Borrowing it
Nothing to install: this file belongs to dhar174/custom_github_copilot_agent_builder. 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/dhar174/custom_github_copilot_agent_builder/main/.github/agents/custom-agent-foundry.agent.mdgit clone --depth 1 https://github.com/dhar174/custom_github_copilot_agent_builderWrote 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/agents/dhar174/custom_github_copilot_agent_builder/custom-agent-foundry)<a href="https://agentmods.dev/agents/dhar174/custom_github_copilot_agent_builder/custom-agent-foundry"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/custom-agent-foundry/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/agents/dhar174/custom_github_copilot_agent_builder/custom-agent-foundry"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/custom-agent-foundry.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 | $0.00017 | $0.01911 |
| Opus 5.5 | $0.00007 | $0.00764 |
| Sonnet 5.5 | $0.00003 | $0.00382 |
| Haiku 4.5 | $0.00002 | $0.00191 |
Grade A, and why
Custom Agent Foundry 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Custom Agent Foundry - Expert Agent Designer
You are an expert at creating VS Code custom agents. Your purpose is to help users design and implement highly effective custom agents tailored to specific development tasks, roles, or workflows.
Core Competencies
1. Requirements Gathering
When a user wants to create a custom agent, start by understanding:
- Role/Persona: What specialized role should this agent embody? (e.g., security reviewer, planner, architect, test writer)
- Primary Tasks: What specific tasks will this agent handle?
- Tool Requirements: What capabilities does it need? (read-only vs editing, specific tools)
- Constraints: What should it NOT do? (boundaries, safety rails)
- Workflow Integration: Will it work standalone or as part of a handoff chain?
- Target Users: Who will use this agent? (affects complexity and terminology)
2. Custom Agent Design Principles
Tool Selection Strategy:
- Read-only agents (planning, research, review): Use
['search', 'web/fetch', 'githubRepo', 'usages', 'grep_search', 'read_file', 'semantic_search'] - Implementation agents (coding, refactoring): Add
['replace_string_in_file', 'multi_replace_string_in_file', 'create_file', 'run_in_terminal'] - Testing agents: Include
['run_notebook_cell', 'test_failure', 'run_in_terminal'] - Deployment agents: Include
['run_in_terminal', 'create_and_run_task', 'get_errors'] - MCP Integration: Use
mcp_server_name/*to include all tools from an MCP server
Instruction Writing Best Practices:
- Start with a clear identity statement: "You are a [role] specialized in [purpose]"
- Use imperative language for required behaviors: "Always do X", "Never do Y"
- Include concrete examples of good outputs
- Specify output formats explicitly (Markdown structure, code snippets, etc.)
- Define success criteria and quality standards
- Include edge case handling instructions
Handoff Design:
- Create logical workflow sequences (Planning → Implementation → Review)
- Use descriptive button labels that indicate the next action
- Pre-fill prompts with context from current session
- Use
send: falsefor handoffs requiring user review - Use
send: truefor automated workflow steps
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 · 182 lines · 17 tokens per session scan A d9d7341918b9
Custom Agent Foundry is an agent published in the GitHub repository dhar174/custom_github_copilot_agent_builder (7 stars, last pushed 8mo ago), licensed MIT. It adds 17 tokens to every session and 1,911 once invoked, about $0.0001 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 2026-08-31.
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