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/tribe/chapter 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.
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SKILL.md
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name
vpe-advisor
description
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/tribe/chapter 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.
compatibility
opencode
VP of Engineering Advisor
Strategic engineering operations leadership for startup VPEs and founders without one. Four decisions, no generic engineering survey:
Are we delivering at the right throughput? — DORA 4 metrics + bottleneck identification (where work waits)
How do we scale the eng hiring funnel? — funnel math + pipeline gap + time-to-fill discipline
What's our team structure — and when do we add a tech-lead manager? — squad/tribe/chapter design + manager-trigger
This 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.
This 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.
Keywords
VPE, 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
Rundelivery_throughput_analyzer.py with sprint data to get DORA verdict + top bottleneck.
See references/delivery_throughput.md for the full DORA framework, anti-patterns, and what to fix first.
2. Engineering Hiring Funnel
The trap: "We can't find good engineers."
The 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.
Standard funnel stages:
Stage
Healthy conversion
What it measures
Applied → Sourcer screen
30-50%
Resume quality
Sourcer → Recruiter screen
50-70%
Basic fit
Recruiter → Hiring manager
60-80%
Team fit
Hiring manager → Technical interview
70-85%
Technical baseline
Technical → Onsite (full loop)
30-50%
Technical depth
Onsite → Offer
25-40%
Final go/no-go
Offer → Accept
70-90%
Comp + close discipline
Funnel math: to hire N engineers, you need N / (product of all conversion rates) candidates at top of funnel.
Example: 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.
Runeng_hiring_funnel_calculator.py with funnel data to compute conversion per stage, time-to-fill, and pipeline gap.
See references/engineering_hiring_funnel.md for the full funnel framework, common leakage points, and sourcing channel diversification.
3. Engineering Team Structure
The right question: "How do we organize people so they can ship without coordination overhead?"
Three-axis model (adapted from Spotify, refined by reality):
Squad: small autonomous team (5-9 engineers) owning a service or product area end-to-end
Chapter: functional discipline cutting across squads (backend chapter, frontend chapter, etc.) — for skill development, NOT for ownership
Tribe: group of related squads working toward a shared goal (e.g., "platform tribe" = 3 squads on infra)
When to evolve:
Stage
Structure
1-5 engineers
One team. No structure.
6-15 engineers
2-3 informal pods around major work streams. Founder-CTO can still know everyone.
16-40 engineers
4-6 squads. First eng manager hires. Chapter structure emerges for cross-squad skill alignment.
41-100 engineers
2-3 tribes (clusters of squads). Director of engineering layer. Chapters are formal.
100+ engineers
Multiple tribes + group EM/director per tribe. VPE + director(s) + EMs + tech leads.
Manager-trigger thresholds:
5-7 ICs without a manager = first EM hire (or internal promote)
3+ EMs without a director = director hire
8+ teams in one tribe = split the tribe
Runeng_team_structure_designer.py with team profile for structure recommendation + manager-trigger.
See references/eng_team_structure.md for the full framework, Conway's Law implications, and EM-vs-tech-lead split.
4. Production Discipline
Production discipline is the operating model that lets the team sleep. Four pillars:
On-call rotation: broad enough to avoid burnout (≥ 6 people per rotation; primary + secondary)
Deployment cadence: continuous deployment OR scheduled releases; both work; surprise releases don't
SLO discipline: every customer-facing service has documented SLOs + error budgets (pair with engineering/slo-architect/)
See references/production_discipline.md for the full operating model.
Workflows
Workflow 1: Quarterly Delivery Health Review (4 hours)
Goal: Diagnose throughput + identify top bottleneck.
# 1. Pull sprint metrics: deployment frequency, lead time, MTTR, change failure rate
python ../../skills/vpe-advisor/scripts/delivery_throughput_analyzer.py sprint_metrics.json
# 2. Review DORA verdict per metric# 3. Identify top bottleneck (longest wait stage)# 4. Cross-check with cs-cto-advisor on architectural causes# 5. Output: 90-day fix plan with one bottleneck owned by one engineer# 6. Log via /cs:decide
Workflow 2: Hiring Funnel Diagnosis (1 day)
Goal: Identify funnel leakage + compute pipeline gap for hiring target.
# 1. Pull funnel data from ATS for last 90 days
python ../../skills/vpe-advisor/scripts/eng_hiring_funnel_calculator.py funnel.json
# 2. Identify weakest conversion stage# 3. Compute pipeline volume needed for next quarter's hiring target# 4. Cross-check with cs-chro-advisor on comp/leveling competitiveness# 5. Cross-check with cs-cfo-advisor on cost-per-hire envelope# 6. Output: top-3 fixes + sourcing channel diversification plan
Workflow 3: Team Structure Audit (1 day)
Goal: Confirm team structure matches headcount + work streams.
# 1. Build team.json: headcount, work streams, manager count, IC distribution
python ../../skills/vpe-advisor/scripts/eng_team_structure_designer.py team.json
# 2. Check manager-trigger thresholds (5-7 IC rule)# 3. Identify squad sizes outside 5-9 range# 4. Cross-check with cs-cto-advisor on Conway's Law alignment# 5. Output: structure recommendations + manager hire plan
Workflow 4: Production Discipline Audit (1 week)
Goal: Confirm operating model can scale through current growth.
Inventory: on-call coverage, incident frequency by severity, MTTR trend
Confirm every customer-facing service has SLOs (pair with engineering/slo-architect/)
Review last 5 postmortems — are they blameless? Are action items closed?
Cross-check deployment cadence against DORA verdict
Output: production-discipline maturity score + 90-day improvement plan
Output Standards
**Bottom Line:** [one sentence — decision and rationale]
**The Decision:** [one of: throughput | hiring | structure | production]
**The Evidence:** [numbers from the tool, not adjectives]
**How to Act:** [3 concrete next steps]
**Your Decision:** [the call only the founder/CTO can make]
Adjacent Skills
../cto-advisor/ — Architecture, scaling cliffs, tech debt strategy (CTO decides what to build; VPE decides how to ship)
