reflect

Solid

Session retrospective and skill audit. Use when asked to reflect, do a retrospective, review lessons learned, audit what went well or wrong, or review session effectiveness.

AI & Automation 39 stars 8 forks Updated 1 weeks ago MIT

Install

View on GitHub

Quality Score: 86/100

Stars 20%
53
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Reflect ## Success Criteria - Every mistake/friction point cites the specific moment and its impact - Improvements are actionable and prioritized (cap defined in step 4) - Each skill audit proposes measurable changes (not vague suggestions) - User is asked which items to persist to memory - If review activity occurred, review-trap patterns are captured to persistent memory, or explicitly marked as "none" ## Process ### 1. Session Review Scan the full conversation. For each finding, cite the specific exchange (quote or paraphrase) and its impact. | Category | Signal | |----------|--------| | **Mistakes** | Wrong outputs, incorrect assumptions, hallucinated facts | | **Friction** | Repeated clarifications, verbose responses, misread intent | | **Wasted effort** | Work discarded, wrong approaches tried first | | **Wins** | Approaches worth repeating, smooth interactions | Skip one-time typos, external tool failures, and issues outside agent control. ### 2. Review Activity Scan (if applicable) If the session included PR or MR review activity in either direction, run this scan before moving on. Skip only if no reviews happened. **Inbound (my code was reviewed):** For each review comment received: - Did I accept it? If yes, what pattern did the reviewer catch that I missed? Is it a recurring blind spot? Capture the one-liner to persistent memory. - Did I push back? If I was right and the reviewer was wrong, nothing to capture. If I was wrong and had to retract mid-threa...

Details

Author
iliaal
Repository
iliaal/ai-skills
Created
6 months ago
Last Updated
1 weeks ago
Language
Shell
License
MIT

Similar Skills

Semantically similar based on skill content — not just same category