/claude-reflect
Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
$ npx -y skills add BayramAnnakov/claude-reflect --skill claude-reflect --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/claude-reflect
Context preview
The summary Claude sees to decide when to auto-load this skill.
Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
SKILL.md
claude-reflect.SKILL.mdname: claude-reflect
description: Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
Claude Reflect - Self-Learning System
A two-stage system that helps Claude Code learn from user corrections.
How It Works
**Stage 1: Capture (Automatic)** Hooks detect correction patterns ("no, use X", "actually...", "use X not Y") and queue them to `~/.claude/learnings-queue.json`.
**Stage 2: Process (Manual)** User runs `/reflect` to review and apply queued learnings to CLAUDE.md files.
Available Commands
| Command | Purpose | |---------|---------| | `/reflect` | Process queued learnings with human review | | `/reflect --scan-history` | Scan past sessions for missed learnings | | `/reflect --dry-run` | Preview changes without applying | | `/reflect-skills` | Discover skill candidates from repeating patterns | | `/skip-reflect` | Discard all queued learnings | | `/view-queue` | View pending learnings without processing |
When to Remind Users
Remind users about `/reflect` when:
- They complete a feature or meaningful work unit
- They make corrections you should remember for future sessions
- They explicitly say "remember this" or similar
- Context is about to compact and queue has items
Correction Detection Patterns
High-confidence corrections:
- Tool rejections (user stops an action with guidance)
- "no, use X" / "don't use Y"
- "actually..." / "I meant..."
- "use X not Y" / "X instead of Y"
- "remember:" (explicit marker)
Learning Destinations
- `~/.claude/CLAUDE.md` - Global learnings (model names, general patterns)
- `./CLAUDE.md` - Project-specific learnings (conventions, tools, structure)
- `./CLAUDE.local.md` - Personal learnings (machine-specific, gitignored)
- `./.claude/rules/*.md` - Modular rules with optional path-scoping
- `~/.claude/rules/*.md` - Global modular rules
- `~/.claude/projects/<project>/memory/*.md` - Auto memory (low-confidence, exploratory)
- `commands/*.md` - Skill improvements (corrections during skill execution)
Example Interaction
User: no, use gpt-5.1 not gpt-5 for reasoning tasks
Claude: Got it, I'll use gpt-5.1 for reasoning tasks.
[Hook captures this correction to queue]
User: /reflect
Claude: Found 1 learning queued. "Use gpt-5.1 for reasoning tasks"
Scope: global
Apply to ~/.claude/CLAUDE.md? [y/n]Read more
name: claude-reflect description: Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something for future sessions.
Claude Reflect - Self-Learning System
A two-stage system that helps Claude Code learn from user corrections.
How It Works
**Stage 1: Capture (Automatic)** Hooks detect correction patterns ("no, use X", "actually...", "use X not Y") and queue them to `~/.claude/learnings-queue.json`.
**Stage 2: Process (Manual)** User runs `/reflect` to review and apply queued learnings to CLAUDE.md files.
Available Commands
| Command | Purpose | |---------|---------| | `/reflect` | Process queued learnings with human review | | `/reflect --scan-history` | Scan past sessions for missed learnings | | `/reflect --dry-run` | Preview changes without applying | | `/reflect-skills` | Discover skill candidates from repeating patterns | | `/skip-reflect` | Discard all queued learnings | | `/view-queue` | View pending learnings without processing |
When to Remind Users
Remind users about `/reflect` when:
- They complete a feature or meaningful work unit
- They make corrections you should remember for future sessions
- They explicitly say "remember this" or similar
- Context is about to compact and queue has items
Correction Detection Patterns
High-confidence corrections:
- Tool rejections (user stops an action with guidance)
- "no, use X" / "don't use Y"
- "actually..." / "I meant..."
- "use X not Y" / "X instead of Y"
- "remember:" (explicit marker)
Learning Destinations
- `~/.claude/CLAUDE.md` - Global learnings (model names, general patterns)
- `./CLAUDE.md` - Project-specific learnings (conventions, tools, structure)
- `./CLAUDE.local.md` - Personal learnings (machine-specific, gitignored)
- `./.claude/rules/*.md` - Modular rules with optional path-scoping
- `~/.claude/rules/*.md` - Global modular rules
- `~/.claude/projects/<project>/memory/*.md` - Auto memory (low-confidence, exploratory)
- `commands/*.md` - Skill improvements (corrections during skill execution)
Example Interaction
User: no, use gpt-5.1 not gpt-5 for reasoning tasks
Claude: Got it, I'll use gpt-5.1 for reasoning tasks.
[Hook captures this correction to queue]
User: /reflect
Claude: Found 1 learning queued. "Use gpt-5.1 for reasoning tasks"
Scope: global
Apply to ~/.claude/CLAUDE.md? [y/n]A self-learning system for Claude Code that captures corrections and discovers workflow patterns — turning them into permanent memory and reusable skills.