Learning Cycle

Process unreviewed LearningAgent session transcripts to identify issues, investigate root causes, and incorporate learnings into agent definitions.

Arguments

This skill takes no arguments. It automatically discovers all pending sessions.

Session Log Folders needing Processing

!learning_agents/scripts/list_pending_sessions.sh

Procedure

If the list above is empty (or the .deepwork/tmp/agent_sessions directory does not exist), inform the user that there are no pending sessions to learn from and stop.

Step 1: Process Each Session

For each session log folder, run the learning cycle in sequence.

1a: Identify Issues

Spawn an Agent to run the identify skill:

Agent tool call:
  description: "Identify issues"
  subagent_type: learning-agents:learning-agent-expert
  model: sonnet
  prompt: "Run: Skill learning-agents:identify <session_log_folder>"

Run those in parallel

1b: Investigate and Incorporate

After identification completes, skip any session where the identify step reported zero issues. Only proceed with sessions that had issues identified.

For remaining sessions, start a new Agent to run investigation and incorporation in sequence for each session_log_folder:

Agent tool call:
  description: "Investigate and incorporate"
  subagent_type: learning-agents:learning-agent-expert
  model: sonnet
  prompt: "Run these two skills in sequence:
           1. Skill learning-agents:investigate-issues <session_log_folder>
           2. Skill learning-agents:incorporate-learnings <session_log_folder>"

Run session log folders from the same agent serially, but different agents in parallel. I.e. if Agent A has 7 sessions and Agent B has 3 sessions, you should have 3 "batches" of Agents where you do one session for Agent A and one for Agent B, then you would have 4 more Agents run serially for the remaining Agent A sessions.

Handling failures

If a sub-skill Agent fails for a session, log the failure, skip that session, and continue processing remaining sessions. Do not mark needs_learning_as_of_timestamp as resolved for failed sessions.

Step 2: Summary

Output in this format:

## Learning Cycle Summary

- **Sessions processed**: <count>
- **Total issues identified**: <count>
- **Agents updated**: <comma-separated list of agent names>
- **Key learnings**:
  - <agent-name>: <brief learning description>
- **Skipped sessions** (if any): <session path> — <reason>

Guardrails

• Do NOT modify agent files directly — always delegate to the learning cycle skills in Tasks
• Use Sonnet model for Agent spawns to balance cost and quality
• Use the learning-agents:learning-agent-expert agent for Agent spawns