| name | cofounder-matching |
| version | 0.3.0 |
| description | Rubric-based co-founder matching methodology. Scores candidate co-founders against a founder's startup profile using a 6-axis framework (domain fit, skill complementarity, values alignment, equity expectations, time commitment, track record). Produces a ranked shortlist with gap analysis + interview questions per candidate. Works standalone (manual candidate input) — can consume DojoOS candidate pool via dojoos-api-consumer agent when available. Use when the user asks "co-founder matching", "find co-founder", "evaluate candidate", "co-founder fit", "co-founder scoring", "/cofounder-matching". NOT legal or HR advice.
|
Co-Founder Matching
Evalúa candidatos a co-founder contra un startup profile usando una rubric de 6 ejes. Produce un ranked shortlist + gap analysis + interview questions customizadas por candidato.
⚠️ Disclaimer
Co-founder matching es decisión high-stakes:
- Co-founder equity grants son típicamente 20-50% — irreversible sin término (o clawback tras vesting)
- Co-founder disputes son la razón #1 de failure en early stage startups
- Este skill genera marco de evaluación estructurado, NO sustituye:
- Working session trial (4-8 semanas de trabajo pre-grant)
- Legal review del Founder Agreement
- Background / reference checks profesionales
- Red flag conversations (visión a 10 años, sacrifice tolerance, exit expectations)
Regla de idioma
Español.
Directorio de salida
./launchpad/{startup-slug}/cofounder-matching/
├── rubric.md # Rubric personalizada para esta startup
├── candidate-[name]/
│ ├── scorecard.md # 6-axis scorecard con evidencia
│ ├── gap-analysis.md # Qué falta vs ideal candidate
│ └── interview-questions.md # Preguntas customizadas para este candidato
└── shortlist.md # Ranked list + recommendation
La rubric — 6 ejes de evaluación
1. Domain fit
¿El candidato entiende el mercado objetivo?
Score 1-5:
- 5: Operador/builder en el vertical con >5 años + network relevante
- 4: Usuario profundo del problema + research extensa del mercado
- 3: Familiar con el mercado pero sin operator experience
- 2: Conoce el mercado desde afuera (consumidor casual, articles)
- 1: Sin conocimiento relevante del dominio
2. Skill complementarity
¿Las skills del candidato llenan gaps del founder existente?
Mapear a los 3 roles archetype de Y Combinator:
- Hacker (builds the product)
- Hustler (sells and markets)
- Designer (designs the experience)
Score 1-5:
- 5: Cubre completamente un gap crítico + es world-class en eso
- 4: Cubre un gap + tiene competencia sólida
- 3: Cubre parcialmente un gap existente
- 2: Duplica skills existentes (overlapping, not complementary)
- 1: No aporta skills relevantes al stage actual
3. Values alignment
¿Comparten los valores clave para esta venture?
5 valores standard a evaluar:
- Ambition (growth-oriented vs. lifestyle)
- Risk tolerance (VC path vs. bootstrap)
- Time horizon (10-year commitment vs. 3-year exit)
- Ethics (how they've handled moral edge cases pre)
- Impact (profit alone vs. impact + profit)
Score 1-5:
- 5: Explícitamente alineado en los 5 valores con evidencia
- 4: Alineado en 4/5, el 5° es aceptable divergence
- 3: Alineado en 3/5, 2 divergencias require explícit conversation
- 2: Divergente en ≥3 valores — red flag
- 1: Misalignment fundamental que predicto breakdown
4. Equity expectations
¿El candidato espera un equity split compatible con el stage + contribution?
Score 1-5:
- 5: Expectativa razonable (10-50% dependiendo stage) + flexible + firma vesting 4yr/1yr cliff sin fricción
- 4: Razonable pero negotiations on terms (acceleration, cliff variation)
- 3: Expectativa alta pero negociable con clarification
- 2: Expectativa inflada (e.g., 50% joining at Seed post-MVP)
- 1: Unreasonable — demand sin justificación o sin aceptar vesting
5. Time commitment
¿Fulltime es realista para este candidato?
Score 1-5:
- 5: Available full-time desde día 1, financial runway personal ≥12 meses
- 4: Full-time en ≤30 días + financial runway ≥6 meses
- 3: 80%+ committed con day-job por ≤3 meses transition
- 2: Part-time indefinido (>50%) — risky pero manageable para stage
- 1: Part-time <30% o indefinido — usualmente red flag para founder
6. Track record + execution evidence
¿Qué ha shipped / construido / logrado el candidato antes?
