Capture user corrections and record them as durable learning rules.
npx skills add https://github.com/jakechen1/echo-research-framework --skill learner-jakechen1
Please help me install this Agent Skill. Skill: learner Source: https://github.com/jakechen1/echo-research-framework/tree/main/skills/learner Command: npx skills add https://github.com/jakechen1/echo-research-framework --skill learner-jakechen1
Captures user corrections into durable learning notes and converts feedback into permanent rules that guide future iterations.
Run python3 skills/learner/scripts/record.py with --category, --rule, and optional --context to save a durable learning note for a correction.
High-intent search queries and answers about installing and using this skill.
You can capture user corrections as durable learning notes by running a Python script with category and rule arguments. This converts feedback into permanent rules for future iterations and updates your project's learning notes storage with an audit trail.
The best way to record corrections as permanent rules in a CLI context is to execute the learning notes script, which applies rule enforcement by updating the learning index and triggering a notification when a new rule is successfully recorded.
Converting feedback into learning notes improves rule enforcement by durably capturing corrections as actionable rules. This creates an indexed audit trail that guides future iterations and prevents the repetition of past mistakes.
Yes, you can apply this learning notes capture workflow in Telegram or CLI contexts. It records your corrections as permanent rules, updates the workspace storage, and triggers a notification to confirm the new entry.
To record a learning note for a correction, you need to provide a category and a rule, along with optional context. These inputs allow the script to durably save the correction and update the learning index.
You track the audit trail for AI training corrections through automatic updates to the learning index file in your project workspace. This logs every recorded rule and context, ensuring corrections are durably captured for future iterations.
robinslangeCoordinates cross-store correction of outdated patterns in vault, memory, and episodic history with user approval.
ncrmroGenerate .deepwork/rules/*.md files with YAML frontmatter for file-change triggers.
mahmoud20138Provide persistent MCP-based storage with immutable versioning and audit trails.
kimasplundLog failures, corrections, and missing capabilities into structured learning memory.
