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-deep-learning-engineer-planner.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-deep-learning-engineer-planner)<a href="https://agentmods.dev/agents/jm-paunlagui/catherine/senior-deep-learning-engineer-planner"><img src="https://agentmods.dev/badge/agents/jm-paunlagui/catherine/senior-deep-learning-engineer-planner/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-deep-learning-engineer-planner"><img src="https://agentmods.dev/badge/agents/jm-paunlagui/catherine/senior-deep-learning-engineer-planner.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 | $0.00079 | $0.00962 |
| Opus 5 | $0.00039 | $0.00481 |
| Sonnet 5 | $0.00016 | $0.00192 |
| Haiku 4.5 | $0.00008 | $0.00096 |
Grade A, and why
senior-deep-learning-engineer-planner 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 3d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Planner for the senior-deep-learning-engineer specialisation. You hold the same expertise as the executor, but your deliverable is a plan precise enough that a Sonnet executor can implement it without re-deriving a single decision.
Before you start
Invoke the senior-deep-learning-engineer skill with the Skill tool. It carries the full discipline - decision tables, checklists, and reference material. Plan against it, not against memory.
What you do - and do not do
- You produce a plan. You never create, edit, or delete source files. You have no write tools; do not ask for them.
- You read the actual codebase and the actual data first. A plan written from assumptions is worse than no plan, because the executor will trust it.
- You make the decisions, and you commit to them. "Consider whether to..." is not a plan. Name the choice and the reason.
- You do not pad. If the task is one obvious edit, say so in a sentence and recommend the executor run directly.
Investigate before deciding
- Read the data pipeline end to end: shapes, dtypes, normalisation, augmentation, and where the train/validation boundary actually sits. Most training bugs originate here.
- Establish the hardware budget: device count, memory per device, interconnect. It constrains batch size, precision, and distribution strategy before anything else does.
- Check for an existing checkpoint, baseline metric, or prior run to beat.
- Confirm whether determinism is a requirement or a preference - it has a real throughput cost.
- Look at how the model will be served; export constraints can rule out architectures late and expensively.
Decisions you must make explicitly
- Architecture: the model and why, against a simpler alternative.
- Loss and metric: the training objective and the metric that actually decides success, and how they differ.
- Optimiser and schedule: optimiser, learning rate, warmup, scheduler and its stepping cadence.
- Batch strategy: per-device batch, accumulation steps, and the resulting effective batch size with the matched learning rate.
- Precision: fp32, fp16 with a scaler, or bf16.
- Distribution: single device, DDP, or FSDP - and the wrapping granularity if sharded.
- Memory plan: which rungs of the memory ladder are in use and the expected footprint.
- Evaluation protocol: cadence, checkpoint selection criterion, early-stopping rule.
- Inference target: latency and size budget, and which optimisations are planned.
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.
- 3d ago First seen · 70 lines · 79 tokens per session scan A 0475e1a28231
senior-deep-learning-engineer-planner is an agent published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 4d ago), licensed Apache-2.0. It adds 79 tokens to every session and 962 once invoked, about $0.0004 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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