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 fmind/dot --skill model-providersgit clone --depth 1 https://github.com/fmind/dotWrote 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/fmind/dot/model-providers)<a href="https://agentmods.dev/skills/fmind/dot/model-providers"><img src="https://agentmods.dev/badge/skills/fmind/dot/model-providers/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/fmind/dot/model-providers"><img src="https://agentmods.dev/badge/skills/fmind/dot/model-providers.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 | $0.00024 | $0.00611 |
| Opus 5.5 | $0.00010 | $0.00244 |
| Sonnet 5.5 | $0.00005 | $0.00122 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
model-providers 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 — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Providers
Configure provider authentication, endpoints, and model selection. Default new integrations to GCP Agent Platform with ADC, global, the latest GA model of the chosen tier, and high thinking. The personal project is ai-studio-fmind; explicit project/customer settings take precedence. Preserve OpenCode's OpenRouter default. Use API keys only when explicitly requested or as a last resort after reporting why ADC cannot work; never retry automatically with a key or switch to AI Studio after an error.
Workflow
- Never let a key pick the provider: resolve the requested provider before inspecting credentials. An available key is not a reason to switch providers. Use the selected guide only; gcloud owns Cloud identity/IAM and agent-harnesses owns coding-host settings.
- Choose the tier, then the latest model: default to the workhorse tier (the latest GA Flash) for agents, tool calls, extraction, and high-volume or latency-sensitive work. Escalate to the frontier tier (Gemini 4 Argon where the project has access, otherwise the latest Pro) for complex long-horizon reasoning, coding, or analysis where quality outweighs cost and latency. Resolve the exact ID from the provider's current model list, never from memory, and confirm it with the read-only probe.
- Pin the provider and scope keys: make the provider explicit in application configuration so ambient Google API keys or SDK backend flags cannot silently select another service. Supply keys only to the process that needs them, through a secret manager or
dot secret run NAME -- COMMAND; use native credential stores where supported. Never place keys in code or generated configuration. - Probe read-only; never silently fall back: validate configuration and use a read-only model/auth probe first. Run inference only within the authorized usage scope, with a bounded output and timeout. Report authentication, model access, and successful inference separately; do not silently fall back to another provider or billing project after an error.
What ships with it
3 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.
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 Changed · +1 lines · +2 tokens per session 9364c5135c4d
- 9d ago Changed · -1 lines 35b8b10dbb2a
- 11d ago Changed 85862e36d2b9
- 12d ago Changed 00009aa7383d
- 14d ago Changed e5143fc8e16f
- 23d ago Changed a9f3c659ffa7
- 26d ago Changed f4a7c206fb8a
- 29d ago First seen · 33 lines · 22 tokens per session scan A 296dbca21541
model-providers is a skill published in the GitHub repository fmind/dot (11 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 611 once invoked, about $0.0001 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 2026-09-17.
Other skills, from other repositories
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prompt-design
Design production LLM or agent prompt stacks: instructions, tool contracts, examples, outputs, and runtime context. Use for precedence, conflicts, dynamic or untrusted context; prove behavior with agent-evaluation.
dotfiles-bootstrap
Bootstrap a workstation with the dotfiles framework. Takes a GitHub user / owner+repo / explicit clone URL and runs dot init (which shells out to chezmoi) with the right safety prompts. Honors the active agent profile (ask / plan / apply / audit) so it defaults to dry-run in safer modes and full apply in apply.
vibe
Delegate a coding task to a cheap AI model (Mistral Vibe by default, but any provider Vibe knows about — DeepSeek, Gemini Flash, etc.) and supervise the result via git diff. Claude orchestrates, the cheap model codes. Claude consumes 500-1500 tokens per delegation regardless of how many file reads the delegate does…
prompt-engineering
Draft, review, or improve a prompt for an LLM or agent against a research-backed rubric — explicit instructions, instruction/data separation, output contracts, reasoning scaffolds, grounding, verification, and evals. Use when writing a prompt, auditing one that misbehaves, or explaining a prompting principle.
go-stack
Build Go projects, libraries, CLIs, TUIs, web apps, or ADK agents with the standard package layout and pinned tooling.