Add member work history and learning docs

docs/continual-learning/README.md

@@ -0,0 +1,37 @@
+# Continual Learning
+
+Status: demo-backed / active
+
+PodMan's continual-learning track owns team memory: what the system learns about
+files, collisions, interventions, outcomes, and future routing for a pod.
+
+## Files
+
+| File | Purpose |
+| --- | --- |
+| [`spec.md`](spec.md) | Data model and observe/store/predict/outcome/adapt loop |
+| [`policy.md`](policy.md) | What PodMan may and may not remember |
+| [`prompt.md`](prompt.md) | Memory-agent prompt for outcome-backed learning |
+| [`plan.md`](plan.md) | Demo build order and acceptance criteria |
+
+## What Is Implemented Now
+
+- MongoDB-backed `observations`, `collisions`, `interventions`, `outcomes`,
+  `engineer_states`, and `team_model` records.
+- Exact signature recall for prior accepted and dismissed outcomes.
+- Outcome writes through `POST /api/outcome`.
+- Team memory graph edges from accepted real outcomes.
+- No raw screenshots or recordings are stored.
+
+## What Is Intentionally Cut
+
+- Autonomous model training.
+- Broad cross-pod generalization.
+- Raw screen capture retention.
+- Vector recall as a dependency for the demo proof.
+
+## Demo Proof Path
+
+Observe screen/git state -> detect collision -> send intervention -> accept or
+dismiss outcome -> recall similar event -> show changed graph or changed
+behavior.

docs/continual-learning/plan.md

@@ -1,6 +1,6 @@
 # Continual Learning Plan
 
-Status: draft  
+Status: demo-backed / active
 Goal: prove PodMan learns from outcomes in the hackathon demo
 
 ## Must-Have Demo Loop
@@ -66,4 +66,3 @@ Goal: prove PodMan learns from outcomes in the hackathon demo
 - The second similar event behaves differently.
 - Exact MongoDB records prove the loop.
 - The graph remains legible with real data.
-

docs/continual-learning/policy.md

@@ -1,6 +1,6 @@
 # Continual Learning Policy
 
-Status: draft  
+Status: demo-backed / active
 Scope: what PodMan may learn about a team
 
 ## Prime Rule
@@ -94,4 +94,3 @@ Delete immediately:
 Seeded data is acceptable only if the demo script is honest about it. Live
 learning requires a live or staged outcome write that visibly updates the graph
 or future decision.
-

docs/continual-learning/spec.md

@@ -1,7 +1,7 @@
 # Continual Learning Spec
 
-Status: draft  
-Scope: how PodMan learns team memory from live work and outcomes  
+Status: demo-backed / active
+Scope: how PodMan learns team memory from live work and outcomes
 Owner: continual learning / Team memory
 
 ## Purpose
@@ -16,6 +16,22 @@ The visible loop:
 observe -> store -> predict -> outcome -> adapt
 ```
 
+## What Is Implemented Now
+
+- `observations`, `collisions`, `interventions`, `outcomes`,
+  `engineer_states`, `team_model`, `graph_nodes`, and `graph_edges` are the
+  current memory truth.
+- Exact signature recall and accepted/dismissed outcomes exist.
+- Accepted real outcomes can produce `learned_from` graph edges and ownership
+  memory.
+- Raw screenshots and recordings are not stored.
+
+## What Is Intentionally Cut
+
+- Full autonomous training.
+- Broad threshold changes from one example.
+- Making vector search required for the demo learning proof.
+
 ## Source Collections
 
 ### `engineer_states`
@@ -214,4 +230,3 @@ editing, collision, intervention, outcome, learned, agent
 - The Team memory graph can explain the learning loop.
 - Dismissals and false positives are retained.
 - The demo does not rely on raw screenshots or hidden state.
-