reflexion

Parse ITERATE feedback, extract root causes, and store reflections in forge.db.

8|5|Updated Dec 30, 2025
One-click install
npx skills add https://github.com/axiomantic/spellbook --skill reflexion-axiomantic
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: reflexion
Source: https://github.com/axiomantic/spellbook/tree/main/skills/reflexion
Command: npx skills add https://github.com/axiomantic/spellbook --skill reflexion-axiomantic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reflexion helps teams turn ITERATE verdicts into actionable learnings by analyzing feedback, extracting root causes, and storing reflections for future iterations.

Core Features & Use Cases

  • Parse feedback from ITERATE roundtables
  • Categorize root causes and generate structured lessons
  • Store reflections in forge.db with status and pattern checks
  • Generate precise retry guidance and escalation triggers

Quick Start

Invoke the reflexion-analyze workflow after an ITERATE verdict to store a reflection and generate retry guidance.

Frequently Asked Questions about reflexion

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract root causes from iteration feedback to prevent repeating the same failures?

Root-cause extraction from iteration feedback is handled by parsing ITERATE verdicts, categorizing the feedback items, and storing structured reflections in forged.db. This identifies underlying issues and generates guidance to prevent repeating the same failures in future cycles.

What is the best way to generate retry guidance after a failed iteration roundtable?

Generating retry guidance after a failed iteration roundtable requires analyzing the stored reflections and detected patterns from previous cycles. The system parses the feedback, extracts root causes, and outputs precise retry guidance to correct the specific failures in the next attempt.

How does pattern detection work when processing multiple feedback items across iteration cycles?

Pattern detection works by parsing and categorizing multiple feedback items across iteration cycles, then checking stored reflections in forged.db for recurring root causes. When repeated patterns are identified, the system triggers alerts to highlight systemic issues.

When should I escalate issues during feedback processing instead of continuing to iterate?

Issues during feedback processing should be escalated after three consecutive iteration cycles fail to resolve the problem. The system includes guardrails that automatically escalate when retry guidance and root-cause analysis fail to break the cycle.

Can I store iteration reflections and track their status across different features and stages?

Storing iteration reflections and tracking their status across features and stages is supported by persisting the categorized root causes to forged.db. The system applies this logic across roundtable iterations, maintaining status checks and pattern alerts for ongoing learning.