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/metraton/gaia/code-standardsnpx skills add metraton/gaia --skill code-standardsgit clone --depth 1 https://github.com/metraton/gaiaWhat 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 | $0.00050 | $0.01468 |
| Opus 5 | $0.00025 | $0.00734 |
| Sonnet 5 | $0.00010 | $0.00294 |
| Haiku 4.5 | $0.00005 | $0.00147 |
Grade A, and why
code-standards 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 2d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Standards
Code Standards governs how code is expressed — clarity, simplicity, safety, and maintainability — not which architectural pattern should exist. Use the applicable domain or pattern guidance to determine the design; use these standards to implement that design clearly and safely.
Iron Law
Write code so that its behavior, responsibility, and intent can be understood from the implementation itself. Prefer code that explains itself over explanations surrounding unclear code.
Mental Model
A good implementation lets another engineer — or another agent — determine what assumptions it relies on and where to look when behavior must change. Clarity reduces the amount of inference required to safely modify the system.
Rules
1. Make responsibility visible
Before adding code, identify the responsibility being implemented and the existing pattern it belongs to; names, boundaries, and structure should reveal that responsibility. Follow the local pattern when one exists — it settles the form of what you write, never whether it was warranted, and matching what surrounds you is no evidence that what surrounds you earned its place. A new abstraction or pattern should exist because the problem requires it, not because the current implementation can be made more elaborate.
2. Prefer the simplest complete implementation
Implement the behavior required by the current problem. Additional abstractions, configuration, indirection, branches, or extensibility added for hypothetical future cases increase the number of assumptions a reader must understand and the number of places a future change can fail. Safety comes from making current assumptions and boundaries explicit, not from anticipating every possible implementation.
3. Make behavior explicit
Important behavior should be visible in code rather than hidden behind unexpected side effects, implicit state, or unrelated abstractions. Inputs, transformations, state changes, and failure paths should be traceable from the implementation.
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.
- 2d ago First seen · 147 lines · 50 tokens per session scan A e5d46cbc4851
code-standards is a skill published in the GitHub repository metraton/gaia (3 stars, last pushed 4d ago), licensed MIT. It adds 50 tokens to every session and 1,468 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.
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Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
python-development
Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
reflect
Review recent work, find repeated workflow patterns, and suggest reusable skills, agents, commands, config changes, or playbooks. Use when the user asks to learn from past sessions, improve recurring workflows, or identify what should be turned into reusable agent instructions.
codemap
Generate comprehensive hierarchical codemaps for UNFAMILIAR repositories. Expensive operation - only use when explicitly asked for codebase documentation or initial repository mapping.
length-converter
Convert between common length units (miles, km, feet, meters) using a multiplication factor.