context-recovery

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.

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Safety Boundaries

• This skill prioritizes channel/session API history to recover context.
• It does NOT perform broad filesystem scans or shell glob searches by default.
• It does NOT send recovered context to external services.
• It does NOT write to disk unless the user explicitly asks to persist recovered state.

Triggers

Automatic Triggers

• Session begins with a `` 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..."

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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: 
  channelId: 
  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: 
  threadId: 
  limit: 50

# Then fetch parent channel for broader context
message:read
  channel: 
  channelId: 
  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 Context (safe mode)

Use platform/session APIs only (no shell filesystem scans):

# List recent sessions (if tool exists)
sessions_list:
  limit: 5

# Pull last messages from likely matching session
sessions_history:
  sessionKey: 
  limit: 80
  includeTools: true

If session APIs are unavailable, skip this step and proceed with channel-only evidence.

Step 4: Optional Memory Check (explicitly scoped)

Only inspect memory if the agent runtime already provides a scoped memory tool/path. Do not run shell glob scans across home directories.

Step 5: Synthesize Context

Compile a structured summary:

## Recovered Context

**Channel:** # ()
**Time Range:**  to 
**Messages Analyzed:** 

### Active Project/Task
- **Repository:** 
- **Branch:** 
- **PR:** # — 

### Recent Work Timeline
1. [] 
2. [] 
3. [] 

### Pending/Incomplete Actions
- ⏳ ""
- ⏳ ""

### Last User Request
> ""

Step 6: Optional Persistence (consent-first)

Do not write to disk by default. If persistence is useful, ask first:

"I can cache this recovered context to memory for later continuity. Should I save it?"

Step 7: Respond with Context

Present the recovered context, then prompt:

"Context recovered. Your last request was [X]. This action [completed/did not complete]. Shall I [continue/retry/clarify]?"

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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

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Constraints

• Prefer this protocol before declaring "insufficient data" 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
• If recovery fails, state what was attempted and what data sources were unavailable
• Avoid broad filesystem scans or writes without explicit user consent

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Auto-Trigger Detection

At session start, scan for compaction indicators:

# Pseudocode for trigger detection
if message contains "":
    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.

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Example

Scenario: Session starts with compaction summary

User message: "Summary unavailable due to context limits..."
User message: "did this happen?"

Agent executes:

1. Detects compaction via `` 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 context APIs — extracts recent user requests
6. Responds:

"Context recovered. Your last request was to extract orchestration decisions from Nexus archived sessions for PR #137 (Skills & Agents System). This action did not complete before context was truncated.

Recovered state:

• PR #137: 15,053 additions, 91 files, CONFLICTING merge state
• Spec location: content/spec-skills-agents-system.md (2,067 lines)

Shall I proceed with the extraction?"