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 agentmods add skills/boshi-xixixi/traeskill/code-exemplars-blueprint-generatornpx skills add boshi-xixixi/TraeSkill --skill code-exemplars-blueprint-generatorgit 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/code-exemplars-blueprint-generator)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/code-exemplars-blueprint-generator"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/code-exemplars-blueprint-generator.svg" alt="Measured on agentmods" 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 | $0.00075 | $0.01368 |
| Opus 5 | $0.00037 | $0.00684 |
| Sonnet 5 | $0.00015 | $0.00274 |
| Haiku 4.5 | $0.00007 | $0.00137 |
Grade A, and why
code-exemplars-blueprint-generator 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
1 near-identical copy found in the catalogue:
- code-exemplars-blueprint-generator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Exemplars Blueprint Generator
Configuration Variables
${PROJECT_TYPE="Auto-detect|.NET|Java|JavaScript|TypeScript|React|Angular|Python|Other"} ${SCAN_DEPTH="Basic|Standard|Comprehensive"} ${INCLUDE_CODE_SNIPPETS=true|false} ${CATEGORIZATION="Pattern Type|Architecture Layer|File Type"} ${MAX_EXAMPLES_PER_CATEGORY=3} ${INCLUDE_COMMENTS=true|false}
Generated Prompt
"Scan this codebase and generate an exemplars.md file that identifies high-quality, representative code examples. The exemplars should demonstrate our coding standards and patterns to help maintain consistency. Use the following approach:
1. Codebase Analysis Phase
- ${PROJECT_TYPE == "Auto-detect" ? "Automatically detect primary programming languages and frameworks by scanning file extensions and configuration files" :
Focus on ${PROJECT_TYPE} code files} - Identify files with high-quality implementation, good documentation, and clear structure
- Look for commonly used patterns, architecture components, and well-structured implementations
- Prioritize files that demonstrate best practices for our technology stack
- Only reference actual files that exist in the codebase - no hypothetical examples
2. Exemplar Identification Criteria
- Well-structured, readable code with clear naming conventions
- Comprehensive comments and documentation
- Proper error handling and validation
- Adherence to design patterns and architectural principles
- Separation of concerns and single responsibility principle
- Efficient implementation without code smells
- Representative of our standard approaches
3. Core Pattern Categories
${PROJECT_TYPE == ".NET" || PROJECT_TYPE == "Auto-detect" ? `#### .NET Exemplars (if detected)
- Domain Models: Find entities that properly implement encapsulation and domain logic
- Repository Implementations: Examples of our data access approach
- Service Layer Components: Well-structured business logic implementations
- Controller Patterns: Clean API controllers with proper validation and responses
- Dependency Injection Usage: Good examples of DI configuration and usage
- Middleware Components: Custom middleware implementations
- Unit Test Patterns: Well-structured tests with proper arrangement and assertions` : ""}
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 · 127 lines · 75 tokens per session scan A 005a023337c8
code-exemplars-blueprint-generator is a skill published in the GitHub repository boshi-xixixi/TraeSkill (261 stars, last pushed 3mo ago), licensed MIT. It adds 75 tokens to every session and 1,368 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
gpt-image-2
面向 GPT Image 2 的图像生成 / 编辑技能。可在 3 种环境下使用:(A) Garden 本地模式,通过 OpenAI 兼容接口直接出图并落盘;(B) Host-Native 模式,把本 Skill 当作提示词工程指引,把渲染好的 prompt 交给宿主 Agent 自带的图像工具出图;(C) Advisor 模式,宿主无任何图像工具时退化为高质量 prompt 顾问。涵盖 18 大类、80+ 个结构化模板,覆盖海报 / UI / 产品 / 信息图 / 学术图 / 技术架构图 / 漫画 / 头像 / 流程板 / 电影分镜 / IP 周边 / 编辑工作流等场景。.
ss-reference
Compile screenshots, URLs, Figma exports, or an existing UI into a project-local StyleSeed output grammar with evidence, tokens, confidence, anti-patterns, and a validation screen. Use when the user supplies a design reference that StyleSeed does not already model.
azure-machine-learning
Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AutoML, Prompt Flow/Agent, managed online endpoints, feature…
azure-data-factory
Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building ADF pipelines with IR/SSIS, triggers, data flows, SAP/SaaS…
azure-databricks
Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Unity Catalog, Lakeflow pipelines, Lakebase, Delta Lake, Databricks…
azure-hdinsight
Expert knowledge for Azure HDInsight development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with HDInsight Spark/Hive/Kafka/HBase clusters, Ambari/Oozie pipelines…