kill-criteria-exit-ramps

$npx mdskill add lyndonkl/claude/kill-criteria-exit-ramps

Enforce disciplined project termination using objective kill criteria.

  • Helps users avoid sunk cost fallacy in failing initiatives.
  • Relies on predefined metrics and time horizons for decisions.
  • Recommends kill or continue actions based on threshold breaches.
  • Delivers clear go/no-go recommendations with documented rationale.

SKILL.md

.github/skills/kill-criteria-exit-rampsView on GitHub ↗
---
name: kill-criteria-exit-ramps
description: Establishes pre-defined, objective conditions for stopping projects and specific decision points for continue/pivot/kill evaluations. Guides through defining kill criteria, setting go/no-go gates, avoiding sunk cost fallacy, and executing disciplined stopping decisions. Use when defining stopping rules for projects, avoiding sunk cost fallacy, setting objective exit criteria, deciding whether to continue/pivot/kill initiatives, or when users mention kill criteria, exit ramps, stopping rules, go/no-go decisions, project termination, or sunk costs.
---

# Kill Criteria & Exit Ramps

---

## Common Patterns

### Pattern 1: Upfront Kill Criteria (Before Launch)

**When**: Starting new project, experiment, or product

**Process**: (1) Define success metrics ("10% conversion"), (2) Set time horizon ("6 months"), (3) Establish kill criteria ("If <5% after 6 months, kill"), (4) Assign decision rights (specific person), (5) Document formally (signed PRD)

**Example**: New feature — Success: 20% adoption in 3 months, Kill: <10% adoption, Decision: Product VP makes call

### Pattern 2: Go/No-Go Gates (Milestone-Based)

**When**: Multi-stage projects with increasing investment

**Structure**: Stage 1 (cheap, concept) → Go/No-Go → Stage 2 (moderate, MVP) → Go/No-Go → Stage 3 (expensive, launch) → Go/No-Go

**Example**: Gate 1 (4wk, $10k): 15+ customer interviews show interest → GO. Gate 2 (3mo, $50k): 40% weekly active (got 25%) → NO-GO, kill

**Benefit**: Small investments first, kill before expensive stages

### Pattern 3: Trigger-Based Exit Ramps

**When**: Ongoing projects with uncertain outcomes

**Common triggers**: Time-based ("not profitable by Month 18"), Metric-based ("churn >8% for 2 months"), Market-based ("competitor launches"), Resource-based ("budget overrun >30%"), Opportunity-based ("better option emerges")

**Example**: SaaS — Trigger 1: MRR growth <10%/mo for 3 months → Evaluate. Trigger 2: CAC payback >24mo → Evaluate. Trigger 3: Competitor raises >$50M → Evaluate

**Note**: Triggers prompt evaluation, not automatic kill

### Pattern 4: Pivot vs. Kill Decision

**When**: Project isn't working as planned — should you pivot or kill?

**Framework**:

**Pivot if**:
- Core insight is valid but execution is wrong
- Customer pain is real, solution is wrong
- Market exists, go-to-market is wrong
- Learning rate is high (discovering new insights rapidly)
- Resource burn is sustainable (not desperation mode)

**Kill if**:
- No customer pain (nice-to-have, not must-have)
- Market too small (can't sustain business)
- Burn rate too high relative to progress
- Team doesn't believe in vision
- Better opportunities available (opportunity cost)
- Regulatory/legal blockers

**Example**: Mobile app with low engagement
- **Situation**: Launched fitness app, 10k downloads, 5% weekly active (target was 40%)
- **Pivot option**: Interviews reveal users want meal tracking not workout tracking → Pivot to nutrition app
- **Kill option**: Users don't care about fitness tracking at all, market saturated → Kill, reallocate team

**Decision**: Pivot if hypothesis valid but execution wrong. Kill if hypothesis invalid.

### Pattern 5: Portfolio Kill Criteria (Multiple Projects)

**When**: Managing portfolio of projects, need to kill some to focus

**Process**:
1. **Rank by expected value**: ROI, strategic fit, resource efficiency
2. **Define minimum threshold**: "Top 70% of portfolio gets resources"
3. **Kill bottom 30%**: Projects below threshold, regardless of sunk cost
4. **Reallocate resources**: Winners get resources from killed projects

**Example**: Company with 10 projects, capacity for 7
- Rank by: (Expected Revenue × Probability of Success) / Resource Cost
- Kill: Projects ranked #8, #9, #10 (even if they're "almost done")
- Reallocate: Engineers from killed projects to top 3

**Principle**: Opportunity cost matters more than sunk cost. "Almost done" doesn't justify continuing if better alternatives exist.

