Reviews recent Claude Code sessions (default last 5) in the current project for patterns, using parallel conversation-reviewer agents per session and cross-session synthesis.
npx claudepluginhub ed3dai/ed3d-plugins --plugin ed3d-session-reflectionThis skill uses the workspace's default tool permissions.
Review multiple recent sessions from the current project directory to identify cross-session patterns.
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Review multiple recent sessions from the current project directory to identify cross-session patterns.
ed3d-extending-claude plugin must be installed.ed3d-session-reflection plugin must be installed (provides the conversation-reviewer agent and reduce-transcript.py script).The user may invoke this as:
/review-recent-sessions — review last 5 sessions/review-recent-sessions 10 — review last 10 sessionsUse the current session's transcript path to determine the project directory. The transcript path looks like:
~/.claude/projects/-Users-ed-Development-.../SESSION_ID.jsonl
The directory containing it is the project's session directory.
If you cannot determine the project directory, ask the user.
Find the most recent JSONL files in the project directory, sorted by modification time, limited to the requested count (default 5).
ls -t "<project_session_dir>"/*.jsonl | head -<count>
Exclude the current session's transcript (the user doesn't want to review the review session itself).
If fewer than 2 sessions are found, tell the user there aren't enough sessions to do a cross-session review and suggest using /review-session instead.
Create a working directory:
mkdir -p /tmp/session-review-batch
For each session, run the reduction script:
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/reduce-transcript.py" "<session.jsonl>" "/tmp/session-review-batch/reduced-<N>.txt"
This can be done in a single bash command with a loop.
For each reduced transcript, dispatch a conversation-reviewer agent in the background:
Transcript path: /tmp/session-review-batch/reduced-N.txt Write your findings to: /tmp/session-review-batch/findings-N.md
Read the transcript, analyze it, and write your findings following your output format.
Dispatch ALL reviewers in a single message to maximize parallelism. Tell the user you've dispatched N reviewers and are waiting for results.
Once all reviewers complete, dispatch a general-purpose Sonnet agent to synthesize:
ed3d-basic-agents:sonnet-general-purpose Synthesize session reviews You are synthesizing findings from multiple Claude Code session reviews into a cross-session analysis.Read all findings files in /tmp/session-review-batch/findings-*.md
Produce a synthesis that identifies:
Recurring patterns — issues that appear across multiple sessions. These are the highest-value findings because they represent systematic problems.
Progression — is the user getting better or worse at prompting over time? Is the agent handling certain tasks better or worse?
Highest-impact recommendations — across all sessions, which recommendations would have the biggest effect? Prioritize:
Session-specific highlights — any single-session finding that's particularly noteworthy even if it didn't recur.
Write your synthesis to /tmp/session-review-batch/synthesis.md
Format as Markdown. Be specific — reference which sessions showed which patterns. Be concise — this is a summary, not a repetition of individual findings.
Read /tmp/session-review-batch/synthesis.md and present the full synthesis to the user.
If any individual session findings are particularly interesting, mention that the user can find per-session details in /tmp/session-review-batch/findings-N.md.