Score 1-5:
- 5: Exit previo o role senior en exitosa startup + refs strong
- 4: Built/shipped product con traction cuantificable + refs positivos
- 3: Builds independientes con evidence (GitHub, Dribbble, case studies)
- 2: Training/certification sin ship evidence
- 1: Puro CV sin evidence de ship capability
Weighted scoring
Los ejes NO pesan igual — el peso depende del stage de la startup:
| Eje | Ideation | MVP | Traction | Funded+ |
|---|
| Domain fit | 20% | 15% | 25% | 30% |
| Skill complementarity | 30% | 35% | 25% | 20% |
| Values alignment | 25% | 20% | 15% | 15% |
| Equity expectations | 10% | 10% | 15% | 15% |
| Time commitment | 10% | 15% | 10% | 10% |
| Track record | 5% | 5% | 10% | 10% |
Rationale: early stage prioriza skill complementarity + values (team gelling); later stage prioriza domain fit + proven execution.
Flujo del skill
Paso 1 — Load startup context
CM-1: "Vamos a evaluar co-founder candidates contra tu startup. Primero necesito el context:
- ¿Tenés
startup-profile.md generado por startup-intake? Si sí, lo leo.
- ¿Cuál es el stage actual? (Ideation / Formation / MVP / Traction / Funded / Scaling)
- ¿Qué gaps de skills tenés explícitos? (CEO + falta CTO, o tenés CEO+CTO y te falta CMO, etc.)
- ¿Qué equity range estás considerando ofrecer? (típico 20-50% dependiendo contribution pre-existente)"
Paso 2 — Generate rubric personalizada
CM-2: Generar rubric.md con weights ajustados al stage, y hacerlo visible al usuario para que vea los criterios antes de evaluar candidates.
Paso 3 — Collect candidates
CM-3: "¿Cuántos candidates querés evaluar?
Para cada candidate, necesito:
- Nombre + contacto (LinkedIn URL ideal)
- Cómo los conociste
- Domain expertise claim (self-described + evidencia)
- Skills claim
- Time commitment expectation
- Equity expectation (o rango)
- Track record: 2-3 highlights verificables
Si tenés DojoOS API habilitada (via dojoos-api-consumer agent), puedo pull candidates from your co-founder matching queue directly. Si no, proceedemos con manual input."
Paso 4 — Score cada candidate
CM-4: Para cada candidate, generar scorecard.md con:
- Score por eje (1-5 con evidencia citada)
- Weighted total score
- Relative ranking vs otros candidates
- Key strengths
- Key concerns
- Kill-switch flags: criterios que si fallan, desqualify regardless de total score (ej. values misalignment severo, equity expectation outrageous, concurrent commitment a competitor venture)
Paso 5 — Gap analysis
CM-5: Generar gap-analysis.md por candidate:
- Qué axis están under-scored
- Can gaps be closed pre-grant? (training, trial period, advisor bridge)
- Can gaps be tolerable post-grant? (rare — usually breaks later)
- Compare candidate profile to "ideal candidate" for this stage
Paso 6 — Interview questions
CM-6: Generar interview-questions.md customizado por candidate:
- 5-8 preguntas targeted a sus weakest axes
- 2-3 preguntas de values exploration (hipotéticos concretos: "Si el lead VC te pidiera firing de un early employee antes de Series A, ¿cómo responderías?")
