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 commands/kittimasak/thai-token-optimizer/tto-contextgit clone --depth 1 https://github.com/kittimasak/thai-token-optimizerWrote 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/commands/kittimasak/thai-token-optimizer/tto-context)<a href="https://agentmods.dev/commands/kittimasak/thai-token-optimizer/tto-context"><img src="https://agentmods.dev/badge/commands/kittimasak/thai-token-optimizer/tto-context.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 | $0.00000 | $0.00169 |
| Opus 5 | $0.00000 | $0.00084 |
| Sonnet 5 | $0.00000 | $0.00034 |
| Haiku 4.5 | $0.00000 | $0.00017 |
Grade C, and why
tto-context scanned grade C with 1 finding 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 5d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- ============================================================================ Thai Token Optimizer v2.0 ============================================================================ Description : A Thai token optimiza This is a copy
77% identical to tto-compress — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/tto-context
Inspect context, checkpoint, cache, and calibration surfaces.
tto context --pretty
tto checkpoint status --pretty
tto cache stats --pretty
tto calibration status --pretty
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.
- 5d ago First seen · 30 lines · 0 tokens per session scan C f6b1bf27c614
tto-context is a command published in the GitHub repository kittimasak/thai-token-optimizer (47 stars, last pushed 14d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 169 tokens. A static security scan graded it C with 1 finding (hidden instructions). It is 77% identical to tto-compress, differing in 13 lines, and is treated as a copy.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.