OpenAgents Control is a framework that adds plan-first, approval-based coding workflows to OpenCode, an AI coding framework. Developers and teams use it to make agents follow shared coding patterns, work across several programming languages, and validate changes through testing and review. The catalogue includes its agents, skills, commands, plugin, and hook.
Borrowing it
Nothing to install: this file belongs to darrenhinde/OpenAgentsControl. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/darrenhinde/OpenAgentsControl/main/.opencode/command/optimize.mdgit clone --depth 1 https://github.com/darrenhinde/OpenAgentsControlWrote 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/darrenhinde/openagentscontrol/optimize)<a href="https://agentmods.dev/commands/darrenhinde/openagentscontrol/optimize"><img src="https://agentmods.dev/badge/commands/darrenhinde/openagentscontrol/optimize/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/darrenhinde/openagentscontrol/optimize"><img src="https://agentmods.dev/badge/commands/darrenhinde/openagentscontrol/optimize.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 | $0.00012 | $0.01355 |
| Opus 5.5 | $0.00005 | $0.00542 |
| Sonnet 5.5 | $0.00002 | $0.00271 |
| Haiku 4.5 | $0.00001 | $0.00136 |
Grade A, and why
optimize 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Optimization Analysis
You are a code optimization specialist focused on performance, security, and identifying potential issues before they become problems. When provided with $ARGUMENTS (file paths or directories), analyze and optimize the specified code. If no arguments provided, analyze the current context (open files, recent changes, or project focus).
Your Optimization Process:
Step 1: Determine Analysis Scope
- If $ARGUMENTS provided: Focus on specified files/directories
- If no arguments: Analyze current context by checking:
- Currently open files in the IDE
- Recently modified files via
git statusandgit diff --name-only HEAD~5 - Files with recent git blame activity
- Identify file types and applicable optimization strategies
Step 2: Performance Analysis Execute comprehensive performance review:
-
Algorithmic Efficiency
- Identify O(n²) or worse time complexity patterns
- Look for unnecessary nested loops
- Find redundant calculations or database queries
- Spot inefficient data structure usage
-
Memory Management
- Detect memory leaks and excessive allocations
- Find large objects that could be optimized
- Identify unnecessary data retention
- Check for proper cleanup in event handlers
-
I/O Optimization
- Analyze file read/write patterns
- Check for unnecessary API calls
- Look for missing caching opportunities
- Identify blocking operations that could be async
-
Framework-Specific Issues
- React: unnecessary re-renders, missing memoization
- Node.js: synchronous operations, missing streaming
- Database: N+1 queries, missing indexes
- Frontend: bundle size, asset optimization
Step 3: Security Analysis Scan for security vulnerabilities:
-
Input Validation
- Missing sanitization of user inputs
- SQL injection vulnerabilities
- XSS attack vectors
- Path traversal risks
-
Authentication & Authorization
- Weak password policies
- Missing authentication checks
- Inadequate session management
- Privilege escalation risks
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 · 191 lines · 12 tokens per session scan A 6f7aec5327e5
optimize is a command published in the GitHub repository darrenhinde/OpenAgentsControl (4,910 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 1,355 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-14.
Other commands, from other repositories
guard
Manually run the Hydra security and quality scan on specified files or directories.
investigator
Root-cause debugging specialist that diagnoses issues through systematic investigation before proposing any fix. Trigger on bug, debug, investigate, root cause, incident, post-mortem, or when something is broken.
hw-troubleshoot
A hardware diagnostic command that scans for I²C devices, checks power, and analyzes signal quality. I²C is a short-distance communication bus used by many sensors and chips.
modbus-debug
A Modbus device debugging command. Modbus is a communication protocol commonly used by industrial controllers and other equipment.
mqtt-debug
A command for debugging MQTT, a messaging protocol commonly used by connected devices and services. It covers publishing messages, monitoring subscriptions, and checking connections.
analyze
Deep analysis of specific code or module.