- 1-2 red flag probes (ej. "Contame de un conflict con un co-founder o partner pasado y cómo se resolvió")
- Reference check questions para terceros (ex-managers, ex-co-founders)
Paso 7 — Shortlist + recommendation
CM-7: Generar shortlist.md:
- Ranked list por weighted score
- Top recommendation con rationale
- Runner-up con rationale
- "Avoid" list con explicit red flags
- Next steps recommended:
- Trial period before grant (4-8 weeks paid engagement)
- Reference checks (3+ independent)
- Founder Agreement + Vesting (via
founder-documents skill)
- Shared equity milestones (e.g., grant 5% immediately, rest after 3-month trial validated)
Output template — scorecard.md
# Candidate Scorecard — [Candidate Name]
**Startup**: [Startup Name]
**Evaluator**: [Founder Name]
**Date**: YYYY-MM-DD
**Stage weighting**: [Ideation / MVP / Traction / Funded]
---
## Overview
- **Contact**: [LinkedIn / email]
- **How we met**: [context]
- **Time commitment claim**: [full-time / part-time X%]
- **Equity expectation**: [X% range or specific]
---
## Score by axis (1-5 with evidence)
### 1. Domain fit — Score: X/5 (weight XX%)
**Evidence**:
- [Specific observation, quote, or claim]
- [Another evidence]
**Analysis**: [why this score, not higher/lower]
### 2. Skill complementarity — Score: X/5 (weight XX%)
[same format]
### 3. Values alignment — Score: X/5 (weight XX%)
[Evaluate each of the 5 standard values with specific observations]
### 4. Equity expectations — Score: X/5 (weight XX%)
### 5. Time commitment — Score: X/5 (weight XX%)
### 6. Track record — Score: X/5 (weight XX%)
---
## Weighted total
| Axis | Score (1-5) | Weight | Weighted |
|---|---|---|---|
| Domain fit | X | XX% | X.XX |
| Skill complementarity | X | XX% | X.XX |
| Values alignment | X | XX% | X.XX |
| Equity expectations | X | XX% | X.XX |
| Time commitment | X | XX% | X.XX |
| Track record | X | XX% | X.XX |
| **TOTAL** | — | **100%** | **X.XX / 5** |
---
## Kill-switch flags
- [ ] Values severe misalignment? Y/N
- [ ] Equity demand unreasonable / no vesting? Y/N
- [ ] Concurrent commitment to competitor? Y/N
- [ ] Failed reference check? Y/N
- [ ] IP conflict / non-compete issue? Y/N
If ANY kill-switch → **DISQUALIFY regardless of score**.
---
## Summary
**Strengths**:
- [Top 3]
**Concerns**:
- [Top 3]
**Recommendation**: [Progress to interview / Trial period / Pass]
**Next step**: [specific action with timeline]
Integración con DojoOS (via dojoos-api-consumer agent)
Disponible desde v0.5.0 — el agent dojoos-api-consumer (ver agents/dojoos-api-consumer.md) es invocable desde este skill en el Paso 3 (collect candidates) pidiendo la operación list_candidate_pool. Hoy esa operación retorna SPEC_GAP (el endpoint no está en la OpenAPI spec todavía) y el skill procede con manual candidate input — como siempre. Cuando @william + @garbanzo expongan el endpoint en DojoOS, el agente comenzará a retornar LIVE_DATA automáticamente sin cambios en este skill: candidate pool + Dojo Score embebido se inyectan al rubric 6-axis. Mismo patrón para push de scorecards de vuelta via sync_candidate_scorecard (también SPEC_GAP hoy).
Integración con otras skills
startup-intake: source de startup-profile.md para stage + gaps contextcap-table-builder: post-matching, el co-founder grant se ejecuta vía este skillfounder-documents: Founder Stock Purchase Agreement + IP Assignment + Vesting Exhibitfeature-to-spike: si durante matching se descubre pattern útil para DojoOS (ej. "weighted scoring ajustado por stage es mejor que pesos fijos"), generar SPIKE
Principios clave
- Kill-switches override score: nunca avanzar si hay values misalignment fundamental
- Trial period obligatorio antes de grant completo: 4-8 semanas paid + milestone-based equity release
- Reference checks siempre: mínimo 3 ex-colleagues o ex-partners (NO solo los que el candidate eligió)
- Weighted scoring ajustado por stage: pesos no son fijos, dependen del momento de la venture
- Documentación como audit trail: cada scorecard referencable ante future founder disputes
Anti-patterns
- Grant de equity sin vesting porque "confiamos" → red flag VC + future dispute risk
- Skipping reference checks porque the candidate es amigo de un amigo
- Over-weighting de domain fit en Ideation stage (se aprende; skill complementarity pesa más temprano)
- Ignorar kill-switches "porque el candidate es excepcional en otros ejes"
- Una sola session de evaluación (no trial period)
Recursos
- Y Combinator Co-Founder Matching (ycombinator.com/cofounder-matching)
- Paul Graham — "The 18 Mistakes That Kill Startups" — #1 is single founder
- First Round Review — Co-Founder Guide
- "The Founder's Dilemmas" (Noam Wasserman, Princeton 2012) — research sobre founder splits
- Reference check framework: 3×3 (3 refs, 3 questions each: values, skills, conflict resolution)
