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 vasilyu1983/AI-Agents-public --skill ai-coding-agents-command-runtimegit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-coding-agents-command-runtime)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-coding-agents-command-runtime"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-command-runtime.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 | $0.00041 | $0.04323 |
| Opus 5 | $0.00020 | $0.02161 |
| Sonnet 5 | $0.00008 | $0.00865 |
| Haiku 4.5 | $0.00004 | $0.00432 |
Grade A, and why
ai-coding-agents-command-runtime 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 7d 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Agents Command Runtime
Use this skill to design or review the slash-command layer of a coding-agent CLI: command registry shape, typed command kinds, lazy loading, source-aware discovery, and safe dispatch across local, remote, and bridge modes.
This skill owns command-runtime architecture for coding agents. For broader agent creation, start with ../ai-coding-agents/SKILL.md.
ASCII Flow
command sources
built-ins + skills + plugins + workflows + dynamic discoveries
|
v
registry composition
typed command contract + source tags + deterministic precedence
plugin-namespaced skills: plugin-name:skill-name
|
v
availability + enablement
feature gates + auth + mode filters + aliases
/agents as first-class tabbed command surface (background agent management)
|
v
dispatch
prompt command | local text | local UI | forked subagent | remote-safe
/reload-skills (in-session reload) | SessionStart reloadSkills hook
--safe-mode (disables CLAUDE.md, plugins, skills, hooks, MCP)
|
v
execution result or unavailable-command error
Quick Reference
| Question | Read | Outcome |
|---|---|---|
| How should commands be represented and discovered? | references/command-registry-and-discovery.md |
Registry model, command kinds, load order, source precedence |
| How should commands execute across inline, forked, and remote flows? | references/command-dispatch-forking-and-remote-safety.md |
Dispatch rules, forked execution, remote-safe filtering, bridge gating |
| How does OpenAI Codex model slash-command availability? | references/openai-codex-command-state-machine.md |
Command metadata, inline-arg support, active-task availability, side-conversation availability |
When To Use
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 318 B
- assets/templates/minimal-command-registry.ts 7.1 KB runs code
- data/sources.json 3.8 KB
- learnings.consolidated.md 608 B
- learnings.md 412 B
- references/command-dispatch-forking-and-remote-safety.md 7.8 KB
- references/command-registry-and-discovery.md 3.9 KB
- references/memoization-invalidation-contract.md 6.0 KB
- references/openai-codex-command-state-machine.md 2.5 KB
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.
- 7d ago First seen · 234 lines · 41 tokens per session scan A d9d2efe61515
ai-coding-agents-command-runtime is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (84 stars, last pushed 4d ago), licensed MIT. It adds 41 tokens to every session and 4,323 once invoked, about $0.0002 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 skills, from other repositories
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
istio-traffic-management
Configure Istio traffic management including routing, load balancing, circuit breakers, and canary deployments. Use when implementing service mesh traffic policies, progressive delivery, or resilience patterns.
projection-patterns
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.
microservices-patterns
Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
track-management
Use this skill when creating, managing, or working with Conductor tracks - the logical work units for features, bugs, and refactors. Applies to spec.md, plan.md, and track lifecycle operations.
moai-kanban-foreman
One unattended kanban foreman iteration: watch the backlog queue, dispatch the next operator-picked card to an isolated worker, collect completion evidence on read (not on claims), and report. This is the body the project's loop.md driver invokes each iteration of a bare /loop; it can also be invoked directly to test…