% end-to-end) = ~570 candidates at top of funnel.\n\n**Run** `eng_hiring_funnel_calculator.py` with funnel data to compute conversion per stage, time-to-fill, and pipeline gap.\n\nSee `references\u002Fengineering_hiring_funnel.md` for the full funnel framework, common leakage points, and sourcing channel diversification.\n\n### 3. Engineering Team Structure\n\n**The right question:** \"How do we organize people so they can ship without coordination overhead?\"\n\n**Three-axis model (adapted from Spotify, refined by reality):**\n\n- **Squad:** small autonomous team (5-9 engineers) owning a service or product area end-to-end\n- **Chapter:** functional discipline cutting across squads (backend chapter, frontend chapter, etc.) — for skill development, NOT for ownership\n- **Tribe:** group of related squads working toward a shared goal (e.g., \"platform tribe\" = 3 squads on infra)\n\n**When to evolve:**\n\n| Stage | Structure |\n|---|---|\n| 1-5 engineers | One team. No structure. |\n| 6-15 engineers | 2-3 informal pods around major work streams. Founder-CTO can still know everyone. |\n| 16-40 engineers | 4-6 squads. First eng manager hires. Chapter structure emerges for cross-squad skill alignment. |\n| 41-100 engineers | 2-3 tribes (clusters of squads). Director of engineering layer. Chapters are formal. |\n| 100+ engineers | Multiple tribes + group EM\u002Fdirector per tribe. VPE + director(s) + EMs + tech leads. |\n\n**Manager-trigger thresholds:**\n- 5-7 ICs without a manager = first EM hire (or internal promote)\n- 3+ EMs without a director = director hire\n- 8+ teams in one tribe = split the tribe\n\n**Run** `eng_team_structure_designer.py` with team profile for structure recommendation + manager-trigger.\n\nSee `references\u002Feng_team_structure.md` for the full framework, Conway's Law implications, and EM-vs-tech-lead split.\n\n### 4. Production Discipline\n\nProduction discipline is the operating model that lets the team sleep. Four pillars:\n\n- **On-call rotation:** broad enough to avoid burnout (≥ 6 people per rotation; primary + secondary)\n- **Incident response:** runbooks, severity definitions, blameless postmortems\n- **Deployment cadence:** continuous deployment OR scheduled releases; both work; surprise releases don't\n- **SLO discipline:** every customer-facing service has documented SLOs + error budgets (pair with `engineering\u002Fslo-architect\u002F`)\n\nSee `references\u002Fproduction_discipline.md` for the full operating model.\n\n## Workflows\n\n### Workflow 1: Quarterly Delivery Health Review (4 hours)\n**Goal:** Diagnose throughput + identify top bottleneck.\n\n```bash\n# 1. Pull sprint metrics: deployment frequency, lead time, MTTR, change failure rate\npython ..\u002F..\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Fdelivery_throughput_analyzer.py sprint_metrics.json\n# 2. Review DORA verdict per metric\n# 3. Identify top bottleneck (longest wait stage)\n# 4. Cross-check with cs-cto-advisor on architectural causes\n# 5. Output: 90-day fix plan with one bottleneck owned by one engineer\n# 6. Log via \u002Fcs:decide\n```\n\n### Workflow 2: Hiring Funnel Diagnosis (1 day)\n**Goal:** Identify funnel leakage + compute pipeline gap for hiring target.\n\n```bash\n# 1. Pull funnel data from ATS for last 90 days\npython ..\u002F..\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Feng_hiring_funnel_calculator.py funnel.json\n# 2. Identify weakest conversion stage\n# 3. Compute pipeline volume needed for next quarter's hiring target\n# 4. Cross-check with cs-chro-advisor on comp\u002Fleveling competitiveness\n# 5. Cross-check with cs-cfo-advisor on cost-per-hire envelope\n# 6. Output: top-3 fixes + sourcing channel diversification plan\n```\n\n### Workflow 3: Team Structure Audit (1 day)\n**Goal:** Confirm team structure matches headcount + work streams.\n\n```bash\n# 1. Build team.json: headcount, work streams, manager count, IC distribution\npython ..\u002F..\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Feng_team_structure_designer.py team.json\n# 2. Check manager-trigger thresholds (5-7 IC rule)\n# 3. Identify squad sizes outside 5-9 range\n# 4. Cross-check with cs-cto-advisor on Conway's Law alignment\n# 5. Output: structure recommendations + manager hire plan\n```\n\n### Workflow 4: Production Discipline Audit (1 week)\n**Goal:** Confirm operating model can scale through current growth.