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Analyzes LearningAgent session transcripts to identify mistakes, knowledge gaps, underperformance, and creates YAML issue files for problems found.
How this skill is triggered — by the user, by Claude, or both
Slash command
/learning-agents:identifyThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are an expert AI quality reviewer analyzing session transcripts to surface actionable issues in a LearningAgent's behavior.
You are an expert AI quality reviewer analyzing session transcripts to surface actionable issues in a LearningAgent's behavior.
$ARGUMENTS is the path to the session log folder (e.g., .deepwork/tmp/agent_sessions/<session_id>/<agent_id>/).
Agent used: !cat $ARGUMENTS/agent_used 2>/dev/null || echo "unknown"
Last learning timestamp (empty if never learned): !cat $ARGUMENTS/learning_last_performed_timestamp 2>/dev/null
Existing issue files (avoid duplicates):
!ls $ARGUMENTS/*.issue.yml 2>/dev/null || echo "(none)"
Additional identification guidelines:
!learning_agents/scripts/cat_agent_guideline.sh $ARGUMENTS issue_identification
Session log folder structure:
!cat learning_agents/doc/learning_log_folder_structure.md 2>/dev/null
Read $ARGUMENTS/conversation_transcript.jsonl. It's JSONL — focus on type: "assistant" messages and type: "tool_result" entries. If learning_last_performed_timestamp exists (shown above), skip lines before that timestamp.
Look for these categories of problems:
Skip trivial issues (minor formatting, environmental failures, issues already covered by existing learnings or issue files listed above).
For each issue, invoke the report-issue skill:
Skill learning-agents:report-issue $ARGUMENTS "<one-sentence description>" "<timestamp of relevant line(s)>"
If zero issues were found, delete the needs_learning_as_of_timestamp file from the session folder:
rm $ARGUMENTS/needs_learning_as_of_timestamp
This marks the session as fully processed so that the investigate and incorporate steps can be skipped.
## Session Issue Summary
**Session**: <session_id>
**Agent**: <agent_used>
**Issues found**: <count>
| # | Category | Brief description |
|---|----------|-------------------|
| 1 | <category> | <one sentence> |
(or: "No actionable issues found. No follow-up needed — session marked as processed.")
npx claudepluginhub unsupervisedcom/deepwork --plugin learning-agentsInvestigates identified issues in LearningAgent sessions by analyzing transcripts, determining root causes like knowledge gaps or missing docs, and updating YAML issue files with reports.
Analyzes Claude Code Orchestrator/Worker/Reviewer transcripts for given issues to detect structural problems, protocol deviations, and anti-patterns.
Analyzes Claude Code JSONL transcripts to detect anti-patterns, tool misuse, user frustration signals, and workflow patterns using DuckDB SQL, 10 dimensions, and PM4Py mining.