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 agents/xsaven/vector-memory-mcp/prompt-mastergit clone --depth 1 https://github.com/Xsaven/vector-memory-mcpWrote 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/agents/xsaven/vector-memory-mcp/prompt-master)<a href="https://agentmods.dev/agents/xsaven/vector-memory-mcp/prompt-master"><img src="https://agentmods.dev/badge/agents/xsaven/vector-memory-mcp/prompt-master.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 | $0.00033 | $0.09053 |
| Opus 5 | $0.00016 | $0.04526 |
| Sonnet 5 | $0.00007 | $0.01811 |
| Haiku 4.5 | $0.00003 | $0.00905 |
Grade A, and why
prompt-master 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 3d 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.
This is a copy
84% identical to agent-master — 142 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.
How it starts
The opening of the file, as written. The whole thing — 825 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This system agent maintains full meta-awareness of its own architecture, capabilities, limitations, and design patterns. Its core purpose is to iteratively improve itself, document its evolution, and engineer new specialized subagents with well-defined roles, contracts, and behavioral constraints. It reasons like a self-refining compiler: validating assumptions, preventing uncontrolled mutation, preserving coherence, and ensuring every new agent is safer, clearer, and more efficient than the previous generation.
Defines the standardized 4-phase lifecycle for Claude Code agents within the Brain system. Ensures consistent creation, validation, optimization, and maintenance cycles.
Brain compilation system knowledge: namespaces, PHP API, archetype structures. MANDATORY scanning of actual source files before code generation.
Scanning workflow
MANDATORY scanning sequence before code generation.
scan-1: Glob('.brain/vendor/jarvis-brain/core/src/Compilation/**/*.php')scan-2: Read(.brain/vendor/jarvis-brain/core/src/Compilation/Runtime.php) → [Extract: constants, static methods with signatures] → END-Readscan-3: Read(.brain/vendor/jarvis-brain/core/src/Compilation/Operator.php) → [Extract: ALL static methods (if, forEach, task, verify, validate, etc.)] → END-Readscan-4: Read(.brain/vendor/jarvis-brain/core/src/Compilation/Store.php) → [Extract: as(), get() signatures] → END-Readscan-5: Read(.brain/vendor/jarvis-brain/core/src/Compilation/BrainCLI.php) → [Extract: ALL constants and static methods] → END-Readscan-6: Glob('.brain/vendor/jarvis-brain/core/src/Compilation/Tools/*.php')scan-7: Read(.brain/vendor/jarvis-brain/core/src/Abstracts/ToolAbstract.php) → [Extract: call(), describe() base methods] → END-Readscan-8: Glob('.brain/node/Mcp/*.php')scan-9: Read MCP classes → Extract ::call(name, ...args) and ::id() patternsready: NOW you can generate code using ACTUAL API from source
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.
- 3d ago First seen · 825 lines · 33 tokens per session scan A 1872a5293e6d
prompt-master is an agent published in the GitHub repository Xsaven/vector-memory-mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 33 tokens to every session and 9,053 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to agent-master, differing in 142 lines, and is treated as a copy.
Other agents, from other repositories
prompt-engineering-expert
Provides expert prompt engineering capabilities specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use PROACTIVELY for prompt creation, optimization, document/code analysis prompts, or AI system design.…
prompt-engineer
Author, restructure, and evaluate the text that steers a model. TRIGGER WHEN: writing system prompts, designing agent instructions, or optimizing prompt performance for reliability and token efficiency.
hyv-veo-prompt-smith
The generative-prompt writer for HearYourVOICE (Phase 4). Looks at the shots still MISSING a source in the shotlist (after CC scouting) and writes copy/paste generation prompts to fill exactly those gaps — no more. Builds each prompt from the measured durations and the veo-prompt guide, applying subject-lock and…
prompting
Agent "prompting" from bestdeejay-design/awesome-ai-handbook, covering prompting for ai agents, 1. how agent prompting differs, 2. system prompt structure, role and tools.
prompt-debugger
Evaluates why a prompt produced bad, unexpected, or suboptimal output and suggests targeted fixes. Use when a user says "my prompt isn't working", "this prompt gives bad results", "why is my prompt failing", "debug this prompt", "the AI keeps getting this wrong", "fix my prompt", "prompt not producing expected…
prompts-guide
Interactive guide for using prompt-factory skill to generate mega-prompts. Helps choose from 69 presets or create custom prompts, select formats (XML/Claude/ChatGPT/Gemini), and explains usage. Use when user wants to generate production-ready prompts for any LLM.