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 pjt222/agent-almanac --skill design-serialization-schemagit clone --depth 1 https://github.com/pjt222/agent-almanacWrote 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/pjt222/agent-almanac/design-serialization-schema)<a href="https://agentmods.dev/skills/pjt222/agent-almanac/design-serialization-schema"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/design-serialization-schema/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/pjt222/agent-almanac/design-serialization-schema"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/design-serialization-schema.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.02679 |
| Opus 5.5 | $0.00033 | $0.01072 |
| Sonnet 5.5 | $0.00016 | $0.00536 |
| Haiku 4.5 | $0.00008 | $0.00268 |
Grade A, and why
design-serialization-schema 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.
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Serialization Schema
Create well-versioned serialization schemas that evolve gracefully without breaking consumers.
When to Use
- Defining a new API contract or data interchange format
- Adding fields to an existing schema without breaking consumers
- Migrating between schema versions
- Choosing between schema systems (JSON Schema, Protobuf, Avro)
- Documenting data validation rules for automated enforcement
Inputs
- Required: Data model (entity relationships, field types, constraints)
- Required: Compatibility requirements (who consumes this data, how long must old formats be readable)
- Optional: Existing schema to evolve
- Optional: Performance requirements (validation speed, schema registry integration)
- Optional: Target serialization format (JSON, binary, columnar)
Procedure
Step 1: Choose a Schema System
| System | Format | Strengths | Best For |
|---|---|---|---|
| JSON Schema | JSON | Widely supported, flexible validation | REST APIs, config validation |
| Protocol Buffers | Binary | Compact, fast, strong typing, built-in evolution | gRPC, microservices |
| Apache Avro | Binary/JSON | Schema in data, excellent evolution support | Kafka, data pipelines |
| XML Schema (XSD) | XML | Comprehensive typing, namespace support | Enterprise/legacy SOAP |
| TypeBox/Zod | TypeScript | Type inference, runtime validation | TypeScript APIs |
Got: Schema system selected based on ecosystem, performance needs, and evolution requirements. If fail: If uncertain, start with JSON Schema — it has the broadest tooling support and can be layered onto existing JSON APIs.
Step 2: Design the Core Schema
JSON Schema example:
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://example.com/schemas/measurement/v1",
"title": "Measurement",
"description": "A sensor measurement reading",
"type": "object",
"required": ["sensor_id", "value", "unit", "timestamp"],
"properties": {
"sensor_id": {
"type": "string",
"pattern": "^[a-z]+-[0-9]+$",
"description": "Unique sensor identifier (lowercase-digits format)"
},
"value": {
"type": "number",
"description": "Measured value"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit", "kelvin", "percent", "ppm"],
"description": "Unit of measurement"
},
"timestamp": {
"type": "string",
"format": "date-time",
"description": "ISO 8601 timestamp with timezone"
},
"metadata": {
"type": "object",
"additionalProperties": true,
"description": "Optional key-value metadata"
}
},
"additionalProperties": false
}
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 · 319 lines · 82 tokens per session scan A 03ee0334a700
design-serialization-schema is a skill published in the GitHub repository pjt222/agent-almanac (42 stars, last pushed 3d ago), licensed MIT. It adds 82 tokens to every session and 2,679 once invoked, about $0.0003 per session on Opus 5.5. 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
migrating-ai-sdk-to-common-ai
Migrates Airflow projects from airflow-ai-sdk to apache-airflow-providers-common-ai 0.4.0+. Use when replacing airflow-ai-sdk with the official Airflow AI provider - migrating LLM decorators (@task.llm, @task.agent, @task.llmbranch, @task.embed), switching from model strings/objects to connection-based LLM…
creating-openlineage-extractors
Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.
langchain4j-spring-boot-integration
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot…
gemini-api-dev
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice design, voice replication, streaming responses, background research tasks, function calling, structured output, or migrating from…
azure-search-documents-ts
Build search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.
cloudflare-email-service
Implement or troubleshoot Cloudflare Email Sending and Email Routing integrations and their delivery configuration.