Generate session retrospectives, AI diaries, and lessons learned files.
npx skills add https://github.com/tinner-deinno/innova-skills-lib --skill rrr-tinner-deinno
Please help me install this Agent Skill. Skill: rrr Source: https://github.com/tinner-deinno/innova-skills-lib/tree/main/core/rrr Command: npx skills add https://github.com/tinner-deinno/innova-skills-lib --skill rrr-tinner-deinno
rrr turns a plain end-of-session “wrap up” into a structured retrospective that captures decisions, timeline, friction, and lessons learned so you improve over time instead of forgetting what happened.
Ask your AI agent: "rrr --detail" to generate a full session retrospective with an accurate timeline and detailed diary.
High-intent search queries and answers about installing and using this skill.
To generate a session retrospective, invoke the rrr command to create a structured wrap-up capturing decisions, timeline, friction, and lessons learned. It mines real timestamps from your local session logs to produce an accurate end-to-end retrospective.
Yes, you can create an AI diary by running rrr in default or --detail mode. It produces a first-person diary entry alongside honest feedback and next steps by mining timestamps directly from the local Claude Code .jsonl session file.
The best way to capture lessons learned is using the rrr --detail command, which saves a generalizable lesson file and appends a required metrics row to your memory vault. It optionally flags recurring friction and error themes for pattern review.
Yes, the retrospective tool works without full logs by using the --quick memory-only mode for a faster wrap-up. When session .jsonl files are available, it runs a background timestamp miner to avoid inventing times.
You should use the --deep multi-subagent mode for a session summary when you need a highly comprehensive retrospective with extensive analysis. This mode provides the deepest level of processing compared to the default, --quick, and --detail options.
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