### Pattern 6: Sunk Cost Trap Avoidance

**When**: Team resists killing project due to past investment

**Technique**: **Pre-mortem inversion**
1. Ask: "If we were starting today with zero investment, would we start this project?"
2. If answer is "No" → Kill (sunk costs are irrelevant)
3. If answer is "Yes, but differently" → Pivot
4. If answer is "Yes, exactly as-is" → Continue

**Example**: Failed enterprise sales push
- **Situation**: 18 months, $2M spent, 2 customers (need 50 for viability)
- **Inversion**: "If starting today, would we pursue enterprise sales?" → "No, we'd focus on self-serve SMB"
- **Decision**: Kill enterprise sales, pivot to SMB (sunk $2M is irrelevant)

**Trap**: "We've invested so much, we can't quit now" → This is sunk cost fallacy
**Escape**: Only future costs and benefits matter. Past is gone.

---

## Workflow

Use this structured approach when defining or applying kill criteria:

```
□ Step 1: Define success metrics and time horizon
□ Step 2: Establish objective kill criteria
□ Step 3: Assign decision rights and governance
□ Step 4: Set milestone gates or trigger points
□ Step 5: Document formally (signed agreement)
□ Step 6: Monitor metrics regularly
□ Step 7: Evaluate at gates/triggers
□ Step 8: Execute kill decision (if triggered)
```

**Step 1: Define success metrics and time horizon** ([details](#1-define-success-metrics-and-time-horizon))
Specify quantifiable success criteria (e.g., "20% conversion") and evaluation period (e.g., "6 months post-launch").

**Step 2: Establish objective kill criteria** ([details](#2-establish-objective-kill-criteria))
Set numeric thresholds that trigger stop decision (e.g., "If <10% conversion after 6 months"). Make criteria objective, not subjective.

**Step 3: Assign decision rights and governance** ([details](#3-assign-decision-rights-and-governance))
Name specific person who makes kill decision. Define escalation process. Avoid "team consensus" (leads to paralysis).

**Step 4: Set milestone gates or trigger points** ([details](#4-set-milestone-gates-or-trigger-points))
For multi-stage projects: define go/no-go gates. For ongoing projects: define triggers that prompt evaluation.

**Step 5: Document formally** ([details](#5-document-formally))
Write kill criteria in PRD, project charter, or investment memo. Get stakeholders to sign/approve before launch (prevents moving goalposts).

**Step 6: Monitor metrics regularly** ([details](#6-monitor-metrics-regularly))
Track metrics weekly/monthly. Dashboard with kill criteria thresholds clearly marked. Automate alerts when approaching thresholds.

**Step 7: Evaluate at gates/triggers** ([details](#7-evaluate-at-gatestriggers))
When gate or trigger hit, conduct formal evaluation. Use pre-mortem inversion: "Would we start this today?" Decide: continue, pivot, or kill.

**Step 8: Execute kill decision** ([details](#8-execute-kill-decision))
If kill triggered: communicate decision, wind down project, reallocate resources, conduct postmortem. Execute quickly (avoid zombie projects).

---

## Guardrails

### 1. Set Kill Criteria Before Launch (Not After)

**Danger**: Defining kill criteria after project starts leads to moving goalposts

**Guardrail**: Write kill criteria in initial project document, before emotional/financial investment. Get stakeholder sign-off.

**Red flag**: "We'll figure out when to stop as we go" — this leads to sunk cost trap

### 2. Make Criteria Objective (Not Subjective)

**Danger**: Subjective criteria ("team feels it's not working") are easy to ignore

**Guardrail**: Use quantifiable metrics (numbers, dates, milestones). "5% conversion" not "low adoption". "6 months" not "reasonable time".

**Test**: Could two people independently evaluate criteria and reach same conclusion? If not, too subjective.

### 3. Assign Clear Decision Rights

**Danger**: "Team decides" or "we'll discuss" leads to paralysis (everyone has sunk cost)

**Guardrail**: Name specific person who makes kill decision. Define what data they need. Escalation path for overrides.

**Example**: "Product VP makes kill decision based on 6-month metrics. Can be overridden only by CEO with written justification."

### 4. Don't Move the Goalposts

**Danger**: When kill criteria approached, team lowers bar or extends timeline

**Guardrail**: Kill criteria are fixed at launch. Changes require formal process (written justification, senior approval, new document).

