autonomous-learner
Self-directed knowledge acquisition guided by analytical frameworks and learning objectives
Skill by Mr-Wolv
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/mr-wolv-autonomous-learner
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/autonomous-learner
What's inside
- Skill name
autonomous-learner- Description
- Self-directed knowledge acquisition guided by analytical frameworks and learning objectives
- License
- MIT
- Instructions
- 48 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Autonomous Learner Self-directed knowledge acquisition engine for exploring and mastering new domains. ## When to use - You need to learn a new technology or concept - You want to explore a domain systematically - Preparing for a project in an unfamiliar area ## Instructions 1. **Define learning objectives** — what specifically do you need to understand? What do you want to be able to do? 2. **Gauge current level** — what do you already know? What are the gaps? 3. **Identify authoritative sources** — official docs, academic papers, recognized experts 4. **Create a learning path** — progressive topics from fundamentals to advanced 5. **For each topic**: - Read/watch the source material - Practice with examples - Test understanding (explain in your own words, apply to a different problem) 6. **Synthesize** — connect new knowledge with existing knowledge ## Learning path structure ```markdown ## Learning Path: [Topic] ### Level 1: Fundamentals - [ ] [Resource] — what, why, basic concepts - [ ] Mini-exercise: [short practical task] ### Level 2: Practical - [ ] [Resource] — common patterns, best practices - [ ] Exercise: [longer task applying the concepts] ### Level 3: Advanced - [ ] [Resource] — edge cases, internals, tradeoffs - [ ] Project: [realistic project that demonstrates mastery] ``` ## Techniques - **Feynman technique**: explain the concept in simple terms — if you can't, you don't understand it well enough - **Spaced repetition**: review at increasing intervals to solidify long-term memory - **Active recall**: test yourself instead of re-reading - **Deliberate practice**: work on tasks at the edge of your ability
From Mr-Wolv/Skill_Playground_Bakr101_2026, MIT.
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Signals
- Stars
- 0
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- Last commit (this folder)
- Jul 7, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from Mr-Wolv
- 0.479score
architecture-patterns
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Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design.
SkillCFreshness: CClaude Code +9 - 0.476score
api-contract-testing
Mr-Wolv
Verify API contracts between services to ensure compatibility and prevent breaking changes.
SkillCFreshness: CClaude Code +9 - 0.474score
architecture-review
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Review an existing or proposed architecture against requirements, boundaries, risks, operability, and tradeoffs.
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assumption-and-bias-check
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Surface hidden assumptions, cognitive biases, blind spots, and overconfidence before a decision or output stands.
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Cite this page
autonomous-learner skill. Agent Mart. https://agentmart.dev/skills/mr-wolv-autonomous-learner (last updated 2026-10-24)