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 skills add Kastalien-Research/thoughtbox --skill claude-promptgit clone --depth 1 https://github.com/Kastalien-Research/thoughtboxWrote 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/kastalien-research/thoughtbox/claude-prompt)<a href="https://agentmods.dev/skills/kastalien-research/thoughtbox/claude-prompt"><img src="https://agentmods.dev/badge/skills/kastalien-research/thoughtbox/claude-prompt/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/kastalien-research/thoughtbox/claude-prompt"><img src="https://agentmods.dev/badge/skills/kastalien-research/thoughtbox/claude-prompt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.01585 |
| Opus 5.5 | $0.00022 | $0.00634 |
| Sonnet 5.5 | $0.00011 | $0.00317 |
| Haiku 4.5 | $0.00005 | $0.00159 |
Grade A, and why
Codex-prompt 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 1mo 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
$ARGUMENTS
Your Task
You are a prompt engineer. If given an existing prompt, improve it. If given a description of what's needed, write the prompt from scratch. Apply every relevant principle below.
Codex 4.6 Prompting Best Practices
Source: Anthropic's official prompting guide. These are the current best practices as of Codex 4.6.
Structure
- Be clear and direct. Describe exactly what you want. If a colleague with no context would be confused by your prompt, Codex will be too.
- Add context for motivation. Explain why instructions matter — Codex generalizes from the reasoning. "Never use ellipses because TTS can't pronounce them" is stronger than "Never use ellipses."
- Use XML tags to separate content types:
<instructions>,<context>,<input>,<examples>. Consistent, descriptive names. Nest when content has hierarchy. - Give Codex a role. One sentence in the system prompt focuses behavior: "You are a senior backend engineer reviewing Python PRs."
- Positive framing. Say what TO DO, not what NOT to do. "Write in flowing prose paragraphs" beats "Do not use markdown."
- Sequential steps. Use numbered lists when order or completeness matters.
Examples (Few-Shot)
- 3-5 examples dramatically improve accuracy and consistency.
- Make them relevant (mirror real use cases), diverse (cover edge cases), and wrapped in
<example>tags. - You can ask Codex to evaluate your examples for quality or generate more from an initial set.
Long Context (20K+ tokens)
- Put longform data at the TOP, query and instructions at the BOTTOM (up to 30% quality lift).
- Wrap documents:
<document index="N">with<source>and<document_content>subtags. - Ask Codex to quote relevant passages before answering to cut through noise.
Output Control
- Codex 4.6 is more concise by default. Request summaries explicitly if you want them.
- Match prompt formatting style to desired output style. Markdown in = markdown out.
- For prose output, use:
<avoid_excessive_markdown_and_bullet_points>wrapper with explicit instructions. - LaTeX is the default for math. Request plain text explicitly if needed.
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.
- 1mo ago First seen · 130 lines · 54 tokens per session scan A 7bc782a61ed3
Codex-prompt is a skill published in the GitHub repository Kastalien-Research/thoughtbox (65 stars, last pushed 3mo ago), licensed MIT. It adds 54 tokens to every session and 1,585 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
opik-optimize
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octave-ultra-mythic
Ultra-high density compression using mythological atoms and semantic shorthand. Preserves soul and constraints at 60% compression for identity transmission, binding protocols, and extreme token scarcity. REQUIRES octave-mastery and octave-compression.
laravel-ai-sdk
Use when integrating AI agents, tool calling, embeddings, structured output, or streaming in Laravel 13 via the laravel/ai package.
prompt-library
Use when looking for a ready-made agent or task template (code review, support, data analysis, translation, extraction) instead of writing one from scratch.
prompt-creation
Use when writing a new system prompt from scratch, structuring it with Anthropic's 9-element format, or choosing emphasis levels and few-shot examples.
prompt-testing
Use when comparing two prompt variants, defining quality/efficiency/robustness metrics, or deciding whether to adopt a challenger prompt over a baseline.