VaultysClaw agent-controller.instructions.md

Project-specific instructions for working on the agent-controller package, which contains an AI agent runtime, tools, integrations, and command-line modes. It defines conventions for imports and adding tools.

In plain words
What is it for?
Adding tools, changing agent runtime behavior, working with the WebSocket client or local database, and modifying LLM or CLI integration.
Why use it?
It keeps changes consistent with this package’s structure and approval rules. It also explains how new tools must be registered so the runtime can use them.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/vaultys/vaultysclaw/agent-controller
Clone the repo
git clone --depth 1 https://github.com/vaultys/VaultysClaw

Made for: GitHub Copilot.

Per session 1,113 This file is loaded in full into every session.
When invoked 1,113 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01113 $0.01113
Opus 5 $0.00557 $0.00557
Sonnet 5 $0.00223 $0.00223
Haiku 4.5 $0.00111 $0.00111

Measured 2d ago against content hash fb31a6e25190, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

VaultysClaw agent-controller.instructions.md 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 2d 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.

.github/instructions/agent-controller.instructions.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Controller Conventions

Module Imports

No @/* alias — use relative paths for local imports:

import { shellTool } from "./tools/shell";
import { getDb } from "../db";

Cross-package: import { AgentCapability, WSMessage } from "@vaultysclaw/shared" — never relative paths across package boundaries.

Adding a Tool

Create src/tools/<name>.ts, export a single AgentToolDefinition constant:

import { createTool } from "@mastra/core/tools";
import { z } from "zod";
import type { AgentToolDefinition } from "./types";

export const myTool: AgentToolDefinition = {
  name: "my-tool",
  capability: "internet_access", // see AgentCapability below
  requiresApproval: false, // true for system-modifying operations
  tool: createTool({
    id: "my-tool",
    description: "...",
    inputSchema: z.object({ url: z.string() }),
    execute: async ({ url }) => {
      /* ... */
    },
  }),
};

Then register in src/tools/index.ts by adding to the builtIn array inside createToolRegistry().

requiresApproval: true for anything that writes files, runs shell commands, or executes code.

AgentCapability values: file_access | internet_access | browser_control | api_call | mail_send | code_execution | system_command | agent_communication | knowledge_search

Adding a Skill

Skills are runtime-loaded plugins — they must ship as compiled .js/.mjs, not .ts.

Structure a local skill under skills/<name>/index.ts, compiled to skills/<name>/index.js:

import type { SkillDefinition } from "../../src/skills/types";

export const skill: SkillDefinition = {
  name: "my-skill",
  description: "...",
  version: "1.0.0",
  tools: [
    /* AgentToolDefinition[] */
  ],
  systemPromptExtension: "Optional: guidance for the LLM about these tools.",
};

Accepted export forms: export const skill = ... or export default { skill: ... }.

The loader scans SKILLS_DIR for .js/.mjs files and index.js/index.mjs inside subdirectories. New skills require a restart (or trigger SkillLoader.load()) unless SKILLS_WATCH=true.

Read the full file on GitHub · 121 lines

Changes

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.

  1. 2d ago First seen · 121 lines · 1,113 tokens per session scan A fb31a6e25190

Subscribe to this mod's changes

VaultysClaw agent-controller.instructions.md is an instructions file published in the GitHub repository vaultys/VaultysClaw (77 stars, last pushed 7d ago), licensed MIT. It adds 1,113 tokens to every session, about $0.0056 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.