Skill: koriyoshi2041/fabric-bridge (version 1.0.0)

--- name: fabric-bridge description: "Run Fabric AI patterns for text transformation, analysis, and content creation. Use when the user asks to use a Fabric pattern, extract wisdom, analyze claims, improve writing, summarize with Fabric, or mentions 'fabric' CLI. Supports 242+ patterns for tasks like content analysis, writing improvement, code review, threat modeling, and structured extraction." homepage: https://github.com/danielmiessler/fabric metadata: {"clawdbot":{"emoji":"🧶","requires":{"bins":["fabric-ai"]},"install":[{"id":"brew","kind":"brew","formula":"fabric-ai","bins":["fabric-ai"],"label":"Install Fabric AI (brew)"}]}} --- # Fabric Bridge Run Fabric AI patterns via the `fabric-ai` CLI. Each pattern is a reusable system prompt for a specific task. > See `references/popular-patterns.md` for a curated list of high-quality patterns by category. ## Important Notes - The command is **`fabric-ai`**, not `fabric`. - First-time setup: run `fabric-ai -S` to configure API keys. - If pattern list is empty: run `fabric-ai -U` to update patterns. - Use `-s` (stream) for most calls to avoid long waits. ## Core Commands ### Basic usage ```bash echo "input text" | fabric-ai -p <pattern> ``` ### Stream output (recommended) ```bash echo "input text" | fabric-ai -p <pattern> -s ``` ### Process a YouTube video ```bash fabric-ai -y "https://youtube.com/watch?v=..." -p extract_wisdom -s ``` ### Process a web page ```bash fabric-ai -u "https://example.com/article" -p summarize -s ``` ### Specify model ```bash echo "input" | fabric-ai -p <pattern> -m gpt-4o ``` ### Chinese output ```bash echo "input" | fabric-ai -p <pattern> -g zh -s ``` ### Chain patterns (pipe output to next pattern) ```bash echo "input" | fabric-ai -p extract_wisdom | fabric-ai -p summarize ``` ### Reasoning strategy (requires setup) ```bash echo "input" | fabric-ai -p <pattern> --strategy cot -s ``` ### Process an image (multimodal) ```bash echo "describe this image" | fabric-ai -p <pattern> -a /path/to/image.png -s ``` ### Use context ```bash echo "input" | fabric-ai -p <pattern> -C my_context -s ``` ### Session continuity ```bash echo "input" | fabric-ai -p <pattern> --session my_session -s ``` ### Save output to file ```bash echo "input" | fabric-ai -p extract_wisdom -o output.md ``` ### Copy output to clipboard ```bash echo "input" | fabric-ai -p extract_wisdom -c ``` ### Dry run (preview without calling API) ```bash fabric-ai -p <pattern> --dry-run ``` ### List all available patterns ```bash fabric-ai -l ``` ## Template Variables Patterns can contain `{{variable}}` placeholders. Pass values with `-v`: ```bash # Single variable echo "input" | fabric-ai -p <pattern> -v="#role:expert" # Multiple variables echo "input" | fabric-ai -p <pattern> -v="#role:expert" -v="#points:30" ``` ## Custom Patterns Create custom patterns at `~/.config/fabric/patterns/<name>/system.md`. Each pattern directory contains a `system.md` file with the system prompt. ## Feeding File Content ```bash cat file.txt | fabric-ai -p <pattern> -s cat file1.md file2.md | fabric-ai -p <pattern> -s ``` ## Tips - Prefer `-s` (stream) for interactive use — output appears incrementally. - Chain patterns for multi-step processing (extract → summarize → translate). - Use `-g zh` when the user wants Chinese output. - Use `-o file.md` to save output, `-c` to copy to clipboard. - Use `--dry-run` to inspect what will be sent before making API calls. - Run `fabric-ai -U` periodically to get new community patterns.

path: references/popular-patterns.md
content: # Popular Fabric Patterns

Curated high-quality patterns by category. Run with `fabric-ai -p <name> -s`.

## Content Analysis & Extraction

- `extract_wisdom` — Flagship pattern. Extract IDEAS, INSIGHTS, QUOTES, HABITS, FACTS from any content.
- `analyze_claims` — Fact-check framework. Rate claims by evidence strength.
- `find_logical_fallacies` — Detect logical fallacies with detailed explanations (222 rules).
- `summarize` — Concise content summary.
- `extract_main_idea` — Pull the core argument from text.
- `extract_article_wisdom` — Article-focused wisdom extraction.
- `label_and_rate` — Rate content quality and assign labels.

## Writing & Improvement

- `improve_writing` — Refine prose for clarity and impact.
- `improve_prompt` — Expert-level prompt engineering (518-line system prompt). Rewrite LLM prompts.
- `improve_academic_writing` — Academic tone and structure polish.
- `write_essay` — Generate structured essays from ideas.
- `create_summary` — Create executive summaries.

## Code

- `review_code` — Principal Engineer code review. Bugs, style, architecture.
- `explain_code` — Explain what code does in plain language.
- `write_pull-request` — Generate PR descriptions from diffs.
- `create_command` — Generate shell commands from natural language.

## Creation & Generation

- `create_keynote` — TED-quality keynote outline.
- `create_micro_summary` — Ultra-short summaries (tweet-length).
- `create_markmap_visualization` — Generate mind maps in markmap format.
- `create_quiz` — Generate quiz questions from content.

## Security & Threats

- `create_threat_scenarios` — Professional threat modeling (STRIDE-based).
- `create_threat_model` — Threat model documentation.
- `analyze_threat_report` — Parse threat intelligence reports.
- `create_security_update` — Summarize security advisories.

## Learning & Education

- `explain_terms` — Define technical terms in simple language.
- `extract_predictions` — Pull predictions and forecasts from content.
- `analyze_paper` — Academic paper analysis and critique.
- `rate_content` — Evaluate content quality on multiple axes.

## Business & Product

- `create_hormozi_offer` — $100M Offers methodology. Build irresistible offers (338-line framework).
- `analyze_product` — Product analysis and positioning.
- `create_user_story` — Generate user stories from requirements.
- `analyze_presentation` — Evaluate slide decks and presentations.

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Run `fabric-ai -l` for the full list of 242+ patterns.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)