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 instructions/matanbt/tropt/claude-mdgit clone --depth 1 https://github.com/matanbt/TROPTWrote 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/instructions/matanbt/tropt/claude-md)<a href="https://agentmods.dev/instructions/matanbt/tropt/claude-md"><img src="https://agentmods.dev/badge/instructions/matanbt/tropt/claude-md.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 | $0.03982 | $0.03982 |
| Opus 5 | $0.01991 | $0.01991 |
| Sonnet 5 | $0.00796 | $0.00796 |
| Haiku 4.5 | $0.00398 | $0.00398 |
Grade A, and why
TROPT CLAUDE.md 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
Guidance for Claude Code when working with this repository. For comprehensive design philosophy see
DESIGN.md; for step-by-step guides seedocs/guides/.READ FIRST for almost every TROPT task — load
skills/tropt/SKILL.md. It's the user-facing companion to this dev-facing file and covers the bulk of what a contributor does day-to-day: adding a recipe, adding a loss, adding an optimizer, adding a model backend, composing custom Model + Loss + Optimizer wirings, swapping components in an existing recipe, debugging cross-cutting pitfalls (mixin mismatches, attention-loss requirements, thinking-model target alignment, black-box vs white-box loss, multi-model OOM, etc.), routing to the right guide / source file, and helping users without a local checkout. Skill loading is not automatic from<repo>/skills/— the file must be explicitlyRead(this is intentional Claude Code behavior, not a bug). Treat the skill as required reading whenever the request touchestropt/recipe_hub/,tropt/loss/,tropt/optimizer/,tropt/model/, or composition patterns. Install is just the supporting first step it also covers.
Tips
- Prefer concise modifications — minimal changes so edits are easy to review. But if minimal changes create technical debt or unreadable code, prefer clarity.
- Temporary scripts or markdown you create go under
claude_stuff/. Don't make a mess. - Avoid over-commenting. Use clear names; only comment where intent isn't obvious.
- Keep docstrings short. One or two sentences max. Only list args that aren't self-evident from name and type.
- Docs maintenance: Don't enumerate specific classes/fields/signatures in docs — they go stale. Point to source instead (e.g., "see
tropt/loss/for the full set").
Project Overview
TROPT (Textual Trigger Optimization Toolbox) is a research platform for optimizing discrete text triggers that elicit specific behaviors from NLP models. Primary use cases:
- Red-teaming: Optimizing triggers toward malicious/undesired model behaviors (LLM jailbreaks)
- Prompt Tuning: Enhancing desired behaviors through trigger optimization
- Model Inspection: Crafting adversarial examples and counterfactuals for research
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 · 294 lines · 3,982 tokens per session scan A 46c66e20b3e7
TROPT CLAUDE.md is an instructions file published in the GitHub repository matanbt/TROPT (11 stars, last pushed 4d ago), licensed MIT. It adds 3,982 tokens to every session, about $0.0199 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 2026-08-30.
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