Execute any of 240+ specialized prompt patterns natively across Extraction, Summarization, Analysis, Creation, Improvement, Security, Rating. Common: extract_wi
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请阅读 https://ai.atlankj.com/install/asset/gh-fabric-61ae00bc91f8 ,按照其中的说明把「Fabric」安装到你(当前 AI 工具)中。执行前先告诉我将运行的命令和写入的位置,等我确认。
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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.