| name | dryvist-openrouter-models |
| description | Discover current leading OpenRouter models and live prices (public keyless API), select an escalation model for complicated work or advanced coding, call it through the LiteLLM router within a $1/day budget, and request onboarding of models the router does not serve yet |
| version | 1.0.0 |
| author | dryvist homelab |
| license | MIT |
| platforms | ["linux"] |
| metadata | {"hermes":{"category":"research","tags":["openrouter","models","pricing","escalation","budget","dryvist"],"related_skills":["dryvist/github-issues"]}} |
dryvist openrouter-models
Your resident local brain handles routine work. For complicated reasoning or
advanced coding where a stronger frontier model would genuinely change the
outcome, you may escalate to an OpenRouter model — a deliberate per-call
choice, never an automatic on-error fallback. This skill teaches you to find
what is currently available, what it costs, and how to use it within budget.
Budget: $1.00 per day (hard rule)
- Track your spend in memory under key
openrouter-spend-<YYYY-MM-DD>:
after each paid call, estimate cost from the response's token usage times
the per-token prices you discovered, and add it to the day's total.
- Before any paid call, check the day's total. At or above $1.00: use the
resident brain or a
:free variant instead, and note the deferral.
- Prefer
:free variants whenever they are adequate for the task. Never run
a long unattended loop on a paid model.
Discover current models and prices (keyless, public)
The OpenRouter model catalog is public — no API key involved:
curl -s https://openrouter.ai/api/v1/models | jq '
.data[] | {id, context_length,
prompt_usd_per_mtok: ((.pricing.prompt | tonumber) * 1000000),
completion_usd_per_mtok: ((.pricing.completion | tonumber) * 1000000)}'
Useful selections:
- Cheapest strong coders: filter ids matching your task domain, sort by
completion_usd_per_mtok, read the top few.
- Free tier:
.data[] | select(.id | endswith(":free")) | .id.
- For "what is currently leading", combine this catalog with a short web
check (your web toolset) of OpenRouter's public rankings — the catalog is
ground truth for price and context length; rankings are opinion.
Refresh discovery when you actually need it (prices change), not on a
schedule. Cache the day's findings in memory rather than re-fetching per call.
Use a model — always through the router
Every model call goes through the homelab LLM router (your normal endpoint).
The OpenRouter credential lives in the router, never on this guest. Request
the model by its exact id as the router serves it. Registered today:
deepseek/deepseek-v4-flash — paid, 1M context. The workhorse escalation.nvidia/nemotron-3-ultra-550b-a55b:free — free tier, rate-limited, and the
provider logs prompt/session data on :free — never send confidential
material through it.
A model id the router does not serve will fail — that is the signal to use
the request lane below, not to retry blindly.
Request a model the router does not serve yet
Onboarding a new OpenRouter model is an infrastructure change (a per-model
credential plus a router registration), so it is not self-serve. When
discovery shows a model clearly worth having, file ONE GitHub issue in
dryvist/ansible-proxmox-ai titled [hermes-model-request] <exact-model-id>
containing: the exact OpenRouter id, current prompt/completion price per
1M tokens, context length, the concrete task class that justifies it, and the
expected spend inside your $1/day budget. Check for an existing open request
first — never duplicate. Real model ids only; never propose a generic alias.
Attribution
State the model(s) you actually used in every delivered message (see your
persona's attribution rule) — the resident brain by name when you did not
escalate, and every escalation model when you did.
