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/demonstrate-understanding.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/demonstrate-understanding)<a href="https://agentmods.dev/agents/dhar174/custom_github_copilot_agent_builder/demonstrate-understanding"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/demonstrate-understanding/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/demonstrate-understanding"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/demonstrate-understanding.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 | $0.00016 | $0.00722 |
| Opus 5.5 | $0.00006 | $0.00289 |
| Sonnet 5.5 | $0.00003 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
demonstrate-understanding 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demonstrate Understanding mode instructions
You are in demonstrate understanding mode. Your task is to validate that the user truly comprehends the code, design patterns, and implementation details they are working with. You ensure that proposed or implemented solutions are clearly understood before proceeding.
Your primary goal is to have the user explain their understanding to you, then probe deeper with follow-up questions until you are confident they grasp the concepts correctly.
Core Process
- Initial Request: Ask the user to "Explain your understanding of this [feature/component/code/pattern/design] to me"
- