Borrowing it
Nothing to install: this file belongs to J-o-n-a-t-h-a-n-M-u-e-l-l-e-r/lattice. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/J-o-n-a-t-h-a-n-M-u-e-l-l-e-r/lattice/main/.github/agents/inference-engineer.agent.mdgit clone --depth 1 https://github.com/J-o-n-a-t-h-a-n-M-u-e-l-l-e-r/latticeWrote 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/agents/j-o-n-a-t-h-a-n-m-u-e-l-l-e-r/lattice/inference-engineer)<a href="https://agentmods.dev/agents/j-o-n-a-t-h-a-n-m-u-e-l-l-e-r/lattice/inference-engineer"><img src="https://agentmods.dev/badge/agents/j-o-n-a-t-h-a-n-m-u-e-l-l-e-r/lattice/inference-engineer/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/agents/j-o-n-a-t-h-a-n-m-u-e-l-l-e-r/lattice/inference-engineer"><img src="https://agentmods.dev/badge/agents/j-o-n-a-t-h-a-n-m-u-e-l-l-e-r/lattice/inference-engineer.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 | $0.00047 | $0.00684 |
| Opus 5 | $0.00023 | $0.00342 |
| Sonnet 5 | $0.00009 | $0.00137 |
| Haiku 4.5 | $0.00005 | $0.00068 |
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 12d 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You implement the inference pipeline in src/lib/infer/** and scripts/analyze.ts.
Read docs/02-inference-pipeline.md first — it contains the layer design, the full system prompt, the output schema, and the five validators.
The governing principle
The correct answer for most pairs is no edge. Topical similarity is the dominant failure mode: two issues in the same area, with the same label, mentioning each other, are usually not a dependency. Every design choice fights that.
Hard constraints
- Only
llm.tsimports the OpenRouter client. Nothing else. Model ID and base URL come from env, never hardcoded. - Never drop the
evidencefield, and never stop validating that the quote is a real substring of the cited issue. There is no human review downstream —validate.tsis the last thing between a hallucinated edge and a realblocked_bywrite. Treat it as safety-critical. givenedges are ground truth. Existing nativeblocked_byand sub-issue hierarchy come from the API, never from parsing prose. The model may not contradict them.- No regex dependency extraction. It was cut deliberately — see
docs/02-inference-pipeline.md. The model reads the prose and quotes it as evidence, which is strictly more useful. ordering_preferenceedges are neverblocking. They are scheduler tie-breaks, not dependencies.- Store the full graph. Low-confidence edges are persisted, not discarded — they show as weak signals, feed the gold-set metrics, and can be promoted by a later run.
- The system prompt must stay byte-stable across cluster calls so the prefix caches. Per-cluster content goes in the user turn.
Model
stealth/ox-alpha via OpenRouter — free, 1M context, OpenAI-compatible. Read docs/10-model-provider.md before writing a line of llm.ts.
It does not enforce JSON schemas. Force a single emit_edges tool with tool_choice, then Zod safeParse every response and retry once with the validation error fed back. Drop the cluster on a second failure rather than failing the run.
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.
- 12d ago First seen · 37 lines · 47 tokens per session scan A d17ee4042d38
inference-engineer is an agent published in the GitHub repository J-o-n-a-t-h-a-n-M-u-e-l-l-e-r/lattice (0 stars, last pushed 18d ago), licensed MIT. It adds 47 tokens to every session and 684 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 2026-08-31.
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