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/github/gh-aw/custom-engine-implementationgit clone --depth 1 https://github.com/github/gh-awWhat 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 | $0.00012 | $0.06106 |
| Opus 5 | $0.00006 | $0.03053 |
| Sonnet 5 | $0.00002 | $0.01221 |
| Haiku 4.5 | $0.00001 | $0.00611 |
Grade A, and why
custom-engine-implementation 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 2d 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 — 826 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Custom Agentic Engine Implementation Guide
This document provides a comprehensive guide for implementing custom agentic engines in GitHub Agentic Workflows (gh-aw). It covers architecture patterns, common refactoring opportunities, and step-by-step implementation instructions.
Table of Contents
- Architecture Overview
- Engine Interface Design
- Common Code Analysis & Refactoring Opportunities
- Implementation Guide
- Testing Strategy
- Integration Checklist
Architecture Overview
Interface Segregation Principle
The agentic engine architecture follows the Interface Segregation Principle (ISP) to avoid forcing implementations to depend on methods they don't use. The system uses interface composition to provide flexibility while maintaining backward compatibility.
Interface Hierarchy
Engine (core identity - required by all)
├── GetID()
├── GetDisplayName()
├── GetDescription()
└── IsExperimental()
CapabilityProvider (feature detection - optional)
├── SupportsToolsAllowlist()
├── SupportsHTTPTransport()
├── SupportsMaxTurns()
├── SupportsWebFetch()
├── SupportsWebSearch()
├── SupportsFirewall()
├── SupportsPlugins()
└── SupportsLLMGateway()
WorkflowExecutor (compilation - required)
├── GetDeclaredOutputFiles()
├── GetInstallationSteps()
└── GetExecutionSteps()
MCPConfigProvider (MCP servers - optional)
└── RenderMCPConfig()
LogParser (log analysis - optional)
├── ParseLogMetrics()
├── GetLogParserScriptId()
└── GetLogFileForParsing()
SecurityProvider (security features - optional)
├── GetDefaultDetectionModel()
└── GetRequiredSecretNames()
CodingAgentEngine (composite - backward compatibility)
└── Composes all above interfaces
Key Architectural Patterns
- BaseEngine Embedding: All engines embed
BaseEnginewhich provides default implementations - Focused Interfaces: Each interface has a single responsibility
- Optional Capabilities: Engines override only the methods they need
- Backward Compatibility:
CodingAgentEnginecomposite interface maintains compatibility
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.
- 2d ago First seen · 826 lines · 12 tokens per session scan A 635973fb1c27
custom-engine-implementation is an agent published in the GitHub repository github/gh-aw (5,050 stars, last pushed 2d ago), licensed MIT. It adds 12 tokens to every session and 6,106 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
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