\n\n1. Inventory: on-call coverage, incident frequency by severity, MTTR trend\n2. Confirm every customer-facing service has SLOs (pair with `engineering\u002Fslo-architect\u002F`)\n3. Review last 5 postmortems — are they blameless? Are action items closed?\n4. Cross-check deployment cadence against DORA verdict\n5. Output: production-discipline maturity score + 90-day improvement plan\n\n## Output Standards\n\n```\n**Bottom Line:** [one sentence — decision and rationale]\n**The Decision:** [one of: throughput | hiring | structure | production]\n**The Evidence:** [numbers from the tool, not adjectives]\n**How to Act:** [3 concrete next steps]\n**Your Decision:** [the call only the founder\u002FCTO can make]\n```\n\n## Adjacent Skills\n\n- `..\u002Fcto-advisor\u002F` — Architecture, scaling cliffs, tech debt strategy (CTO decides what to build; VPE decides how to ship)\n- `..\u002Fchro-advisor\u002F` — Hiring systems (ladders, bands, leveling rubrics company-wide); VPE owns eng-specific funnel execution\n- `..\u002Fcoo-advisor\u002F` — Operating cadence company-wide; VPE owns eng-specific cadence\n- `..\u002F..\u002F..\u002Fengineering\u002Fslo-architect\u002F` — SLO design (tactical; VPE owns the policy that SLOs are required)\n- `..\u002F..\u002F..\u002Fengineering\u002Fchaos-engineering\u002F` — Chaos experiment design (tactical resilience)\n- `..\u002F..\u002F..\u002Fengineering\u002Ffeature-flags-architect\u002F` — Progressive delivery (tactical deployment)\n- `..\u002F..\u002F..\u002Fengineering\u002Fkubernetes-operator\u002F` — K8s operator pattern (tactical infra)\n- `cs-engineering-lead` agent — Day-to-day incident + on-call coordination (VPE owns the operating model that engineering-lead executes)\n\n## References\n\n- [delivery_throughput.md](references\u002Fdelivery_throughput.md) — Full DORA framework + 4 common bottlenecks + what to fix first + anti-patterns\n- [engineering_hiring_funnel.md](references\u002Fengineering_hiring_funnel.md) — 7-stage funnel + conversion benchmarks + common leakage + sourcing channel diversification + technical interview design\n- [eng_team_structure.md](references\u002Feng_team_structure.md) — Squad\u002Fchapter\u002Ftribe model + headcount-to-structure map + Conway's Law + EM-vs-tech-lead split + span-of-control\n- [production_discipline.md](references\u002Fproduction_discipline.md) — On-call rotation design + incident response + blameless postmortem culture + deployment cadence + SLO discipline integration\n\n---\n\n**Version:** 1.0.0\n**Status:** Production Ready\n",{"name":5,"license":15,"metadata":24,"description":7},{"author":25,"domain":26,"updated":27,"version":28,"category":29,"frameworks":30,"python-tools":31},"Alireza Rezvani","vp-engineering-leadership",1778630400000,"1.0.0","c-level","delivery-throughput, hiring-funnel, team-structure, production-discipline","delivery_throughput_analyzer.py, eng_hiring_funnel_calculator.py, eng_team_structure_designer.py","claude\u002Fopen-prs-review-Of9HK","a48d8438efe8e36550acc4c2006bdcddf62911db","c-level-advisor\u002Fskills\u002Fvpe-advisor","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills","https:\u002F\u002Fgithub.com\u002Faneeba-pixel","Student at University of Gujrat","2026-07-14 21:41:17",{"source":8,"skillName":5,"commands":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add aneeba-pixel\u002Fclaude-skills --skill vpe-advisor",{"tool":45,"command":46},"bunx","bunx skills add aneeba-pixel\u002Fclaude-skills --skill vpe-advisor",{"tool":48,"command":49},"pnpm","pnpm dlx skills add aneeba-pixel\u002Fclaude-skills --skill