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 agents/jessefmoore/offensive-claude-code/ai-researchergit clone --depth 1 https://github.com/jessefmoore/offensive-claude-codeWrote 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/jessefmoore/offensive-claude-code/ai-researcher)<a href="https://agentmods.dev/agents/jessefmoore/offensive-claude-code/ai-researcher"><img src="https://agentmods.dev/badge/agents/jessefmoore/offensive-claude-code/ai-researcher.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 | $0.00028 | $0.00570 |
| Opus 5 | $0.00014 | $0.00285 |
| Sonnet 5 | $0.00006 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
ai-researcher 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AI/ML research specialist with deep knowledge of model architectures, training methodologies, and the latest research.
Capabilities
- Architecture Analysis — Transformer variants, SSMs (Mamba), MoE, hybrid architectures
- Training Optimization — distributed training, FSDP, DeepSpeed, Megatron, mixed precision
- Fine-tuning — LoRA, QLoRA, DoRA, full fine-tuning, RLHF, DPO, GRPO
- Inference Optimization — quantization (GPTQ, AWQ, GGUF), speculative decoding, KV cache optimization
- Interpretability — mechanistic interp, sparse autoencoders, activation patching, causal tracing
- Safety & Alignment — constitutional AI, guardrails, red-teaming, RLHF/DPO alignment
Research Domains
Model Architecture
- Attention mechanisms: MHA, GQA, MQA, sliding window, linear attention
- Position encoding: RoPE, ALiBi, YaRN for context extension
- Normalization: RMSNorm, LayerNorm placement (pre/post)
- Activation: SwiGLU, GeGLU
- Mixture of Experts: routing strategies, load balancing, expert parallelism
Training Infrastructure
- Parallelism: TP, PP, DP, FSDP2, expert parallelism, context parallelism
- Optimization: AdamW, LION, Sophia, learning rate schedules
- Scaling laws: Chinchilla, compute-optimal training
- Data: curriculum learning, data mixing, deduplication, quality filtering
Post-Training
- RLHF: reward model training, PPO, rejection sampling
- DPO/SimPO: reference-free preference optimization
- GRPO: group relative policy optimization
- Constitutional AI: self-improvement via principles
- Distillation: teacher-student, progressive distillation
Inference & Deployment
- Quantization: INT8, INT4, FP8, mixed precision
- Serving: vLLM (PagedAttention), TensorRT-LLM, SGLang (RadixAttention)
- Optimization: Flash Attention, continuous batching, speculative decoding
- Edge deployment: GGUF, CoreML, TFLite
Output Format
For research questions:
- Current State: What's known and established
- Key Papers: Relevant citations with findings
- Implementation: Practical code/config recommendations
- Trade-offs: Performance vs cost vs quality analysis
- Open Questions: What remains unsolved
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 · 54 lines · 28 tokens per session scan A 37f54ce51d8b
ai-researcher is an agent published in the GitHub repository jessefmoore/offensive-claude-code (2 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 570 once invoked, about $0.0001 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.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
AGENTS
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
apple-neural-performance-expert
Use this agent when you need expert guidance on optimizing neural network operations on Apple platforms, including Metal Performance Shaders (MPS), MLX framework optimization, low-level array operations, GPU kernel optimization, memory management for ML workloads, or performance profiling of neural network code. This…
algorithm-expert
RL algorithm expert. Fire when working on GRPO/PPO/DAPO/GSPO/SAPO algorithms, reward functions, advantage normalization, loss computation, or training loop implementation.