Context Recovery

Automatically recover working context after session compaction or when continuation is implied but context is missing. Works across Discord, Slack, Telegram, Signal, and other supported channels.

Use when: Session starts with truncated context, user references prior work without specifying details, or compaction indicators appear.

Triggers

Automatic Triggers

• Session begins with a <summary> tag (compaction detected)
• User message contains compaction indicators: "Summary unavailable", "context limits", "truncated"

Manual Triggers

• User says "continue", "did this happen?", "where were we?", "what was I working on?"
• User references "the project", "the PR", "the branch", "the issue" without specifying which
• User implies prior work exists but context is unclear
• User asks "do you remember...?" or "we were working on..."

Execution Protocol

Step 1: Detect Active Channel

Extract from runtime context:

• channel — discord | slack | telegram | signal | etc.
• channelId — the specific channel/conversation ID
• threadId — for threaded conversations (Slack, Discord threads)

Step 2: Fetch Channel History (Adaptive Depth)

Initial fetch:

message:read
  channel: <detected-channel>
  channelId: <detected-channel-id>
  limit: 50

Adaptive expansion logic:

1. Parse timestamps from returned messages
2. Calculate time span: newest_timestamp - oldest_timestamp
3. If time span < 2 hours AND message count == limit:
   • Fetch additional 50 messages (using before parameter if supported)
   • Repeat until time span ≥ 2 hours OR total messages ≥ 100
4. Hard cap: 100 messages maximum (token budget constraint)

Thread-aware recovery (Slack/Discord):

# If threadId is present, fetch thread messages first
message:read
  channel: <detected-channel>
  threadId: <thread-id>
  limit: 50

# Then fetch parent channel for broader context
message:read
  channel: <detected-channel>
  channelId: <parent-channel-id>
  limit: 30

Parse for:

• Recent user requests (what was asked)
• Recent assistant responses (what was done)
• URLs, file paths, branch names, PR numbers
• Incomplete actions (promises made but not fulfilled)
• Project identifiers and working directories

Step 3: Fetch Session Logs (if available)

# Find most recent session files for this agent
SESSION_DIR=$(ls -d ~/.clawdbot-*/agents/*/sessions 2>/dev/null | head -1)
SESSIONS=$(ls -t "$SESSION_DIR"/*.jsonl 2>/dev/null | head -3)

for SESSION in $SESSIONS; do
  echo "=== Session: $SESSION ==="
  
  # Extract user requests
  jq -r 'select(.message.role == "user") | .message.content[0].text // empty' "$SESSION" | tail -20
  
  # Extract assistant actions (look for tool calls and responses)
  jq -r 'select(.message.role == "assistant") | .message.content[]? | select(.type == "text") | .text // empty' "$SESSION" | tail -50
done

Step 4: Check Shared Memory

# Extract keywords from channel history (project names, PR numbers, branch names)
# Search memory for relevant entries
grep -ri "<keyword>" ~/clawd-*/memory/ 2>/dev/null | head -10

# Check for recent daily logs
ls -t ~/clawd-*/memory/202*.md 2>/dev/null | head -3 | xargs grep -l "<keyword>" 2>/dev/null

Step 5: Synthesize Context

Compile a structured summary:

## Recovered Context

**Channel:** #<channel-name> (<platform>)
**Time Range:** <oldest-message> to <newest-message>
**Messages Analyzed:** <count>

### Active Project/Task
- **Repository:** <repo-name>
- **Branch:** <branch-name>
- **PR:** #<number> — <title>

### Recent Work Timeline
1. [<timestamp>] <action/request>
2. [<timestamp>] <action/request>
3. [<timestamp>] <action/request>

### Pending/Incomplete Actions
- ⏳ "<quoted incomplete action>"
- ⏳ "<another incomplete item>"

### Key References
| Type | Value |
|------|-------|
| PR | #<number> |
| Branch | <name> |
| Files | <paths> |
| URLs | <links> |

### Last User Request
> "<quoted request that may not have been completed>"

### Confidence Level
- Channel context: <high/medium/low>
- Session logs: <available/partial/unavailable>
- Memory entries: <found/none>

Step 6: Cache Recovered Context

Persist to memory for future reference:

# Write to daily memory file
MEMORY_FILE=~/clawd-*/memory/$(date +%Y-%m-%d).md

cat >> "$MEMORY_FILE" << EOF

## Context Recovery — $(date +%H:%M)

**Channel:** #<channel-name>
**Recovered context for:** <project/task summary>

### Key State
- <bullet points of critical context>

### Pending Items
- <incomplete actions>

EOF

This ensures context survives future compactions.

Step 7: Respond with Context

Present the recovered context, then prompt:

Channel-Specific Notes

Discord

• Use channelId from the incoming message metadata
• Guild channels have full history access
• Thread recovery: check for threadId in message metadata
• DMs may have limited history

Slack

• Use channel parameter with Slack channel ID
• Thread context requires threadId — always check for thread context first
• Parent channel fetch provides surrounding conversation context
• May need workspace-level permissions for full history

Telegram / Signal / Others

• Same message:read interface
• History depth may vary by platform
• Group vs. DM context may differ

Constraints

• MANDATORY: Execute this protocol before responding "insufficient data" or asking clarifying questions when context appears missing
• Adaptive depth: start with 50, expand to 100 max
• Time-based goal: capture at least 2 hours of context when possible
• Session log extraction: last 3 session files maximum
• Memory cache: append to daily file, do not overwrite
• If recovery fails, state what was attempted and what data sources were unavailable

Auto-Trigger Detection

At session start, scan for compaction indicators:

# Pseudocode for trigger detection
if message contains "<summary>":
    trigger_context_recovery()
elif message contains any of ["Summary unavailable", "context limits", "truncated", "compacted"]:
    trigger_context_recovery()
elif message matches continuation_patterns:
    # "continue", "where were we", "did this happen", etc.
    trigger_context_recovery()

Do not wait for user to ask — if compaction is detected, proactively recover and present context.

Example

Scenario: Session starts with compaction summary

User message: "<summary>Summary unavailable due to context limits...</summary>"
User message: "did this happen?"

Agent executes:

1. Detects compaction via <summary> tag
2. message:read channel=discord channelId=1460342116516364533 limit=50
3. Calculates time span: 2.5 hours — sufficient, no expansion needed
4. Parses messages, finds last action: "Now let me extract the key orchestration decisions..."
5. Checks session logs — extracts recent user requests
6. Writes context summary to memory/YYYY-MM-DD.md
7. Responds: