ClaudeBox is a Docker-based development environment that runs Claude Code, an AI coding assistant, inside isolated containers with preset development profiles. It is for developers who want reproducible environments for projects in languages such as C/C++, Python, Rust, and Go, while retaining project settings and data between sessions. Its catalogue add-ons are commands and instructions for managing ClaudeBox environments, profiles, and related workflows.
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.
git clone --depth 1 https://github.com/RchGrav/claudeboxWrote 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/rchgrav/claudebox/adaptive)<a href="https://agentmods.dev/commands/rchgrav/claudebox/adaptive"><img src="https://agentmods.dev/badge/commands/rchgrav/claudebox/adaptive/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/commands/rchgrav/claudebox/adaptive"><img src="https://agentmods.dev/badge/commands/rchgrav/claudebox/adaptive.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 | $0.00000 | $0.08943 |
| Opus 5.5 | $0.00000 | $0.03577 |
| Sonnet 5.5 | $0.00000 | $0.01789 |
| Haiku 4.5 | $0.00000 | $0.00894 |
Grade A, and why
adaptive 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 582 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Next-Generation Agentic Workflow Orchestrator for Claude Code
System Overview
This orchestration system is a highly advanced agentic meta-prompt designed for Claude Code. It enables skilled, autonomous coding workflows that can tackle complex tasks while iteratively self-improving. It combines an interactive planning phase (leveraging Claude Code's Plan Mode), dynamic multi-agent execution, and continuous evaluation loops. The orchestrator seamlessly switches between collaborative dialogue and autonomous operation, ensuring the user remains in control when needed while the agent works independently towards the goal.
Key Features:
- Plan Mode Integration: Begins with a read-only analysis of the project (Plan Mode) to outline a solution and ask clarifying questions, ensuring a solid plan before coding.
- Polymorphic Variable System: Uses evolving variables to track state, progress, and learnings across iterations, enabling the workflow to adapt and refine itself over time.
- Iterative Self-Improvement: Implements an infinite-agentic-loop style refinement: generating solutions, evaluating them with fresh unbiased agents, and feeding the insights back for the next iteration.
- Hybrid Autonomy: Can operate in fully autonomous mode for well-defined tasks or interactive mode for ambiguous tasks, with dynamic switching based on confidence and user preference.
- User Interaction & Control: Provides periodic progress updates and junctures for user input (configurable), preventing runaway processes and keeping the user involved in decision-making for better quality output.
- MCP (Model-Context Protocol) Enabled: Automatically detects and connects to relevant MCP servers (like GitHub, filesystem, databases, etc.) for enriched context and real-world integration, without manual setup.
- Quality and Performance Focus: Enforces high coding standards, comprehensive testing, and performance checks on each iteration. If the solution doesn't meet quality thresholds, the loop continues (or asks for guidance) until it does.
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.
- 1mo ago First seen · 582 lines · 0 tokens per session scan A 76152553477c
adaptive is a command published in the GitHub repository RchGrav/claudebox (1,172 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 8,943 tokens. 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.