diff --git a/mma-orchestrator/SKILL.md b/mma-orchestrator/SKILL.md
index b97cffa..70ee3bc 100644
--- a/mma-orchestrator/SKILL.md
+++ b/mma-orchestrator/SKILL.md
@@ -26,12 +26,13 @@ If you run a test or command that fails with a significant error or large traceb
 1. **DO NOT** analyze the raw logs in your own context window.
 2. **DO** spawn a stateless Tier 4 agent to diagnose the failure.
 3. *Command:* `uv run python scripts/mma_exec.py --role tier4-qa "Analyze this failure and summarize the root cause: [LOG_DATA]"`
+4. Avoid direct reads to files, use file summaries or ast skeletons for files if they are code and we have a tool for parsing them.
 
 ## 3. Persistent Tech Lead Memory (Tier 2)
 Unlike the stateless sub-agents (Tiers 3 & 4), the **Tier 2 Tech Lead** maintains persistent context throughout the implementation of a track. Do NOT apply "Context Amnesia" to your own session during track implementation. You are responsible for the continuity of the technical strategy.
 
 ## 4. AST Skeleton Views
-To minimize context bloat for Tier 3, use "Skeleton Views" of dependencies (extracted via `mcp_client.py` or similar) instead of full file contents, unless the Tier 3 worker is explicitly modifying that specific file.
+To minimize context bloat for Tier 2 & 3, use "Skeleton Views" of dependencies (extracted via `mcp_client.py` or similar) instead of full file contents, unless the Tier 3 worker is explicitly modifying that specific file.
 
 <examples>
 ### Example 1: Spawning a Tier 4 QA Agent