Session Start - Context Loading

Load relevant memories to maintain continuity across sessions.

When to Invoke

AUTOMATICALLY at the beginning of every session:

• When user sends their first message
• Before responding to any request
• Silently in the background

Execution Steps

1. Identify context signals:

   • Current working directory (from system context)
   • Project name (from path or CLAUDE.md)
   • User's first message keywords
   • Git branch if available

2. Search for relevant memories:

   mcp__memory-service__memory_search(
     query="<project name> OR <key topic>",
     limit=5,
     time_expr="last 7 days",
     quality_boost=0.3
   )
   

3. Also search for:

   • Recent session summaries: query="session-summary", limit=3
   • Client info if mentioned: query="<client name>", tags=["client"]
   • Active decisions: query="decision", time_expr="last 14 days"

4. Integrate context:

   • Use loaded memories to inform responses
   • Reference relevant past decisions
   • Continue where previous sessions left off

5. Silent operation:

   • Do NOT announce "I loaded X memories"
   • Just use the context naturally
   • Only mention if user asks about previous sessions

Search Strategies

By Project

memory_search(query="botsniper trading", limit=5)
memory_search(query="foodshot ai", limit=5)

By Recency

memory_search(time_expr="last 3 days", limit=10)
memory_search(time_expr="yesterday", limit=5)

By Type

memory_search(query="decision", tags=["decision"], limit=5)
memory_search(query="client", tags=["client"], limit=5)

Combined

memory_search(
  query="<project> decisions",
  time_expr="last 7 days",
  quality_boost=0.3,
  limit=10
)

What to Look For

Example Flow

User starts session in /Users/maskedhunter/coding/botsniper-optimizer:

1. Detect: Project is "botsniper-optimizer", domain is "trading bots"
2. Search: memory_search(query="botsniper trading bot", limit=5, time_expr="last 7 days")
3. Load: Recent decisions about Billy V4, Blood V5 parameters
4. Search: memory_search(query="session-summary", limit=2)
5. Load: Yesterday's session about optimizing stop losses
6. Respond: Use this context to continue naturally

User says: "Let's continue working on the Hurricane bot"

1. Detect: Topic is "Hurricane bot", domain is "Kalshi weather trading"
2. Search: memory_search(query="hurricane kalshi weather", limit=5)
3. Load: Recent decisions about forecast verification, position monitoring
4. Continue: Pick up where previous session left off

Cloud Recall Fallback

If local memory search returns fewer than 3 results AND cloud backup is configured (~/.claude-memory-cloud.env exists):

1. Search cloud backup:

   cd ~/coding/claude-memory && python3 -m cloud.cli search "<project or topic>" --limit 5 --include-deleted
   

2. If cloud has memories not found locally:

   • Include cloud results in context with [CLOUD] indicator
   • Offer to restore if user asks about missing context

3. Restore if needed:

   cd ~/coding/claude-memory && python3 -m cloud.cli restore --hash <hash1>,<hash2>
   

The cloud preserves everything forever - even memories that were deleted or compressed during local consolidation. This ensures total recall.

Important Notes

• Be proactive: Don't wait for user to ask for context
• Be silent: Don't announce memory loading
• Be selective: Quality over quantity - top 5-10 memories
• Be natural: Weave context into responses seamlessly
• Cloud fallback: Use cloud when local results are sparse