simplicio-learn

Extract durable lessons from run trajectories and write them to persistent memory files.

9|Updated May 8, 2026
One-click install
npx skills add https://github.com/simpletibr/simplicio-loop-marketing --skill simplicio-learn-simpletibr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: simplicio-learn
Source: https://github.com/simpletibr/simplicio-loop-marketing/tree/main/.claude/skills/simplicio-learn
Command: npx skills add https://github.com/simpletibr/simplicio-loop-marketing --skill simplicio-learn-simpletibr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? After a coding run or session ends, the lessons it produced are lost, so the next run repeats the same mistakes and re-derives the same solutions. This Skill mines a finished run's trajectory for high-signal lessons and persists them to AGENTS.md or machine-readable memory so future runs start smarter. ## Core Features & Use Cases - Correction Mining: Detects failed-then-succeeded command pairs, classifies the error type, and records wrong-pattern to right-pattern mappings. - Precedent & Bug Pattern Storage: Stores solved-problem fingerprints and structured root-cause entries in .orchestrator/patterns.jsonl so recurring issues are reused, not re-diagnosed. - Incremental, Deduped Memory: Processes only new trajectory segments via an index file, dedups semantically, and caps memory sections at ~12 bullets with eviction. - Use Case: After simplicio-tasks finishes its self-audit, run this Skill to write the top corrections and stable workspace facts into AGENTS.md so the next session pre-empts known failures. ## Quick Start Ask the agent to run a retrospective on this session and write any durable lessons to the project memory.

Frequently Asked Questions about simplicio-learn

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

FAQPage Schema
How do I make an AI coding agent remember lessons between sessions?▼

Run a retrospective step that mines the session trajectory for corrections, solved precedents, and stable facts, then writes them to a persistent file like AGENTS.md. This Skill automates that with incremental indexing and deduplication so memory stays lean.

What kinds of lessons should be stored in agent memory?▼

Store three durable categories: command corrections with error classification, solved-problem precedents with fingerprints, and stable workspace facts or user preferences. One-off state, transcripts, and secrets are explicitly excluded.

How does the skill avoid duplicating lessons already in memory?▼

It loads an incremental index of previously processed trajectory segments and semantically dedups new candidates against stored bullets. Near-duplicates bump an occurrence count instead of adding new entries, and sections are capped at about 12 bullets.

When should I run a retrospective on an agent run?▼

Run it after a task workflow finishes its self-audit, at session end via a stop hook, or whenever the user asks to remember something. If nothing durable surfaced, the correct output is to write nothing.

Can stored lessons override the agent's safety rules?▼

No. Transcript and item content is treated as untrusted, and a lesson cannot encode an instruction that overrides safety gates. Memory is also bounded, deduped, and evictable, and wrong lessons are deleted rather than kept.