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 instructions/arize-ai/arize-skills/agents-mdgit clone --depth 1 https://github.com/Arize-ai/arize-skillsWrote 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/instructions/arize-ai/arize-skills/agents-md)<a href="https://agentmods.dev/instructions/arize-ai/arize-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/arize-ai/arize-skills/agents-md.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 | $0.00614 | $0.00614 |
| Opus 5 | $0.00307 | $0.00307 |
| Sonnet 5 | $0.00123 | $0.00123 |
| Haiku 4.5 | $0.00061 | $0.00061 |
Grade A, and why
arize-skills AGENTS.md 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.
How it starts
The opening of the file, as written. The whole thing — 20 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Authoring Guidelines for AI Agents
Skill files are read by AI, not humans
Skill files (SKILL.md, references/, etc.) are loaded as context for AI agents. Follow these rules when editing them, per the Agent Skills specification:
- Use Markdown links for every file and doc reference — write
See [the reference guide](references/REFERENCE.md), not a bare path or URL. This is the syntax in the spec's File references example; standardize on it repo-wide for consistency. Keep the target a relative path from the skill root, one level deep (e.g.references/EXAMPLES.md), and avoid deeply nested reference chains. (Runnable commands and code stay literal —scripts/extract.pyin a run instruction,go get …, code blocks — they are commands, not links.) - Additional documentation goes in
references/— supplementary docs (e.g.,ax-profiles.md,ax-setup.md,EXAMPLES.md) belong in thereferences/subdirectory, per the Agent Skills specification. - Keep
SKILL.mdfocused — the full body loads on activation; the spec recommends staying under ~5000 tokens / 500 lines and moving detail intoreferences/.
Quality checks
CI (.github/workflows/quality-checks.yml) runs these on every PR. Run the no-Node subset locally with bash scripts/check.sh before pushing:
scripts/validate_skills.py— per-skill structure: frontmatter,name(kebab-case, 3–64 chars, matches the folder, unique),descriptionlength, declaredassetsexist and are ≤5 MB, Markdown links with relative targets resolve,SKILL.md≤500 lines, and a warning for bare doc paths that should be Markdown links (the rule above).scripts/validate_manifests.py—version.txtmatches every plugin manifest (release-please keeps them in sync; this proves they never drift) and metadata (name,description,keywords,license,repository) is consistent and within limits.- codespell (.codespellrc) and line endings (scripts/check_line_endings.sh) — spelling and LF hygiene.
- Vally lint (eng/vally-lint.mjs) —
@microsoft/vally's static skill lint (spec-compliance + valid references). Needs Node:npm install --no-save @microsoft/[email protected] && node eng/vally-lint.mjs. - Plugin install smoke test —
claude plugin validate .plus a marketplace add/install of the plugin.
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.
- 5d ago First seen · 20 lines · 614 tokens per session scan A 67782cdebfbd
arize-skills AGENTS.md is an instructions file published in the GitHub repository Arize-ai/arize-skills (48 stars, last pushed 4d ago), licensed MIT. It adds 614 tokens to every session, about $0.0031 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 instructions, from other repositories
trulens AGENTS.md
AGENTS.md instructions for truera/trulens, covering agents.md, setup commands, code style, testing instructions and test markers.
openlit AGENTS.md
AGENTS.md instructions for openlit/openlit, covering openlit agent rules, ce/oss boundary, extension points, project hierarchy and ui and security.
latitude-llm AGENTS.md
AGENTS.md instructions for latitude-dev/latitude-llm, covering agents.md, product scope, repo-wide conventions, how to use this guide and skill glossary.
openlit CLAUDE.md
Claude Code instructions for openlit/openlit, a project described as: Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. 🚀💻 Integrates with 50+ LLM Providers, VectorDBs, Agent Frameworks and GPUs.
phoenix AGENTS.md
AGENTS.md instructions for Arize-ai/phoenix, covering agent instructions, build & development and python dependency version policy.
phoenix CLAUDE.md
Claude Code instructions for Arize-ai/phoenix, a project described as: AI Observability & Evaluation.