ref-output-serialization-conventions

ref-output-serialization-conventions is a skill for Claude Code, Codex from deephaven/deephaven-mcp. It costs 57 tokens per session (2,600 once invoked), scanned A, original, Apache-2.0.

A set of rules for turning program values into strings and fields returned to users or AI agents by MCP tools and command-line commands.

In plain words
What is it for?
Use it when creating or reviewing return values, response payloads, command-line output fields, or other user-facing serialized data.
Why use it?
It keeps output formats predictable, including the capitalization of categories and runtime statuses.

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/deephaven/deephaven-mcp/ref-output-serialization-conventions
Any agent
npx skills add deephaven/deephaven-mcp --skill ref-output-serialization-conventions
Clone the repo
git clone --depth 1 https://github.com/deephaven/deephaven-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ref-output-serialization-conventions

README.md
[![agentmods](https://agentmods.dev/badge/skills/deephaven/deephaven-mcp/ref-output-serialization-conventions.svg)](https://agentmods.dev/skills/deephaven/deephaven-mcp/ref-output-serialization-conventions)
Your own site
<a href="https://agentmods.dev/skills/deephaven/deephaven-mcp/ref-output-serialization-conventions"><img src="https://agentmods.dev/badge/skills/deephaven/deephaven-mcp/ref-output-serialization-conventions.svg" alt="Measured on agentmods" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,600 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00057 $0.02600
Opus 5 $0.00028 $0.01300
Sonnet 5 $0.00011 $0.00520
Haiku 4.5 $0.00006 $0.00260

Measured 5d ago against content hash 2d07728b4aa5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ref-output-serialization-conventions 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 5d 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.

.agents/skills/ref-output-serialization-conventions/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

This skill is the hub for project conventions on how values are serialized into user-facing output. New rules accrete here rather than spawning sibling skills.

Enum value casing

Pick the right enum accessor at definition time; never normalize casing at the call site.

The two categories

Categorical labels — kind, type, classification, identifier-like tokens. Emit StrEnum.value. Lowercase by convention.

  • type: SystemType.COMMUNITY.value"community"
  • origin: SessionOrigin.STATIC.value"static"
  • system: lowercase system name
  • phase: InitializationPhase.COMPLETED.value"completed"
  • launch_method: "docker" / "python"

Runtime state — current liveness, status that changes over the session's lifetime. Emit .name. UPPERCASE.

  • liveness_status: ResourceLivenessStatus.ONLINE.name"ONLINE"

Discriminator when a field mutates: a lifecycle value that belongs to a documented protocol vocabulary — echoed in docs, filters, and agent retry logic — is categorical even though it changes over time (phase progresses not_startedcompleted and emits .value). Runtime state is a point-in-time health probe with no protocol vocabulary (liveness_status).

Canonical implementations:

  • Categorical: resource_manager/_manager.py (SessionManager.to_dict) reads self.system_type.value and self.origin.value.
  • Runtime state: mcp_systems_server/_tools/session.py (_get_session_liveness_info) reads status.name.

The call-site rule

Never call .upper() or .lower() on an enum value at the call site. If a value's casing feels wrong, fix the enum definition — or recognize a vocabulary mismatch that needs a deeper fix, not a transform.

# Good — the enum's chosen accessor is what ships
{"type": mgr.system_type.value, "liveness_status": status.name}

# Bad — call-site normalization
{"type": mgr.system_type.name.lower(), "origin": mgr.origin.name.lower()}

CLI human-mode column headers

Read the full file on GitHub · 79 lines

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. 5d ago First seen · 79 lines · 57 tokens per session scan A 2d07728b4aa5

Subscribe to this mod's changes

ref-output-serialization-conventions is a skill published in the GitHub repository deephaven/deephaven-mcp (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 57 tokens to every session and 2,600 once invoked, about $0.0003 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens