runtime

Enforces bounded reasoning, tool budgets, and scoped execution policies for agent tasks.

1|Updated Jul 18, 2025
One-click install
npx skills add https://github.com/AdelysAlberto/md-configs-and-agents --skill runtime-adelysalberto
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: runtime
Source: https://github.com/AdelysAlberto/md-configs-and-agents/tree/main/agents-hermes/skills/runtime
Command: npx skills add https://github.com/AdelysAlberto/md-configs-and-agents --skill runtime-adelysalberto

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI agents often waste tokens and time on unbounded reasoning loops, redundant tool calls, and repository-wide exploration for localized tasks. This Skill defines a universal runtime policy that keeps agents efficient, focused, and verifiable. ## Core Features & Use Cases - Reasoning Circuit Breaker: Detects repeated reasoning, recursive planning, and unproductive analysis, then forces consolidation, decision, and execution. - Resource Budgets: Enforces strict tool, context, and scope budgets so agents prefer targeted operations over broad exploration. - Progressive Execution & Verification: Applies an Understand-Decide-Execute-Verify cycle with escalation rules for high-risk changes like security or architecture decisions. - Use Case: When an agent works on files under src/, this policy prevents it from scanning the entire repository, re-reading files, or endlessly reconsidering decisions, ensuring a verified result with minimal token usage. ## Quick Start Apply the runtime policy to keep the agent focused and efficient while modifying files under src/.

Frequently Asked Questions about runtime

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I stop an AI agent from looping during reasoning?▼

Use a reasoning circuit breaker that detects repeated conclusions, recursive planning, and unproductive analysis. When triggered, the agent consolidates evidence, picks the best available decision, executes, and verifies instead of continuing to loop.

How to reduce token usage in AI coding agents?▼

Enforce tool and context budgets: require every tool call to produce meaningful progress, prefer targeted file reads over directory scans, and reuse already-obtained information. Stop searching once sufficient evidence is available.

What is the best search strategy for AI agents using documentation?▼

Search with a specific question or hypothesis and prefer sources in order: official documentation, specifications, source repositories, then project code. Avoid inspecting node_modules or browsing directories without a concrete reason.

When should an AI agent escalate its reasoning depth?▼

Escalate only for architecture changes, security-sensitive operations, data integrity risks, destructive actions, complex cross-module dependencies, or ambiguous requirements. Routine tasks should use lightweight reasoning and targeted execution.

Does this runtime policy restrict repository-wide exploration?▼

Yes, for localized tasks repository-wide exploration is prohibited unless justified by evidence. Agents must start from files directly related to the request and expand scope only when necessary.