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.
npx skills add boshi-xixixi/TraeSkill --skill create-llmsgit clone --depth 1 https://github.com/boshi-xixixi/TraeSkillWrote 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/boshi-xixixi/traeskill/create-llms)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/create-llms"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/create-llms/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/boshi-xixixi/traeskill/create-llms"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/create-llms.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 | $0.01475 |
| Opus 5 | $0.00015 | $0.00737 |
| Sonnet 5 | $0.00006 | $0.00295 |
| Haiku 4.5 | $0.00003 | $0.00147 |
Grade A, and why
create-llms 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 6d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- create-llms — 100% identical, 0 lines differ
- create-llms — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create LLMs.txt File from Repository Structure
Create a new llms.txt file from scratch in the root of the repository following the official llms.txt specification at https://llmstxt.org/. This file provides high-level guidance to large language models (LLMs) on where to find relevant content for understanding the repository's purpose and specifications.
Primary Directive
Create a comprehensive llms.txt file that serves as an entry point for LLMs to understand and navigate the repository effectively. The file must comply with the llms.txt specification and be optimized for LLM consumption while remaining human-readable.
Analysis and Planning Phase
Before creating the llms.txt file, you must complete a thorough analysis:
Step 1: Review llms.txt Specification
- Review the official specification at https://llmstxt.org/ to ensure full compliance
- Understand the required format structure and guidelines
- Note the specific markdown structure requirements
Step 2: Repository Structure Analysis
- Examine the complete repository structure using appropriate tools
- Identify the primary purpose and scope of the repository
- Catalog all important directories and their purposes
- List key files that would be valuable for LLM understanding
Step 3: Content Discovery
- Identify README files and their locations
- Find documentation files (
.mdfiles in/docs/,/spec/, etc.) - Locate specification files and their purposes
- Discover configuration files and their relevance
- Find example files and code samples
- Identify any existing documentation structure
Step 4: Create Implementation Plan
Based on your analysis, create a structured plan that includes:
- Repository purpose and scope summary
- Priority-ordered list of essential files for LLM understanding
- Secondary files that provide additional context
- Organizational structure for the llms.txt file
Implementation Requirements
Format Compliance
The llms.txt file must follow this exact structure per the specification:
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.
- 6d ago First seen · 211 lines · 31 tokens per session scan A 713aff883a2c
create-llms is a skill published in the GitHub repository boshi-xixixi/TraeSkill (262 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 1,475 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
<skill-name>
A template for defining a coding-agent skill, including its title, trigger situations, overview, workflow, common mistakes, and optional references.
spec-writing
A method for writing a software specification: a document that records decisions, reasons, boundaries, and ways to judge whether implementation succeeded. It first checks whether important unknowns require user clarification or technical research.
onboarding-unknown-codebase
A method for quickly understanding an unfamiliar codebase, meaning a software project whose structure and behavior you do not yet know. It builds a project map by examining overview files, directories, and one main execution path.
commit-message
A guide for writing clear, traceable Git commit messages using the Conventional Commits format, which labels changes such as features, bug fixes, documentation, and refactoring.
clarifying-questions
Guidance for clarifying vague or assumption-heavy requests before making changes.
debugging
A systematic method for finding the underlying cause of a software bug by observing the failure, forming a hypothesis, and testing it.