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
- You log a framework outcome or look up a framework's track record
- The skill maintains a scorecard tracking which frameworks work in which contexts
- 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
| Field | Value |
|---|---|
| Framework | RICE Scoring |
| Engagement | fenwick-analytics |
| Domain | B2B SaaS / Data Infrastructure |
| Stage | Series B |
| Team Size | ~40 (product team ~6) |
| Introduced Via | workshop |
| Outcome | abandoned |
| Adaptations | Confidence multiplier removed at introduction |
| Abandonment Reason | Scores felt arbitrary; slowed standups; reverted to stack-ranking |
| Date | April 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:
| Engagement | Stage | Outcome | Abandonment Reason |
|---|---|---|---|
| brightpath-health | Series B | abandoned | Team couldn't agree on Reach estimates |
| corda-logistics | Series B | abandoned | Too slow for high-velocity roadmap cycles |
| fenwick-analytics | Series B | abandoned | Confidence 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.mdand 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?