vpe-advisor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002FSKILL.md",true,[53,59,63,67,71,77,81],{"path":54,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":57,"sourceHtmlUrl":58},"references\u002Fdelivery_throughput.md","REFERENCE","参考文档",7327,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002Freferences\u002Fdelivery_throughput.md",{"path":60,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":61,"sourceHtmlUrl":62},"references\u002Feng_team_structure.md",7950,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002Freferences\u002Feng_team_structure.md",{"path":64,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":65,"sourceHtmlUrl":66},"references\u002Fengineering_hiring_funnel.md",8312,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002Freferences\u002Fengineering_hiring_funnel.md",{"path":68,"resourceType":55,"resourceTypeDesc":56,"sizeBytes":69,"sourceHtmlUrl":70},"references\u002Fproduction_discipline.md",8002,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002Freferences\u002Fproduction_discipline.md",{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"scripts\u002Fdelivery_throughput_analyzer.py","SCRIPT","脚本",10052,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Fdelivery_throughput_analyzer.py",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"scripts\u002Feng_hiring_funnel_calculator.py",11049,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Feng_hiring_funnel_calculator.py",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":83,"sourceHtmlUrl":84},"scripts\u002Feng_team_structure_designer.py",11271,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fc-level-advisor\u002Fskills\u002Fvpe-advisor\u002Fscripts\u002Feng_team_structure_designer.py",[86,93,100,107,114,121],{"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":19},"01M78K120A3JKH3NH8GC19YG4M","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Triggers whenever a user uploads a syllabus, course outline, or curriculum document and wants supplementary readings. Also triggers on: 'syllabus reading list', 'find papers for my course', 'create a reading list from this syllabus', 'recent research for my class', 'supplementary readings', 'find journal articles for these topics', 'what recent papers cover this material', 'any new research on these course topics', 'update my syllabus with recent papers'. Even casual mentions when a syllabus is attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[],[],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":19},"01M78K0QS9AGDJ43CPQYEHM5S0","research","Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends\u002Fsentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Triggers — \"research [topic]\", \"look into [topic]\", \"what do we know about [topic]\", \"investigate [topic]\", \"find me information on [topic]\", \"do some research on [topic]\", \"I need to understand [topic]\", or any research request that doesn't obviously match a more-specific specialist skill. Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fresearch\u002Fskills\u002Fresearch\u002FSKILL.md",[],[],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":19},"01M78K0FV3HMJ94SK965CWMNNP","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[],[],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":19},"01M78K04E77B04KYBB6M01EV3J","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Triggers: 'prior art search for [invention]', 'patent search on [topic]', 'freedom to operate analysis', 'FTO for [product]', 'patent landscape for [field]', 'is [invention] novel', 'patents on [topic]', 'competitive patent analysis', 'prior art for litigation', 'patent diligence on [company]'. Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[],[],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":19},"01M78JZW8XEFZP6HSF53M8D4M1","notebooklm","Browser automation skill for controlling Google's NotebookLM. Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio Overview, infographics, slide decks, study guides, briefing docs, mind maps, timelines, FAQs), and creating new notebooks. Triggers on any phrase involving NotebookLM — 'open NotebookLM', 'check my [name] notebook', 'pull info from NotebookLM', 'ask my notebook about X', 'add [source] to NotebookLM', 'create an infographic in NotebookLM', 'use NotebookLM Studio', 'generate a slide deck from my notebook', or any variation where the goal involves NotebookLM. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":19},"01M78JZHX45S2Y0GPY9Z5V6TQ3","litreview","Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[],[]]