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

Memory Type	Why It Matters
Session summaries	What happened last time
Decisions	Active choices still relevant
Open items	Unfinished work to continue
Client info	Key details to remember
Gotchas	Pitfalls to avoid
Patterns	Established conventions

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