ad-performance-learning
Post-launch performance analysis and intelligence feedback loop for paid ad campaigns. Use after ads are live and performance data is available. Analyzes real performance data, extracts actionable learnings, and propagates them back into engine files (A01 intelligence, A02 hook taxonomy, A06 Arena calibration, A10 scoring models). When data conflicts with predictions, data wins. Learnings must be actionable (WHAT + WHY + HOW to apply) and the loop must close by updating engine files, not just producing reports. Trigger when users mention ad performance, campaign results, ad analytics, what worked, learning from ads, or updating the engine with results. Requires live campaign performance data.
Resources
3Install
npx skillscat add tonyflo79/ai-crush-vault/ad-performance-learning Install via the SkillsCat registry.
A12 — Performance Learning Loop
Pipeline Position: Final Ad Engine skill. Feeds back into A01, A02, A06, A10.
PURPOSE
Close the feedback loop: analyze real performance data, extract learnings, and
propagate them back into the engine so future campaigns start smarter.
Three Laws:
- Data beats predictions (when data conflicts with predictions, data wins)
- Learnings must be actionable (WHAT + WHY + HOW to apply)
- The loop must close (update engine files, not just reports)
IDENTITY
This skill IS: Learning propagation engine that closes the feedback loop.
This skill is NOT: A pre-launch scorer (A10), a packager (A11), an intelligence scanner (A01).
Upstream: Live campaign performance data
Downstream: Updates A01 (Intelligence), A02 (Hook Taxonomy), A06 (Arena), A10 (Scoring)
REFERENCE FILES
A12-PERFORMANCE-LEARNING-AGENT.md— Complete orchestration specificationA12-PERFORMANCE-LEARNING-ANTI-DEGRADATION.md— Quality enforcement rules
OUTPUT
Primary: PERFORMANCE-LEARNING.md
Location: outputs/[project-name]/ad-engine/A12/
Categories
Install
npx skillscat add tonyflo79/ai-crush-vault/ad-performance-learning Install via the SkillsCat registry.