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continuous-learning
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Build a private, self-hosted "watch a movie together in sync" site for two people in different countries — synced play/pause/seek over WebSocket, WebRTC camera+mic call with a coturn TURN relay, a shared laser pointer, ffmpeg smooth-bitrate transcoding so it never buffers, and a token-gated room link. Use when someone wants to watch local video files in sync with a partner/friend remotely, replace Kosmi/Teleparty/Twoseven with something they own, stream a personal movie library over the web with synced controls, or add a video-call overlay to a video player. Triggers: "watch movies together long distance", "sync video playback two browsers", "self-hosted watch party", "Kosmi alternative", "stream my movie to my girlfriend with subtitles and sync".
Subject-aware reframing of horizontal video to vertical (9:16 / 4:5 / 1:1) using YOLOv8 person detection. Per-scene decision between TRACK (crop tightly on subject) and LETTERBOX (scale + black bars) so people stay centered through cuts and motion. Trigger when user says "reframe to vertical", "16:9 to 9:16", "convert landscape to portrait", "shorts crop", "smart crop for TikTok", "vertical YouTube short from horizontal", "auto-crop interview clip", "reframe podcast video", "make this video vertical", or any equivalent intent. Use as a STAGE inside viral-clipper / book-video / channel-breakdown pipelines whenever the source has more than one person on screen or the subject moves around the frame.
Receive and verify Clerk webhooks. Use when setting up Clerk webhook handlers, debugging signature verification, or handling user events like user.created, user.updated, session.created, or organization.created.
Strip AI voice from text — humanize while preserving meaning. Combines glebis's RU/EN/DE/ES/FR/PT/JP/IT diagnostic pipeline with the author's Humanizer Prompt + the Buddha-at-2-AM shipped-manuscript reverse-engineering + the (redacted) Russian-original voice analysis. Use for KDP books, the publication series, marketing copy, or any text that needs to read like a real person wrote it. Pairs with locked voice voice spec.
Invoke DeerFlow — ByteDance's multi-agent research harness running Claude Sonnet 4.6. Use for deep research tasks, multi-step web research, document analysis, and report generation. Spins up a local LangGraph agent that decomposes the task into sub-agents in parallel. Requires the backend to be running first.
Router for all UI/UX/visual design work. Invoke when user types "design conductor", "/design", "design:", or "redesign:". Reads the task, picks the right stages (PLAN → STYLE → BUILD → REFINE → AUDIT), and dispatches the matching sub-skills in order. Don't merge the sub-skills — chain them.
| name | continuous-learning |
| description | Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use. |
| origin | ECC |
Automatically evaluates Claude Code sessions on end to extract reusable patterns that can be saved as learned skills.
~/.claude/skills/learned/This v1 skill is still supported, but continuous-learning-v2 is the preferred path for new installs. Keep v1 when you explicitly want the simpler Stop-hook extraction flow or need compatibility with older learned-skill workflows.
This skill runs as a Stop hook at the end of each session:
~/.claude/skills/learned/Edit config.json to customize:
{
"min_session_length": 10,
"extraction_threshold": "medium",
"auto_approve": false,
"learned_skills_path": "~/.claude/skills/learned/",
"patterns_to_detect": [
"error_resolution",
"user_corrections",
"workarounds",
"debugging_techniques",
"project_specific"
],
"ignore_patterns": [
"simple_typos",
"one_time_fixes",
"external_api_issues"
]
}
| Pattern | Description |
|---|---|
error_resolution | How specific errors were resolved |
user_corrections | Patterns from user corrections |
workarounds | Solutions to framework/library quirks |
debugging_techniques | Effective debugging approaches |
project_specific | Project-specific conventions |
Add to your ~/.claude/settings.json:
{
"hooks": {
"Stop": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
}]
}]
}
}
/learn command - Manual pattern extraction mid-sessionHomunculus v2 takes a more sophisticated approach:
| Feature | Our Approach | Homunculus v2 |
|---|---|---|
| Observation | Stop hook (end of session) | PreToolUse/PostToolUse hooks (100% reliable) |
| Analysis | Main context | Background agent (Haiku) |
| Granularity | Full skills | Atomic "instincts" |
| Confidence | None | 0.3-0.9 weighted |
| Evolution | Direct to skill | Instincts → cluster → skill/command/agent |
| Sharing | None | Export/import instincts |
Key insight from homunculus:
"v1 relied on skills to observe. Skills are probabilistic—they fire ~50-80% of the time. v2 uses hooks for observation (100% reliable) and instincts as the atomic unit of learned behavior."
See: docs/continuous-learning-v2-spec.md for full spec.