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-provider-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-provider-runtime)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-coding-agents-provider-runtime"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-provider-runtime/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/vasilyu1983/ai-agents-public/ai-coding-agents-provider-runtime"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-provider-runtime.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.04538 |
| Opus 5 | $0.00018 | $0.02269 |
| Sonnet 5 | $0.00007 | $0.00908 |
| Haiku 4.5 | $0.00004 | $0.00454 |
Grade A, and why
ai-coding-agents-provider-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 10d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Agents Provider Runtime
Use this skill to design or review the model-provider layer inside a coding-agent runtime: provider abstraction, streaming semantics, tool-call protocol normalization, context-window strategy, retries, and fallback routing.
This skill owns the model-facing runtime surface for coding agents. It is the main missing layer when trying to generalize Claude Code-derived patterns toward Codex-class portability.
ASCII Flow
agent turn
|
v
provider selection
capability needs + model policy + cost/latency + context window
|
v
request normalization
messages + tools + structured outputs + cache hints + metadata
|
v
provider stream
tokens + tool calls + errors + usage events
|
v
runtime event model
normalized deltas + retries/fallbacks + final response
Quick Reference
| Question | Read | Outcome |
|---|---|---|
| How should providers and streaming semantics be normalized? | references/provider-abstraction-and-stream-normalization.md |
Stable provider interface, streaming event model, and tool-call normalization |
| How should retries, context windows, and fallback routing work? | references/context-window-retries-and-fallback-routing.md |
Provider selection, truncation rules, retry classes, and fallback policy |
| How does OpenAI Codex check local OSS provider readiness? | references/openai-codex-local-oss-provider-readiness.md |
Ollama/LM Studio readiness workflow, model presence, version gates, fetch/load diagnostics, and capability-driven selection |
| What exactly differs across Claude, OpenAI, Gemini, and Ollama today? | references/provider-capability-matrix.md |
Feature-by-feature comparison (streaming, structured output, tool calls, vision, caching) plus a capability-flag interface and shim design notes |
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 347 B
- assets/templates/parity-test-checklist.md 5.3 KB
- data/sources.json 5.4 KB
- learnings.consolidated.md 609 B
- learnings.md 331 B
- references/context-window-retries-and-fallback-routing.md 1.9 KB
- references/openai-codex-local-oss-provider-readiness.md 3.9 KB
- references/provider-abstraction-and-stream-normalization.md 1.7 KB
- references/provider-capability-matrix.md 7.9 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.
- 10d ago First seen · 248 lines · 36 tokens per session scan A d8afb1c7bf42
ai-coding-agents-provider-runtime is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 8d ago), licensed MIT. It adds 36 tokens to every session and 4,538 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.
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