fabric

A pattern-selection tool for Fabric, a command-line collection of specialised prompts for tasks such as summarising, analysing, extracting information, and threat modelling.

In plain words
What is it for?
Use it to process articles, videos, papers, code, debates, malware, or other content into summaries, insights, reviews, reports, or improved writing.
Why use it?
It helps choose a suitable prompt pattern when the requested transformation or analysis is not obvious.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/danielmiessler/paiplugin/fabric
Any agent
npx skills add danielmiessler/PAIPlugin --skill fabric
Clone the repo
git clone --depth 1 https://github.com/danielmiessler/PAIPlugin

Made for: Claude Code, Codex.

Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,519 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00060 $0.03519
Opus 5 $0.00030 $0.01759
Sonnet 5 $0.00012 $0.00704
Haiku 4.5 $0.00006 $0.00352

Measured 2d ago against content hash 7beb9c557808, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fabric scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (fabric-repo/completions/fabric.bash, fabric-repo/completions/setup-completions.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s "https://..." | fabric -p analyze_claims
skills/fabric/SKILL.md · 379 lines

How it starts

The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Fabric Skill

Setup Check - Fabric Repository

IMPORTANT: Before using this skill, verify the Fabric repository is available:

# Check if Fabric repo exists
if [ ! -d "$HOME/.claude/skills/fabric/fabric-repo" ]; then
  echo "Fabric repository not found. Cloning..."
  cd "$HOME/.claude/skills/fabric"
  git clone https://github.com/danielmiessler/fabric.git fabric-repo
  echo "Fabric repository cloned successfully."
else
  echo "Fabric repository found at $HOME/.claude/skills/fabric/fabric-repo"
fi

If the repo doesn't exist, clone it immediately before proceeding with any pattern selection.

When to Activate This Skill

Primary Use Cases:

  • "Create a threat model for..."
  • "Summarize this article/video/paper..."
  • "Extract wisdom/insights from..."
  • "Analyze this [code/malware/claims/debate]..."
  • "Improve my writing/code/prompt..."
  • "Create a [visualization/summary/report]..."
  • "Rate/review/judge this content..."

The Goal: Select the RIGHT pattern from 242+ available patterns based on what you're trying to accomplish.

🎯 Pattern Selection Strategy

When a user requests Fabric processing, follow this decision tree:

1. Identify Intent Category

Threat Modeling & Security:

  • Threat model → create_threat_model or create_stride_threat_model
  • Threat scenarios → create_threat_scenarios
  • Security update → create_security_update
  • Security rules → create_sigma_rules, write_nuclei_template_rule, write_semgrep_rule
  • Threat analysis → analyze_threat_report, analyze_threat_report_trends

Summarization:

  • General summary → summarize
  • 5-sentence summary → create_5_sentence_summary
  • Micro summary → create_micro_summary or summarize_micro
  • Meeting → summarize_meeting
  • Paper/research → summarize_paper
  • Video/YouTube → youtube_summary
  • Newsletter → summarize_newsletter
  • Code changes → summarize_git_changes or summarize_git_diff

Wisdom Extraction:

  • General wisdom → extract_wisdom
  • Article wisdom → extract_article_wisdom
  • Book ideas → extract_book_ideas
  • Insights → extract_insights or extract_insights_dm
  • Main idea → extract_main_idea
  • Recommendations → extract_recommendations
  • Controversial ideas → extract_controversial_ideas

Read the full file on GitHub · 379 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 379 lines · 60 tokens per session scan A 7beb9c557808

Subscribe to this mod's changes

fabric is a skill published in the GitHub repository danielmiessler/PAIPlugin (56 stars, last pushed 9mo ago), licensed MIT. It adds 60 tokens to every session and 3,519 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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