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/ariegoldkin/claude-forge/prompt-cachinggit clone --depth 1 https://github.com/ArieGoldkin/claude-forgeWrote 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/ariegoldkin/claude-forge/prompt-caching)<a href="https://agentmods.dev/commands/ariegoldkin/claude-forge/prompt-caching"><img src="https://agentmods.dev/badge/commands/ariegoldkin/claude-forge/prompt-caching.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 5.1 | $0.00054 | $0.00090 |
| Opus 5 | $0.00027 | $0.00045 |
| Sonnet 5 | $0.00011 | $0.00018 |
| Haiku 4.5 | $0.00005 | $0.00009 |
Grade A, and why
prompt-caching 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 6d 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.
What it actually says
prompt-caching
Invoking skill: atk:prompt-caching
Follow the instructions in the prompt-caching skill exactly.
$ARGUMENTS
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.
- 6d ago First seen · 12 lines · 54 tokens per session scan A 91cf233213b8
prompt-caching is a command published in the GitHub repository ArieGoldkin/claude-forge (6 stars, last pushed 28d ago), licensed MIT. It adds 54 tokens to every session and 90 once invoked, about $0.0003 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 commands, from other repositories
octo-meta-prompt
"Generate an optimized prompt for any task using meta-prompting techniques".
devkit.prompt-optimize
Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions.
dev-ai-integration
Integration of language models (LLM) and AI APIs into applications.
ai
Invoke the AI/LLM Application Engineer for RAG, agents, prompt engineering, evals, tool use, and LLM guardrails.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
text-classification
Apply LLM-based text classification expertise to the task below. Cover codebook design (Halterman & Keith format), learning regime selection, human-LLM hybrid workflows, cross-model validation, and agreement statistics as relevant.