| name | review-recent-sessions |
| description | Use when the user wants to review their recent opencode sessions for patterns — analyzes the last N sessions (default 5) in the current project, dispatching parallel reviewers per session, then synthesizing cross-session findings |
Review Recent Sessions
Review multiple recent sessions from the current project to identify cross-session patterns.
Prerequisites
- The
opencode CLI must be available on PATH (provides opencode session list and opencode export).
- The
ed3d-conversation-reviewer agent and the writing-opencode-directives skill must be available (the reviewer loads that skill).
- This skill ships
scripts/reduce-transcript.py (its absolute path is shown in the file list when this skill loads).
Invocation
The user may ask to:
- review recent sessions — default to the last 5
- review the last N sessions — use their N
Steps
1. List recent sessions
List the current project's sessions (newest first):
opencode session list
Session IDs match ses_[A-Za-z0-9]+.
Exclude the current session (the user doesn't want to review the review session itself): it is the most recently updated one and its title matches the running conversation — ask the user if it's ambiguous. Never export the active session — a session still being written can truncate mid-write. Take the requested count (default 5) from the remaining sessions.
If fewer than 2 sessions remain, tell the user there aren't enough sessions to do a cross-session review and suggest the review-session skill instead.
2. Export and reduce all transcripts
Create a working directory, then for each selected session export it and run scripts/reduce-transcript.py (shipped with this skill; its absolute path is shown in the file list when this skill loads):
mkdir -p /tmp/session-review-batch
opencode export <sessionID-N> > /tmp/session-review-batch/session-N.json
python3 scripts/reduce-transcript.py /tmp/session-review-batch/session-N.json /tmp/session-review-batch/reduced-N.txt
This can be done in a single bash command with a loop over the selected session IDs.
3. Dispatch parallel reviewers
For each reduced transcript, dispatch a ed3d-conversation-reviewer agent:
task:
subagent_type: ed3d-conversation-reviewer
description: Review session N of M
prompt: |
Review the reduced opencode session transcript.
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 so they run in parallel; react to completion notifications — do not poll or sleep. Tell the user you've dispatched N reviewers and are waiting for results.
4. Synthesize findings
Once all reviewers complete, dispatch a general-purpose Sonnet agent to synthesize:
task:
subagent_type: ed3d-sonnet-general-purpose
description: Synthesize session reviews
prompt: |
You are synthesizing findings from multiple opencode session reviews into a cross-session analysis.
Read all findings files in /tmp/session-review-batch/findings-*.md
Produce a synthesis that identifies:
1. **Recurring patterns** — issues that appear across multiple sessions. These are the highest-value findings because they represent systematic problems.
2. **Progression** — is the user getting better or worse at prompting over time? Is the agent handling certain tasks better or worse?
3. **Highest-impact recommendations** — across all sessions, which recommendations would have the biggest effect? Prioritize:
- AGENTS.md changes (things the user keeps correcting)
- Plugin hooks or permission rules (behaviors that should be enforced automatically)
- Skills/workflows (multi-step processes that keep being done manually)
4. **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.
5. Present synthesis
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.
