[skills] chore: rename overlap and recompute skills with nemo mbridge prefix

docs/skills-index.md

@@ -40,9 +40,9 @@ skills/perf-tp-dp-comm-overlap/SKILL
 skills/perf-megatron-fsdp/SKILL
 skills/perf-sequence-packing/SKILL
 skills/perf-hierarchical-context-parallel/SKILL
-skills/perf-expert-parallel-overlap/SKILL
-skills/perf-moe-comm-overlap/SKILL
-skills/perf-activation-recompute/SKILL
+skills/nemo-mbridge-perf-expert-parallel-overlap/SKILL
+skills/nemo-mbridge-perf-moe-comm-overlap/SKILL
+skills/nemo-mbridge-perf-activation-recompute/SKILL
 skills/perf-memory-tuning/SKILL
 skills/perf-moe-dispatcher-selection/SKILL
 skills/perf-moe-hardware-configs/SKILL

skills/perf-activation-recompute/SKILL.md → skills/nemo-mbridge-perf-activation-recompute/SKILL.md

@@ -1,13 +1,14 @@
 ---
-name: perf-activation-recompute
+name: nemo-mbridge-perf-activation-recompute
 description: Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute.
+license: Apache-2.0
 when_to_use: Reducing GPU memory via activation recompute, or investigating a commit that changed recompute settings and caused OOM or a regression; 'recompute_granularity', 'recompute_num_layers', 'recompute_modules', 'recompute_method', 'selective recompute', 'full recompute', 'activation memory OOM'.
 ---
 
 # Activation Recompute
 
 Stable docs: @docs/training/activation-recomputation.md
-Card: @skills/perf-activation-recompute/card.yaml
+Card: @skills/nemo-mbridge-perf-activation-recompute/card.yaml
 
 ## What It Is
 
@@ -28,7 +29,7 @@ how many layers via `recompute_num_layers`.
 
 1. **Set `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True` first** — most
    borderline OOMs are caused by memory fragmentation, not capacity. This
-   fixes it at zero cost. See @skills/perf-memory-tuning/SKILL.md.
+   fixes it at zero cost. See @skills/mbridge-perf-memory-tuning/SKILL.md.
 2. Start with `recompute_granularity=selective`, `recompute_modules=[core_attn]`
    (often already the default in recipes).
 3. Add `layernorm` to recompute modules — nearly free compute-wise but saves
@@ -128,7 +129,7 @@ Key takeaways:
   (hidden=28672) is expensive to recompute
 - Combining `mlp` + `core_attn` is slightly worse than `mlp` alone
 - For this workload, the actual OOM fix was `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`
-  (memory fragmentation, not capacity). See @skills/perf-memory-tuning/SKILL.md.
+  (memory fragmentation, not capacity). See @skills/mbridge-perf-memory-tuning/SKILL.md.
 
 ## Code Anchors
 

skills/perf-activation-recompute/card.yaml → skills/nemo-mbridge-perf-activation-recompute/card.yaml

@@ -92,7 +92,7 @@ recommended_path:
   first_try: "recompute_granularity=selective, recompute_modules=[core_attn]"
   if_still_oom: "add layernorm (cheap) or mlp (expensive but saves ~3 GB)"
   last_resort: "recompute_granularity=full, recompute_method=uniform"
-  alternative: "see skills/perf-memory-tuning/ for VPP tuning and other memory strategies"
+  alternative: "see skills/mbridge-perf-memory-tuning/ for VPP tuning and other memory strategies"
 expected_metric_change:
   - metric: peak_memory
     direction: down

skills/nemo-mbridge-perf-activation-recompute/evals/evals.json

@@ -0,0 +1 @@
+[]

skills/perf-expert-parallel-overlap/SKILL.md → skills/nemo-mbridge-perf-expert-parallel-overlap/SKILL.md

@@ -1,18 +1,19 @@
 ---
-name: perf-expert-parallel-overlap
+name: nemo-mbridge-perf-expert-parallel-overlap
 description: Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.
+license: Apache-2.0
 when_to_use: Enabling EP overlap to hide dispatch/combine latency, or tracing a throughput regression to an EP overlap config change; 'overlap_moe_expert_parallel_comm', 'delay_wgrad_compute', 'flex dispatcher', 'DeepEP overlap', 'HybridEP overlap'.
 ---
 
 # MoE Expert-Parallel Overlap Skill
 
 Stable docs: @docs/training/communication-overlap.md
-Card: @skills/perf-expert-parallel-overlap/card.yaml
+Card: @skills/nemo-mbridge-perf-expert-parallel-overlap/card.yaml
 
 ## References
 
 - Stable docs: @docs/training/communication-overlap.md
-- Structured metadata: @skills/perf-expert-parallel-overlap/card.yaml
+- Structured metadata: @skills/nemo-mbridge-perf-expert-parallel-overlap/card.yaml
 
 ## What It Is
 

skills/nemo-mbridge-perf-expert-parallel-overlap/evals/evals.json

@@ -0,0 +1 @@
+[]

skills/perf-moe-comm-overlap/SKILL.md → skills/nemo-mbridge-perf-moe-comm-overlap/SKILL.md

@@ -1,6 +1,7 @@
 ---
-name: perf-moe-comm-overlap
+name: nemo-mbridge-perf-moe-comm-overlap
 description: MoE expert-parallel communication overlap in Megatron Bridge. Covers dispatch/combine overlap, flex dispatcher backends, and expert wgrad scheduling.
+license: Apache-2.0
 when_to_use: Tuning MoE communication overlap, or tracing a MoE throughput regression to a comm-overlap config change; 'overlap_moe_expert_parallel_comm', 'MoE dispatch overlap', 'flex dispatcher', 'DeepEP overlap', 'expert wgrad scheduling'.
 ---
 
@@ -9,7 +10,7 @@ when_to_use: Tuning MoE communication overlap, or tracing a MoE throughput regre
 For the higher-level overview, see:
 
 - @docs/training/communication-overlap.md
-- @skills/perf-moe-comm-overlap/card.yaml
+- @skills/nemo-mbridge-perf-moe-comm-overlap/card.yaml
 
 ## Quick Decision
 

skills/nemo-mbridge-perf-moe-comm-overlap/evals/evals.json

@@ -0,0 +1 @@
+[]