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deepwork_jobs.learn

Analyze DeepWork job conversations to extract learnings and update AGENTS.md instructions.

Updated Jan 27, 2026
Search Tags:#workflow#agents.md#knowledge-management#learn#deepwork#instruction-improvement#after-action
One-click install
npx skills add https://github.com/ncrmro/deepwork-permanent-portfolio --skill deepwork-jobs-learn-ncrmro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deepwork_jobs.learn
Source: https://github.com/ncrmro/deepwork-permanent-portfolio/tree/main/.claude/skills/deepwork_jobs.learn
Command: npx skills add https://github.com/ncrmro/deepwork-permanent-portfolio --skill deepwork-jobs-learn-ncrmro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes DeepWork job execution conversations to identify and capture learnings, then structures improvements to instructions and processes.

Core Features & Use Cases

  • Generalizable learnings: Extract recurring issues, inefficiencies, and opportunities from conversations to update job instructions and improve workflows.
  • Bespoke Learnings: Capture run-specific insights in AGENTS.md placed in the deepest common folder to guide future work.
  • Change Management: Suggest and record changes to instruction files, doc specs, and changelogs, and ensure syncing.

Quick Start

Analyze the latest job execution conversation and produce an AGENTS.md with generalizable learnings and a bespoke run summary in the deepest common folder.

Frequently Asked Questions about deepwork_jobs.learn

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

FAQPage Schema
How do I extract learnings from job execution conversations to improve instructions?▼

Analyzing job execution conversations identifies recurring issues and captures both generalizable improvements for job instructions and bespoke run-specific insights for future workflows.

What is an AGENTS.md file and when do I need it for knowledge management?▼

An AGENTS.md file captures run-specific bespoke learnings and generalizable workflow improvements, placed in the deepest common folder to guide future work after analyzing job execution conversations.

How to update instruction files based on after-action reviews across multiple jobs?▼

After-action reviews across multiple jobs extract recurring inefficiencies and opportunities, applying generalizable learnings to update instruction files, bump versions, and sync necessary changes.

Can I capture both generalizable and bespoke learnings from a single job run?▼

Yes, analyzing a job execution conversation produces generalizable learnings for updating workflow instructions alongside bespoke run-specific insights captured in an AGENTS.md summary.

What's the best way to manage instruction file changes and version bumping after workflow analysis?▼

Workflow analysis suggests and records changes to instruction files, doc specs, and changelogs, ensuring version bumping and syncing are completed to maintain instruction accuracy.

Does this approach work for improving agent workflows without external dependencies?▼

Yes, analyzing job execution conversations to identify learnings and update instructional guidance operates with no external dependencies, requiring only conversation logs and instruction files.

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