Apply over 240 reusable prompt templates to summarize content, extract insights, improve writing, review code, or analyze security threats. It reads each template directly and returns results in the format defined for that task.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Execute any of 240+ specialized prompt patterns natively across Extraction, Summarization, Analysis, Creation, Improvement, Security, Rating. Common: extract_wisdom, create_threat_model, analyze_claims, improve_writing, review_code, mermaid, youtube_summary. CLI used only for YouTube transcript (-y) and URL fallback (-u). Two workflows: ExecutePattern, UpdatePatterns. USE WHEN fabric, fabric pattern, run fabric, update patterns, threat model, analyze claims, improve writing, review code, mermaid, STRIDE, sigma rules. NOT FOR multi-agent investigation (Research) or content-adaptive extraction (ExtractWisdom).
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Before executing, check for user customizations at:
~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Fabric/
If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.
When executing a workflow, do BOTH:
Send voice notification:
curl -s -X POST http://localhost:31337/notify \
-H "Content-Type: application/json" \
-d '{"message": "Running the WORKFLOWNAME workflow in the Fabric skill to ACTION"}' \
> /dev/null 2>&1 &
Output text notification:
Running the **WorkflowName** workflow in the **Fabric** skill to ACTION...
Full documentation: ~/.claude/LIFEOS/DOCUMENTATION/Notifications/NotificationSystem.md
Runs any of 240+ specialized prompt patterns across extraction, summarization, analysis, creation, improvement, security, and rating. Common ones: extract_wisdom, create_threat_model, analyze_claims, improve_writing, review_code, mermaid, youtube_summary. Patterns run natively — LifeOS reads the pattern's system.md and applies it directly, no CLI round-trip. The fabric CLI is only used for YouTube transcripts (-y) and URL fallback (-u).
Good prompts are scattered, hard to remember, and easy to rewrite badly from scratch each time. You want a threat model, a claims analysis, or a clean summary, but reconstructing the right prompt every time is slow and inconsistent. Calling an external CLI for each one adds latency and a dependency. This skill keeps 240+ proven patterns on hand and applies them directly as prompts, so the right structured prompt is one pattern name away.
A prompt pattern system providing 240+ specialized patterns for content analysis, extraction, summarization, threat modeling, and transformation.
Patterns Location: Patterns/
| Workflow | Trigger | File |
|---|---|---|
| ExecutePattern | "use fabric", "run pattern", "apply pattern", "extract wisdom", "summarize", "analyze with fabric" | Workflows/ExecutePattern.md |
| UpdatePatterns | "update fabric", "update patterns", "sync fabric", "pull patterns" | Workflows/UpdatePatterns.md |
Example 1: Extract wisdom from content
User: "Use fabric to extract wisdom from this article"
-> Invokes ExecutePattern workflow
-> Selects extract_wisdom pattern
-> Reads Patterns/extract_wisdom/system.md
-> Applies pattern to content
-> Returns structured IDEAS, INSIGHTS, QUOTES, etc.
Example 2: Update patterns
User: "Update fabric patterns"
-> Invokes UpdatePatterns workflow
-> Runs git pull from upstream fabric repository
-> Syncs patterns to local Patterns/ directory
-> Reports pattern count
Example 3: Create threat model
User: "Use fabric to create a threat model for this API"
-> Invokes ExecutePattern workflow
-> Selects create_threat_model pattern
-> Applies STRIDE methodology
-> Returns structured threat analysis
Instead of calling fabric -p pattern_name, LifeOS executes patterns natively:
Patterns/{pattern_name}/system.mdOnly use fabric command for:
-y URL - YouTube transcript extraction-u URL - URL content fetching (when native fetch fails)| Intent | Pattern | Description |
|---|---|---|
| Extract insights | extract_wisdom | IDEAS, INSIGHTS, QUOTES, HABITS |
| Summarize | summarize | General summary |
| 5-sentence summary | create_5_sentence_summary | Ultra-concise |
| Threat model | create_threat_model | Security threat analysis |
| Analyze claims | analyze_claims | Fact-check claims |
| Improve writing | improve_writing | Writing enhancement |
| Code review | review_code | Code analysis |
| Main idea | extract_main_idea | Core message extraction |
Browse the Patterns/ directory for the complete list of 240+ patterns organized by category.
How it works:
User Request → Pattern Selection → Read system.md → Apply → Return Results
Pattern Structure:
Patterns/
├── extract_wisdom/
│ └── system.md # The prompt instructions
├── summarize/
│ └── system.md
├── create_threat_model/
│ └── system.md
└── ...240+ patterns
Each pattern's system.md contains the full prompt that defines:
| Category | Count | Examples |
|---|---|---|
| Extraction | 30+ | extract_wisdom, extract_insights, extract_main_idea |
| Summarization | 20+ | summarize, create_5_sentence_summary, youtube_summary |
| Analysis | 35+ | analyze_claims, analyze_code, analyze_threat_report |
| Creation | 50+ | create_threat_model, create_prd, create_mermaid_visualization |
| Improvement | 10+ | improve_writing, improve_prompt, review_code |
| Security | 15 | create_stride_threat_model, create_sigma_rules, analyze_malware |
| Rating | 8 | rate_content, judge_output, rate_ai_response |
-y) and URL fetching (-u)| Path | Purpose |
|---|---|
Patterns/ | Local pattern storage (240+) |
Workflows/ | Execution workflows |
fabric -y URL for YouTube extraction — don't scrape YouTube pages. fabric handles transcript extraction natively.extract_wisdom not extractwisdom. Check fabric --list if unsure.After completing any workflow, append a single JSONL entry:
echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Fabric","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl
Replace WORKFLOW_USED with the workflow executed, 8_WORD_SUMMARY with a brief input description, and SECONDS with approximate wall-clock time. Log status: "error" if the workflow failed.
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Compare a component's own design needs with the goals of the larger system through three structured passes. The result identifies conflicts, recommends an appropriate scope, and assesses whether the proposed design fits the whole.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Find quotes that fit a text’s themes, with attribution and a short reason for each suggestion. Add sourced quotes, research thinkers, and organize a quote collection by theme and usage history to avoid repetition.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Collect social posts, business listings, and product reviews through Apify actors, ready-made data collection tools. Filter results by date, rating, or engagement in code before sending relevant records to the AI for research or lead generation.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Create blog illustrations, technical diagrams, comparisons, comics, and icons through workflows tailored to each format. The skill guides composition, color, and model selection, then stages generated images for review before they are used in a project.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Find arXiv papers by topic, research category, or paper ID. Retrieve titles, authors, abstracts, and links, check new submissions, and add AlphaXiv's AI-generated overviews when available to help decide which papers to read.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Clean spoken recordings by transcribing words, identifying filler and false starts, and joining cuts with short audio fades. Preview proposed edits, reduce breaths, repair noise-gate clicks, and optionally use Cleanvoice for additional cloud processing.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.