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 agentmods add skills/rockielab/rockie-codex/inference-engineernpx skills add Rockielab/rockie-codex --skill inference-engineergit clone --depth 1 https://github.com/Rockielab/rockie-codexWrote 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/rockielab/rockie-codex/inference-engineer)<a href="https://agentmods.dev/skills/rockielab/rockie-codex/inference-engineer"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-codex/inference-engineer.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 | $0.00159 | $0.07056 |
| Opus 5 | $0.00079 | $0.03528 |
| Sonnet 5 | $0.00032 | $0.01411 |
| Haiku 4.5 | $0.00016 | $0.00706 |
Grade A, and why
inference-engineer 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 6d 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.
This is a copy
100% identical to inference-engineer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
inference-engineer
The packaging skill that turns "here is a model" into "here is an API your agent can call." Companion to /autoresearch (Rockie's R&D half): autoresearch produces models and findings, inference-engineer operates them.
When to invoke
- Explicit:
/inference-engineer <model-url-or-HF-repo>(URL optional; agent will ask for it). - Intent-triggered: any user message expressing one of —
- "I want to host / serve / deploy / productize / run inference on / get an API for / make available "
- "I trained / fine-tuned a model — what do I do with it?"
- "How do I serve this for my agent / app / customers / a batch of N requests?"
- "Reproduce paper / blog / GitHub repo X on a small GPU"
- Cascade entry from sub-skills: the eval / kernel / gateway sub-skills can call back here to re-provision when a user asks to swap hardware or change serving config.
The two researcher motivations this skill serves
- "I found a model — productize it." A researcher has a HF repo / GitHub repo / paper-with-code link. They want to host it themselves (cost, privacy, latency, customization). They need: right hardware, right serving stack, an API, MCP exposure to their chat agent, quality + cost numbers.
- "I trained a model — now what?" Autoresearch produced a checkpoint. The researcher wants to share it, evaluate it head-to-head against the baseline, or wire it into a downstream agent. They need the same productization path but starting from local weights instead of a public repo.
Both routes converge on the same 8-step orchestration below.
If this skill needs Rockie-managed training, eval, synthetic-data, or
other GPU job execution outside POST /api/inference/loads, route that
work through /experiment and its embedded budget-term-sheet approval gate.
Do not introduce raw /api/jobs/submit calls here.
Read this before doing anything
The skill reasons from a checked-in research corpus, not from re-Googling. Before step 1 of any run, read all of:
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.
- prompts/cost-confirm.md 3.0 KB
- prompts/intake-clarify.md 4.8 KB
- research/api-gateways.md 5.8 KB
- research/evaluation.md 6.1 KB
- research/hardware-workload.md 5.8 KB
- research/kernel-engineering.md 7.3 KB
- research/serving-stacks.md 6.0 KB
- runtime/monitor_contract.py 3.6 KB runs code
- test_skill_auth_contract.py 790 B runs code
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.
- 6d ago First seen · 346 lines · 159 tokens per session scan A 62ae8834a2a9
inference-engineer is a skill published in the GitHub repository Rockielab/rockie-codex (20 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 159 tokens to every session and 7,056 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to inference-engineer, differing in 0 lines, and is treated as a copy.
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