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continuous-learning-agent

Self-improvement patterns for AI agents to learn from feedback, errors, and successful patterns across sessions

Skill by 4444J99

IndexedMIT★ 17
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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/4444j99-continuous-learning-agent

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

Self-improvement patterns for AI agents to learn from feedback, errors, and successful patterns across sessions

Use cases: agent-building

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Secrets
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Install scripts
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/continuous-learning-agent

What's inside

Skill name
continuous-learning-agent
Description
Self-improvement patterns for AI agents to learn from feedback, errors, and successful patterns across sessions
License
MIT
Metadata
source: affaan-m/everything-claude-code · category: agent-improvement · adapted-by: ai-skills
Instructions
405 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • learning-resources.md · references/

SKILL.md (first lines)

# Continuous Learning Agent

A meta-skill that enables AI agents to learn from experience and improve over time by separating journaled memory from policy changes that alter future behavior.

## Core Concept

Traditional agents reset completely between sessions. This skill treats memory and learning as related but distinct operations:
- **Journal / memory** records what happened, what was tried, and what evidence exists.
- **Learning / policy** changes what the agent will do next time for a recognizable event class.

Do not call a session log, decision journal, or context note "learning" unless it produces a policy delta, threshold revision, banned move, or acquired pattern that changes future behavior.

## Learning Mechanisms

Every learning loop has two layers:

1. **Journal layer**: episodic or semantic records used for auditability.
2. **Policy layer**: compact behavioral deltas used to improve performance on future tasks.

The journal layer is optional when an existing memory system already covers it. The policy layer is mandatory for this skill.

### 1. Error Pattern Recognition

After each error, first document the event if no existing memory system already captures it:

```markdown
## Error Log Entry

**Date**: 2026-01-30
**Context**: Implementing user authentication
**Error**: TypeError: Cannot read property 'id' of undefined
**Root Cause**: Missing null check before accessing user object
**Fix**: Added optional chaining: user?.id
**Pattern**: Always validate object existence before property access
**Prevention**: Add TypeScript strict null checks
```

Then extract the policy delta:

```markdown
## Policy Delta: [Short Title]

**Date**: 2026-01-30
**Event Class**: Accessing nested properties on possibly absent objects
**Prior Policy**: Read nested properties directly after optimistic object construction.
**Failure Mode**: Undefined objects caused runtime TypeErrors.
**Revised Policy**: Validate object existence or use typed optional access before nested reads.
**Trigger**: Any code path receiving user, API, database, or tool-returned objects.
**Propagation Target**: Project AGENTS.md, test helper, lint rule, or skill source.
**Verification**: Add or run a test that fails under the prior policy and passes under the revised policy.
```

### 2. Success Pattern Collection

After successful implementations, record both the reusable pattern and the policy form that lets future agents apply it without replaying the whole story:

```markdown
## Success Pattern

**Task**: Add pagination to API endpoint
**Approach**: Cursor-based pagination with encoded tokens
**Why It Worked**: Handles large datasets efficiently, stateless
**Reusable Pattern**: 
- Use cursor tokens instead of offset/limit
- Encode cursor with base64
- Include hasNext/hasPrevious flags
- Return next/previous cursor in response

**Code Template**:
\`\`\`typescript
interface PaginatedResponse<T> {
  data: T[];
  cursor: {
    next: string | null;
    previous: string | null;
  };
}
\`\`\`
```

```markdown
## Acquired Pattern

**Event Class**: API endpoints returning large ordered datasets
**Revised Policy**: Prefer cursor tokens over offset pagination unless the product explicitly needs random page access.
**Trigger**: New list endpoint over a growing table or external API collection.
**Verification**: Exercise first page, next page, empty page, and invalid cursor behavior.
```

### 3. Feedback Integration

Use the active project's policy surface when one exists. Examples:
- `AGENTS.md`, `CLAUDE.md`, `GEMINI.md`, or other runtime instruction files for enduring operating rules.
- `.claude/policy/`, `.codex/policy/`, `.agents/policy/`, or equivalent for local policy deltas.
- A governed memory or feedback-note repository when the user has established one.

Only create a local journal directory when there is no stronger existing memory surface:

```bash
mkdir -p .claude/journal .claude/policy/deltas
```

Store journal records and policy deltas separately:

```
.claude/journal/
  patterns/
    authentication.md
    database-queries.md
    error-handling.md
  mistakes/
    common-bugs.md
    performance-issues.md
  preferences/
    code-style.md
    testing-approach.md
    naming-conventions.md
.claude/policy/
  deltas/

From 4444J99/a-i--skills, MIT.

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Stars
17
Forks
9
Open issues
6
Last commit (this folder)
May 17, 2026 (5 months ago)
Last release
Feb 6, 2026 (8 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

continuous-learning-agent skill. Agent Mart. https://agentmart.dev/skills/4444j99-continuous-learning-agent (last updated 2026-10-24)