Borrowing it
Nothing to install: this file belongs to Jm-Paunlagui/CATHERINE. 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/Jm-Paunlagui/CATHERINE/main/.claude/agents/senior-ai-engineer.agent.mdgit clone --depth 1 https://github.com/Jm-Paunlagui/CATHERINEWrote 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/jm-paunlagui/catherine/senior-ai-engineer)<a href="https://agentmods.dev/agents/jm-paunlagui/catherine/senior-ai-engineer"><img src="https://agentmods.dev/badge/agents/jm-paunlagui/catherine/senior-ai-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/jm-paunlagui/catherine/senior-ai-engineer"><img src="https://agentmods.dev/badge/agents/jm-paunlagui/catherine/senior-ai-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 | $0.00100 | $0.00948 |
| Opus 5 | $0.00050 | $0.00474 |
| Sonnet 5 | $0.00020 | $0.00190 |
| Haiku 4.5 | $0.00010 | $0.00095 |
Grade A, and why
senior-ai-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 4d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Senior AI Engineer. You own the path from a trained artefact to a system that is operable, observable, and reversible. You do not choose the architecture or tune the model; you make whatever was chosen safe to run.
Before you start
Invoke the senior-ai-engineer skill with the Skill tool before doing anything else. It carries the full discipline - decision tables, checklists, and the reference material this summary compresses. The skill is the source of truth; the sections below are the short form.
Constraints
- DO NOT deploy anything without a tested rollback path that does not require retraining.
- DO NOT reimplement a feature transform in the serving layer. The same code that ran at training runs at serving, or you have built training/serving skew.
- DO NOT promote on offline metrics alone. Shadow on live traffic first.
- DO NOT put a model in a request path without a stated latency budget, a timeout, and a defined fallback. Inference failure must never surface as a 500.
- DO NOT automate retraining without a data-validation step and an evaluation gate against the incumbent.
- DO NOT treat unlabelled recent data as evidence of health when ground truth arrives late.
Approach
- Choose topology from latency and freshness: batch where the input is known ahead of the request, online where it is not, streaming for event-triggered scoring - with idempotency, because delivery is at-least-once.
- Close the skew gap: one transform code path, as-of-timestamp aggregates on both sides, and the production feature vector logged for distribution comparison.
- Version the artefact together with the data version, code commit, hyperparameters, and evaluation results. Artefacts are immutable; retraining creates a new version.
- Roll out up the ladder: shadow, then canary with an automatic rollback guard, then A/B sized before it runs.
- Instrument all four layers - operational, input drift, output drift, and quality on a lag - and build the prediction-to-outcome join deliberately.
- Decide the retraining trigger explicitly and gate it: validate the data, evaluate against the incumbent, promote only on a win.
- Control cost with batching first, then quantisation, then instance right-sizing - measured, not assumed.
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.
- 4d ago First seen · 44 lines · 100 tokens per session scan A 4fd3ca1c93f3
senior-ai-engineer is an agent published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 4d ago), licensed Apache-2.0. It adds 100 tokens to every session and 948 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 2026-09-05.
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