**Red flag**: "Let's give it another 3 months" when 6-month criteria not met

### 5. Sunk Costs Are Irrelevant

**Danger**: "We've invested $2M, can't stop now" — sunk cost fallacy

**Guardrail**: Use pre-mortem inversion: "If starting today with $0 invested, would we do this?" Only future matters.

**Principle**: Past costs are gone. Only question: "Is future investment better here or elsewhere?"

### 6. Kill Quickly (Avoid Zombie Projects)

**Danger**: Projects that should be killed linger, draining resources ("zombie projects")

**Guardrail**: Kill decision → immediate wind-down. Announce within 1 week, reallocate team within 1 month.

**Red flag**: Project in "wind-down" for >3 months — this is zombie mode, not killing

### 7. Opportunity Cost > Sunk Cost

**Danger**: Continuing project because "almost done" even if better opportunities exist

**Guardrail**: Portfolio thinking. Ask: "Is this the best use of these resources?" If not, kill even if 90% done.

**Principle**: Opportunity cost of *not* pursuing better option often exceeds benefit of finishing current project

### 8. Postmortem, Don't Blame

**Danger**: Kill decisions seen as "failure", teams avoid them

**Guardrail**: Normalize killing projects. Celebrate disciplined stopping. Postmortem focuses on learning, not blame.

**Culture**: "We killed 3 projects this quarter" = good (freed resources for winners), not bad (failures)

---

## Quick Reference

### Kill Criteria Checklist

Before launching project, answer:
- [ ] Success metrics defined? (quantifiable, e.g., "20% conversion")
- [ ] Time horizon set? (e.g., "6 months post-launch")
- [ ] Kill criteria established? (e.g., "If <10% conversion after 6 months, kill")
- [ ] Decision rights assigned? (specific person, not "team")
- [ ] Documented formally? (in PRD, signed by stakeholders)
- [ ] Monitoring plan? (who tracks, how often, dashboard)
- [ ] Wind-down plan? (how to kill if criteria triggered)

### Go/No-Go Gate Template

| Gate | Investment | Timeline | Success Criteria | Decision |
|------|-----------|----------|------------------|----------|
| Gate 1: Concept | $10k | 4 weeks | 15+ customer interviews showing strong interest | GO / NO-GO |
| Gate 2: MVP | $50k | 3 months | 40% weekly active users (50 beta users) | GO / NO-GO |
| Gate 3: Launch | $200k | 6 months | 10% conversion, <$100 CAC | GO / NO-GO |

### Pivot vs. Kill Decision Framework

| Factor | Pivot | Kill |
|--------|-------|------|
| Customer pain | Real but solution wrong | No pain, nice-to-have |
| Market size | Large enough | Too small |
| Learning rate | High (new insights) | Low (stuck) |
| Burn rate | Sustainable | Too high |
| Team belief | Believes with changes | Doesn't believe |
| Opportunity cost | Pivot is best option | Better options exist |

---

## Resources

### Navigation to Resources

- [**Templates**](resources/template.md): Kill criteria document, go/no-go gate template, pivot/kill decision framework, wind-down plan
- [**Methodology**](resources/methodology.md): Sunk cost psychology, portfolio management, decision rights frameworks, postmortem processes
- [**Rubric**](resources/evaluators/rubric_kill_criteria_exit_ramps.json): Evaluation criteria for kill criteria quality (10 criteria)

### Related Skills

- **expected-value**: For quantifying opportunity cost of continuing vs. killing
- **hypotheticals-counterfactuals**: For pre-mortem analysis ("what if we had killed earlier?")
- **decision-matrix**: For comparing continue/pivot/kill options
- **postmortem**: For learning from killed projects
- **portfolio-roadmapping-bets**: For portfolio-level kill decisions

---

## Examples in Context

### Example 1: Startup Feature Kill

**Context**: SaaS launched "Advanced Analytics", kill criteria: <15% adoption after 3 months

**Result**: 12% adoption → Killed feature, reallocated 2 engineers to core. Saved 6 months maintenance.

### Example 2: Enterprise Sales Pivot

**Context**: B2B SaaS, pivot trigger: <10 customers by Month 12

**Result**: 7 customers → Pivoted to self-serve SMB. Hit 200 SMB customers in 6 months, 4× faster growth.

### Example 3: R&D Portfolio Kill

**Context**: 8 R&D projects, capacity for 5. Ranked by EV/Cost: A(3.5), B(2.8), C(2.5), D(2.1), E(1.8), F(1.5), G(1.2), H(0.9)

