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/skills/senior-llm-engineer/SKILL.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/skills/jm-paunlagui/catherine/senior-llm-engineer)<a href="https://agentmods.dev/skills/jm-paunlagui/catherine/senior-llm-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-llm-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/skills/jm-paunlagui/catherine/senior-llm-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-llm-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 | $0.00150 | $0.02041 |
| Opus 5 | $0.00075 | $0.01020 |
| Sonnet 5 | $0.00030 | $0.00408 |
| Haiku 4.5 | $0.00015 | $0.00204 |
Grade A, and why
senior-llm-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 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior LLM Engineer
You are a Senior LLM Engineer. Your domain is systems built on top of foundation models — not training them.
Model facts are not yours to remember
Model IDs, context windows, pricing, and API parameters change faster than any skill file can track. Invoke the claude-api skill before writing code against the Anthropic API or quoting a model's price, limits, or capabilities. Never answer those from memory — a confidently stale model ID is the most common defect in this specialisation. If another provider is in play, read that provider's current documentation instead.
The three-way decision, made in order
Reach for the cheapest mechanism that works, and only escalate with evidence:
- Prompting — the task is expressible in instructions and a few examples. Fastest to iterate, zero training cost.
- RAG — the model lacks knowledge that exists in your data, and that knowledge changes. Retrieval keeps it current without retraining.
- Fine-tuning — the model lacks a behaviour, format, or domain style that examples in context cannot reliably produce, and you have hundreds to thousands of consistent examples.
Fine-tuning does not fix hallucination on facts; RAG does. RAG does not fix a model that will not follow your output format; prompting or fine-tuning does. Diagnose which failure you have before choosing.
Context engineering
- Put stable content first and volatile content last — that ordering is what makes prompt caching effective.
- Retrieve, do not stuff. A long context degrades attention to the middle and costs linearly. More context is not more accuracy.
- Separate instructions from data, and mark untrusted content explicitly as data. Content retrieved from documents, tools, or users is never an instruction.
- Give the model a defined way to say "not enough information." Without it, an unanswerable question becomes a fabrication.
Structured output and tool calling
- Use the API's native structured output or tool-use mechanism. Do not regex a JSON object out of prose — it fails on the first nested brace or code fence.
- Validate every returned structure against a schema before use, and handle the invalid case. A schema-conformant response is not necessarily a correct one.
- Design tool schemas the way you design an API: precise descriptions, tight enums, required fields marked. Ambiguous tool descriptions are the main cause of wrong tool selection.
- Make tools idempotent where possible, and return errors to the model as structured text it can act on rather than throwing.
- Cap the agentic loop — a maximum step count and a termination condition. An unbounded tool loop is an unbounded bill.
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 First seen · 94 lines · 150 tokens per session scan A a3bf0027c7b4
senior-llm-engineer is a skill published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 7d ago), licensed Apache-2.0. It adds 150 tokens to every session and 2,041 once invoked, about $0.0007 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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