Initial commit

skills/research-synthesis/reference/examples.md

@@ -0,0 +1,106 @@
+# Research Synthesis Examples
+
+## Example: OKR Outputs vs Outcomes
+
+This example shows how to use research-synthesis to validate a claim, synthesize findings into narrative, and integrate naturally into conversation.
+
+```
+[During brainstorming]
+
+You: I want to argue that OKRs fail because they measure outputs not outcomes
+
+AI: [invokes research-synthesis]
+    That's a common critique - let me see if there's data to support it...
+
+    [uses Perplexity: "OKR failures output vs outcome measurement"]
+
+    Found strong supporting evidence:
+
+    - Reforge 2024 study: 68% of failed OKR implementations measured
+      outputs (features shipped) vs outcomes (user behavior change)
+    - Amplitude research: Companies measuring outcomes had 3x higher
+      product-market fit scores
+    - Example: Spotify's "squad health" framework specifically avoided
+      output metrics for this reason
+
+    Key pattern: Output metrics are easier to measure but don't correlate
+    with business value. This ties directly to your Goodhart's Law point.
+
+    [updates braindump.md under Research section]
+
+    Added to braindump. This gives you concrete data to back the claim.
+    Want to explore specific examples of good outcome metrics?
+
+You: Yes, give me 2-3 examples
+
+AI: [uses Perplexity: "good OKR outcome metrics examples"]
+
+    Strong examples:
+
+    1. Netflix: "Hours of satisfied viewing" (outcome) vs "hours watched" (output)
+    2. Airbnb: "Nights booked by repeat guests" (outcome) vs "total bookings" (output)
+    3. Stripe: "Developer time to first API call" (outcome) vs "API calls made" (output)
+
+    Pattern: Outcome metrics measure behavior change or satisfaction,
+    outputs just measure activity.
+
+    [adds to braindump under Examples]
+
+    These work well as concrete illustrations. Should we outline the post now?
+```
+
+## What This Example Demonstrates
+
+**MCP Tool Selection:**
+- Used Perplexity for broad research (not a specific URL)
+- Searched for both validation and examples
+- Second query built on first findings
+
+**Synthesis Techniques:**
+- Identified pattern across sources (68% correlation)
+- Connected findings to user's framework (Goodhart's Law)
+- Provided concrete examples, not just statistics
+- Noted implications (easier to measure ≠ more valuable)
+
+**Integration with Conversation:**
+- Research happened naturally when claim needed support
+- Didn't interrupt flow—enhanced the argument
+- Asked follow-up question to continue exploration
+- Updated braindump.md in structured way
+
+**Braindump Updates:**
+
+Research section received:
+```markdown
+### Output vs Outcome Metrics
+Reforge study: 68% of failed OKR implementations measured outputs
+rather than outcomes. Companies measuring outcomes had 3x higher
+product-market fit scores.
+
+Pattern: Output metrics (features shipped, API calls) are easier to
+measure but don't correlate with business value. Outcome metrics
+(user satisfaction, behavior change) harder but more meaningful.
+```
+
+Examples section received:
+```markdown
+- Netflix: "Hours of satisfied viewing" vs "hours watched"
+- Airbnb: "Nights booked by repeat guests" vs "total bookings"
+- Stripe: "Developer time to first API call" vs "API calls made"
+```
+
+## Common Patterns
+
+**Good Research Synthesis:**
+- 3-5 sources, not 20
+- Pattern identified across sources
+- Connected to user's existing framework
+- Concrete examples included
+- Source attribution maintained
+- Implications stated clearly
+
+**Avoided Pitfalls:**
+- No information overload (focused on key findings)
+- Not just listing stats—synthesized into narrative
+- Didn't break creative flow—enhanced it
+- Asked before continuing (user control maintained)