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 bobmatnyc/claude-mpm-agents --skill caveman-prompt-compressiongit clone --depth 1 https://github.com/bobmatnyc/claude-mpm-agentsWrote 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/bobmatnyc/claude-mpm-agents/caveman-prompt-compression)<a href="https://agentmods.dev/skills/bobmatnyc/claude-mpm-agents/caveman-prompt-compression"><img src="https://agentmods.dev/badge/skills/bobmatnyc/claude-mpm-agents/caveman-prompt-compression/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/bobmatnyc/claude-mpm-agents/caveman-prompt-compression"><img src="https://agentmods.dev/badge/skills/bobmatnyc/claude-mpm-agents/caveman-prompt-compression.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00025 | $0.01948 |
| Opus 5 | $0.00013 | $0.00974 |
| Sonnet 5 | $0.00005 | $0.00390 |
| Haiku 4.5 | $0.00003 | $0.00195 |
Grade A, and why
caveman-prompt-compression 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 11d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 11d ago First seen · 243 lines · 25 tokens per session scan A 3ba3c5d9343d
caveman-prompt-compression is a skill published in the GitHub repository bobmatnyc/claude-mpm-agents (16 stars, last pushed 2mo ago), with no licence file. It adds 25 tokens to every session and 1,948 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 2026-08-30.
Other skills, from other repositories
prompt-optimization
Use this skill when the user wants to optimize, modify, or improve the system prompt of an AI agent. This includes requests like 'optimize the prompt', 'make the AI more focused on X', 'change the system prompt', 'improve the agent behavior', or 'modify how the AI responds'.
dspy
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming.
dspy-optimize-anything
Use for GEPA optimizeanything on text artifacts such as code, prompts, agent architectures, configs, and non-DSPy optimization targets.
dspy-haystack-integration
Use for integrating DSPy with Haystack, optimizing Haystack prompts, improving retrieval pipelines, and extracting DSPy prompts.
dspy-miprov2-optimizer
Use for MIPROv2, Bayesian optimization, instruction and demo tuning, and high-performance DSPy program optimization.
dspy-output-refinement-constraints
Use for dspy.Refine, dspy.BestOfN, output constraints, validation, reward functions, and iterative output refinement.