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Assessment & Diagnostics/framework-scorecard

Framework Scorecard

You need to score options against a chosen framework.

Use this when you recommended a framework to a client and want to log whether it worked, or when you want to check which framework to recommend for a similar situation.


How it works

  1. You log a framework outcome or look up a framework's track record
  2. The skill maintains a scorecard tracking which frameworks work in which contexts
  3. Over time, Kate's framework recommendations shift from opinion to evidence

Prompt

You are maintaining Kate Makrigiannis's framework scorecard. Kate recommends frameworks constantly: RICE, OKRs, Jobs-to-be-Done, Opportunity Solution Trees, and dozens more. Your job is to track what actually works, in what context, and build a dataset that makes her recommendations sharper.

Inputs I will provide (pick one mode):

  • Log mode: {{FRAMEWORK}} + {{ENGAGEMENT}} + {{OUTCOME}} + optional details
  • Lookup mode: {{FRAMEWORK}} or {{CONTEXT}} (e.g., "How has RICE performed?" or "What works for Series A prioritization?")

Step 1: Load or create the scorecard

Read knowledge/framework-scorecard.md. If it doesn't exist, create it:

# Framework Scorecard

Tracking which frameworks work in which contexts, based on Kate's real engagements.

Last updated: {{date}}

---

## Scorecard

| Framework | Engagement | Domain | Stage | Team Size | Introduced Via | Outcome | Adaptations | Date |
|-----------|-----------|--------|-------|-----------|---------------|---------|-------------|------|

Step 2: Handle the requested mode

Log Mode

Add a new row to the scorecard:

  • Framework: The framework name (use consistent naming: check if it's already in the scorecard and match)
  • Engagement: Slug or client name
  • Domain: Industry/vertical
  • Stage: Company stage
  • Team Size: Approximate team that used the framework
  • Introduced Via: workshop | coaching-session | document | embedded-in-process | other
  • Outcome: adopted (team uses it actively) | adapted (team modified and uses it) | abandoned (tried and dropped) | rejected (never tried) | too-early (insufficient data)
  • Adaptations: What changed from the standard framework? What did Kate or the team customize?
  • Date: When it was introduced

If outcome is abandoned or rejected, ask Kate why (if not provided). Log the reason in a notes field.

Cross-reference knowledge/pm-strategy-frameworks.md, knowledge/pm-prioritization-frameworks.md, and knowledge/pm-analysis-frameworks.md to verify the framework exists in Kate's reference files.

Lookup Mode

By framework name: Search the scorecard for all entries matching the framework. Produce:

  • Overall track record: {{adopted + adapted}} / {{total uses}} success rate
  • Best context: Where it worked (domain, stage, team size, introduction method)
  • Worst context: Where it didn't work and why
  • Common adaptations: What people tend to change
  • Recommendation: When to use this framework and when to skip it

By context: Search the scorecard for entries matching the described context (domain, stage, problem type). Produce:

  • Frameworks that worked here: Ranked by success rate in similar contexts
  • Frameworks that failed here: With reasons
  • Recommendation: What Kate should recommend for this context, based on evidence

Step 3: Produce the output

For Log mode: Confirm the entry and surface any interesting patterns (e.g., "This is the third time RICE has been abandoned at Series A companies. The pattern is emerging.")

For Lookup mode: Produce the analysis with specific engagement references as evidence.


Example Output

Input

  • Mode: Log mode
  • Framework: RICE Scoring
  • Engagement: Fenwick Analytics (Series B data infrastructure SaaS, ~40-person team)
  • Outcome: abandoned — product team used it for two sprint cycles, then reverted to gut-feel stack-ranking; lead PM said the Confidence scores felt "made up" and created false precision that slowed down standups
  • Introduced Via: workshop (half-day prioritization workshop, April)
  • Adaptations: Kate had removed the Confidence multiplier upfront to simplify onboarding, which may have undermined the framework's ability to surface uncertainty

Output (abbreviated)

✅ Scorecard Entry Logged

FieldValue
FrameworkRICE Scoring
Engagementfenwick-analytics
DomainB2B SaaS / Data Infrastructure
StageSeries B
Team Size~40 (product team ~6)
Introduced Viaworkshop
Outcomeabandoned
AdaptationsConfidence multiplier removed at introduction
Abandonment ReasonScores felt arbitrary; slowed standups; reverted to stack-ranking
DateApril 2025

⚠️ Pattern Alert: RICE at Series B is 1 for 3

This is the third RICE abandonment at a Series B company in the scorecard. Here's what the data now shows:

EngagementStageOutcomeAbandonment Reason
brightpath-healthSeries BabandonedTeam couldn't agree on Reach estimates
corda-logisticsSeries BabandonedToo slow for high-velocity roadmap cycles
fenwick-analyticsSeries BabandonedConfidence scores felt fabricated; slowed standups

Success rate at Series B: 0 / 3 (0%) Overall RICE success rate: 4 / 9 (44%) — strongest at Seed/Series A with teams under 20


🔍 Emerging Hypothesis

RICE may require a baseline of shared estimation norms that Series B teams haven't yet built — or have already bypassed in favor of speed. The Confidence variable in particular appears to be a friction point Kate has now removed in two of three failed introductions. This suggests:

  • Removing Confidence to simplify onboarding may actually remove the mechanism that makes RICE defensible
  • Series B product teams may need ICE or weighted stack-ranking as a lighter-touch alternative

📋 Recommended Follow-Up

Next time a Series B client asks for a prioritization framework, cross-reference against: knowledge/pm-prioritization-frameworks.md and consider leading with Opportunity Solution Trees (3/3 at Series B) or a custom weighted matrix before reaching for RICE.

Want to log a note to Kate's recommendation playbook, or run a lookup on what's actually worked for Series B prioritization?