Borrowing it
Nothing to install: this file belongs to lanesket/llm.log. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lanesket/llm.log/main/.agents/skills/extract/SKILL.mdgit clone --depth 1 https://github.com/lanesket/llm.logWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lanesket/llm.log/extract)<a href="https://agentmods.dev/skills/lanesket/llm.log/extract"><img src="https://agentmods.dev/badge/skills/lanesket/llm.log/extract/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lanesket/llm.log/extract"><img src="https://agentmods.dev/badge/skills/lanesket/llm.log/extract.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.00785 |
| Opus 5.5 | $0.00012 | $0.00314 |
| Sonnet 5.5 | $0.00006 | $0.00157 |
| Haiku 4.5 | $0.00003 | $0.00078 |
Grade A, and why
extract scanned grade A with 0 findings 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 1mo ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to extract — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identify reusable patterns, components, and design tokens, then extract and consolidate them into the design system for systematic reuse.
Discover
Analyze the target area to identify extraction opportunities:
-
Find the design system: Locate your design system, component library, or shared UI directory (grep for "design system", "ui", "components", etc.). Understand its structure:
- Component organization and naming conventions
- Design token structure (if any)
- Documentation patterns
- Import/export conventions
CRITICAL: If no design system exists, ask before creating one. Understand the preferred location and structure first.
-
Identify patterns: Look for:
- Repeated components: Similar UI patterns used multiple times (buttons, cards, inputs, etc.)
- Hard-coded values: Colors, spacing, typography, shadows that should be tokens
- Inconsistent variations: Multiple implementations of the same concept (3 different button styles)
- Reusable patterns: Layout patterns, composition patterns, interaction patterns worth systematizing
-
Assess value: Not everything should be extracted. Consider:
- Is this used 3+ times, or likely to be reused?
- Would systematizing this improve consistency?
- Is this a general pattern or context-specific?
- What's the maintenance cost vs benefit?
Plan Extraction
Create a systematic extraction plan:
- Components to extract: Which UI elements become reusable components?
- Tokens to create: Which hard-coded values become design tokens?
- Variants to support: What variations does each component need?
- Naming conventions: Component names, token names, prop names that match existing patterns
- Migration path: How to refactor existing uses to consume the new shared versions
IMPORTANT: Design systems grow incrementally. Extract what's clearly reusable now, not everything that might someday be reusable.
Extract & Enrich
Build improved, reusable versions:
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.
- 1mo ago First seen · 94 lines · 31 tokens per session scan A 55308221b325
extract is a skill published in the GitHub repository lanesket/llm.log (25 stars, last pushed 6mo ago), licensed MIT. It adds 31 tokens to every session and 785 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to extract, differing in 11 lines, and is treated as a copy.
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