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 antongulin/agenticflow-ai-skills --skill agenticflow-llm-modelsgit clone --depth 1 https://github.com/antongulin/agenticflow-ai-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/antongulin/agenticflow-ai-skills/agenticflow-llm-models)<a href="https://agentmods.dev/skills/antongulin/agenticflow-ai-skills/agenticflow-llm-models"><img src="https://agentmods.dev/badge/skills/antongulin/agenticflow-ai-skills/agenticflow-llm-models/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/antongulin/agenticflow-ai-skills/agenticflow-llm-models"><img src="https://agentmods.dev/badge/skills/antongulin/agenticflow-ai-skills/agenticflow-llm-models.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 | $0.00109 | $0.02155 |
| Opus 5 | $0.00055 | $0.01077 |
| Sonnet 5 | $0.00022 | $0.00431 |
| Haiku 4.5 | $0.00011 | $0.00215 |
Grade A, and why
agenticflow-llm-models 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 11d 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Author: Anton Gulin · Tool: opencode-skill-creator · GitHub: @antongulin · Registry: skills.sh
AgenticFlow LLM Models
Choose the right model for your agent based on capability needs, speed requirements, and reasoning depth. Use built-in credits for all models listed here.
When NOT to use this skill
Use agenticflow-built-in-credits skill instead for pricing, credits, or billing questions. Use agenticflow-mcp skill if they need external API keys (BYOK). This skill covers model selection and capabilities, not account management or credit usage.
Orient first
af bootstrap --json
Extract models[] — this is the source of truth for available models in your workspace. Never hardcode model lists; they change between CLI releases and backend deployments. The models below are recommendations, but the live models[] array is the final authority.
Discover & health
af changelog --json # What's new in the CLI — model additions/removals
af context --json # AI agent orientation, env vars, invocation guidance
af bootstrap --strict --json # Health check — exits non-zero if degraded
af bootstrap returns an invocation block and data_fresh boolean. If data_fresh: false, the backend is degraded — don't rely on stale model data from a degraded response. af bootstrap --strict exits non-zero when the backend is unhealthy, so CI/automation can abort before choosing models against a degraded workspace.
Verification rule: Before recommending any model, check
models[]fromaf bootstrap --json. If a model is absent from that list, warn the user and fall back to a confirmed model.
Author's Top 3 Recommendations
These are the author's personal picks based on reliability, reasoning quality, and speed:
| Rank | Model | Role | Why |
|---|---|---|---|
| 1st | deepseek-v4-flash |
Primary default | Best all-rounder — strong reasoning, reliable tool use, good speed. Replaces the older GLM 4.7 Flash default. |
| 2nd | gemini-2.5-flash-lite |
Fallback / media | Fastest option with media support. Use when speed matters more than reasoning depth. |
| 3rd | qwen-3.5-flash |
Deep verification | Deepest thinker — use when reasoning depth and verification matter most. |
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.
- 11d ago First seen · 197 lines · 109 tokens per session scan A 576edd4f978f
agenticflow-llm-models is a skill published in the GitHub repository antongulin/agenticflow-ai-skills (2 stars, last pushed 6d ago), licensed MIT. It adds 109 tokens to every session and 2,155 once invoked, about $0.0005 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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