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 avizmarlon/agent-skills --skill agent-runtime-paritygit clone --depth 1 https://github.com/avizmarlon/agent-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/skills/avizmarlon/agent-skills/agent-runtime-parity)<a href="https://agentmods.dev/skills/avizmarlon/agent-skills/agent-runtime-parity"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/agent-runtime-parity/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/avizmarlon/agent-skills/agent-runtime-parity"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/agent-runtime-parity.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.02066 |
| Opus 5.5 | $0.00022 | $0.00826 |
| Sonnet 5.5 | $0.00011 | $0.00413 |
| Haiku 4.5 | $0.00006 | $0.00207 |
Grade A, and why
agent-runtime-parity 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 2mo 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Runtime Parity
Use this skill when managing capabilities across multiple AI runtimes to prevent divergence, or when a configuration change, rule update, MCP addition, or credential routing must propagate uniformly to all active AI surfaces.
This skill ensures that team AI tools stay synchronized and that operational changes made in one runtime surface automatically propagate to others—eliminating manual copy-paste sync and the drift that follows.
Core Model
Treat your canonical source repository (e.g., <REPO_ROOT>/skills/, <REPO_ROOT>/rules/) as the single source of truth for cross-agent capabilities.
Active AI Surfaces
Define your active AI surfaces explicitly. Common examples:
- Claude Code (Anthropic)
- Codex / Codex CLI (OpenAI)
- Cursor (VSCode-based, custom LLM integration)
- Gemini / Gemini CLI (Google)
- Custom agent frameworks (LangChain, LlamaIndex, etc.)
- Shared or team-wide agent orchestration tools
- Desktop or CLI agent runners
Tool-Native Adapters
Use tool-native configuration formats rather than converting everything to one format:
- Skill frameworks (SKILL.md, AGENT.md, etc.): store in tool-native directories
- Rules and instructions (
.mdc,.txt,.md): store in tool-specific rule folders - MCP/connector configs (JSON, YAML): use environment variable references instead of hardcoded secrets
- Credential routing notes: document via Bitwarden, vaults, or secure config stores—never commit raw secrets
Example Structure
<REPO_ROOT>/
├── skills/
│ ├── skill-name-1/
│ │ └── SKILL.md
│ └── skill-name-2/
│ └── SKILL.md
├── rules/
│ ├── universal-rule.md
│ └── <tool>-specific-rule.mdc
├── mcp-configs/
│ ├── claude-config.json
│ ├── codex-config.json
│ └── cursor-config.json
└── sync-scripts/
└── sync-agent-parity.ps1 (or .sh for Unix)
Hard Rule: Propagation Mandate
Any durable operational change made for one AI surface must be propagated to every active AI surface in the same session, or the gap must be documented explicitly.
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.
- 2mo ago First seen · 225 lines · 55 tokens per session scan A 791ea6decc02
agent-runtime-parity is a skill published in the GitHub repository avizmarlon/agent-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 55 tokens to every session and 2,066 once invoked, about $0.0002 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 2026-08-31.
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