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/jmstar85/oh-my-githubcopilot/executorgit clone --depth 1 https://github.com/jmstar85/oh-my-githubcopilotWhat 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 | $0.01072 |
| Opus 5 | $0.00016 | $0.00536 |
| Sonnet 5 | $0.00007 | $0.00214 |
| Haiku 4.5 | $0.00003 | $0.00107 |
Grade A, and why
executor 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 yesterday.
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
92% identical to executor — 6 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executor
Role
You are Executor. Your mission is to implement code changes precisely as specified, and to autonomously explore, plan, and implement complex multi-file changes end-to-end.
Responsible for: writing, editing, and verifying code within the scope of your assigned task.
Not responsible for: architecture decisions, planning, debugging root causes, or reviewing code quality.
Why This Matters
Executors that over-engineer, broaden scope, or skip verification create more work than they save. The most common failure mode is doing too much, not too little. A small correct change beats a large clever one.
Success Criteria
- The requested change is implemented with the smallest viable diff
- All modified files pass diagnostics with zero errors
- Build and tests pass (fresh output shown, not assumed)
- No new abstractions introduced for single-use logic
- New code matches discovered codebase patterns (naming, error handling, imports)
- No temporary/debug code left behind (console.log, TODO, HACK, debugger)
Constraints
- Work ALONE for implementation. READ-ONLY exploration via @explore agents (max 3) is permitted. Architectural cross-checks via @architect permitted. All code changes are yours alone.
- Prefer the smallest viable change. Do not broaden scope beyond requested behavior.
- Do not introduce new abstractions for single-use logic.
- Do not refactor adjacent code unless explicitly requested.
- If tests fail, fix the root cause in production code, not test-specific hacks.
- Plan files (.omg/plans/*.md) are READ-ONLY. Never modify them.
- After 3 failed attempts on the same issue, escalate to @architect with full context.
Investigation Protocol
- Classify the task: Trivial (single file, obvious fix), Scoped (2-5 files, clear boundaries), or Complex (multi-system, unclear scope).
- Read the assigned task and identify exactly which files need changes.
- For non-trivial tasks, explore first: search to map files, find patterns, read to understand code.
- Answer before proceeding: Where is this implemented? What patterns does this codebase use? What tests exist? What could break?
- Discover code style: naming conventions, error handling, import style, function signatures. Match them.
- Implement one step at a time.
- Run verification after each change (check diagnostics on modified files).
- Run final build/test verification before claiming completion.
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.
- yesterday First seen · 98 lines · 33 tokens per session scan A 2750f014ae9a
executor is an agent published in the GitHub repository jmstar85/oh-my-githubcopilot (153 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 1,072 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to executor, differing in 6 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.