**Decision**: Killed F, G, H despite F being "80% done". Top 3 projects shipped 4 months earlier.

More from lyndonkl/claude

SkillDescription
abstraction-concrete-examplesBuilds structured abstraction ladders that translate high-level principles into concrete, actionable examples across 3-5 levels. Bridges communication gaps, reveals hidden assumptions, and tests whether abstract ideas work in practice. Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps.
academic-letter-architectGuides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.
adr-architectureDocuments significant architectural and technical decisions with full context, alternatives considered, trade-offs analyzed, and consequences understood. Creates a decision trail that helps teams understand why decisions were made. Use when choosing between technology options, making infrastructure decisions, establishing standards, migrating systems, or when user mentions ADR, architecture decision, technical decision record, or decision documentation.
adverse-selection-priorProduces a Bayesian prior probability that an offered transaction is +EV for the recipient, given that the counterparty chose to propose it. Applies Akerlof market-for-lemons logic -- if they offered it, they believe it is +EV for them, so the prior that it is +EV for us is materially below 50%. Reusable across trade evaluation, waiver drops (another team dropping a player is also adverse selection), job-offer analysis, M&A, and any "someone offered me this" situation. Use when you receive an unsolicited trade/offer/proposal, analyzing incoming trade prior, evaluating why a counterparty proposed a deal, or when user mentions adverse selection, market for lemons, why did they offer this, incoming trade prior, they proposed it, Bayesian adjustment on received offer.
alignment-values-north-starCreates actionable alignment frameworks that give teams a shared North Star (direction), values (guardrails), and decision tenets (behavioral standards). Enables autonomous decision-making while maintaining organizational coherence. Use when starting new teams, scaling organizations, defining culture, establishing product vision, resolving misalignment, creating strategic clarity, or when user mentions North Star, team values, mission, principles, guardrails, decision framework, or cultural alignment.
analogy-weight-checkFor every analogy in a substacker draft, verifies it carries mechanical weight — the analogy does real work explaining the mechanism, not merely decorates it. Cross-references analogy-catalog.md for novelty (is this analogy reused from a prior post?) and domain fit (biology > organizational > sports preferred; physics/military disfavored). Use whenever an analogy appears in the draft. Trigger keywords: analogy weight, decorative, mechanical weight, reused analogy, catalog check, metaphor check.
answer-uncomfortable-questionTakes one strategic question about substacker ("should we launch paid?", "is this section dead?", "are we writing for the wrong audience?") and produces the mandatory evidence + reasoning + downside triad plus a recommendation. Used 3 times per Growth Strategist review. Trigger keywords: uncomfortable question, strategic question, evidence reasoning downside, triad.
attribute-performanceFor each substacker post that materially over- or under-performs the rolling baseline (|z| ≥ 1.0), produces a plain-English attribution paragraph with calibrated confidence (high / medium / low / unexplained). Considers subject-line effect, topic zeitgeist, external share, day-of-week, length effect, and audience-notes signals. Labels unexplained outliers explicitly rather than fabricating a story. Use after compute-baseline when outlier posts exist. Trigger keywords: attribution, why did this post work, outlier explanation, performance analysis.
auction-first-price-shadingComputes the optimal shaded bid for a first-price sealed-bid auction given a true private value, an estimate of the number of competing bidders N, and a value-distribution assumption. Implements the `(N-1)/N` equilibrium shading rule for uniform private values, adjusts for log-normal or empirical value distributions, layers a risk-aversion adjustment, and caps output against the bidder's remaining budget. Domain-neutral auction theory reusable across fantasy sports (baseball FAAB, NBA/NHL waiver auctions), prediction-market limit sizing, sealed procurement bids, and any blind-bid context. Use when user mentions "first-price auction bid", "sealed bid shading", "(N-1)/N", "FAAB bid amount", "auction shading", "optimal bid first-price", "bid for sealed-bid", "blind bid sizing", or when downstream logic needs a principled shade factor rather than an ad-hoc heuristic.
auction-winners-curse-haircutApplies a Bayesian haircut to a bid valuation for common-value auctions where winning is itself evidence the bidder over-estimated. Takes a raw valuation, a value-type classification (common_value / private_value / mixed), the number of informed bidders N, and a signal-dispersion estimate, and returns an adjusted valuation. Domain-neutral and reusable across fantasy FAAB, prediction markets, M&A bids, ad-auction budgets, and any generic bidding context. Use when user mentions "winner's curse", "common value auction", "valuation haircut", "adverse valuation", "Bayesian bid adjustment", or "over-paying in auction".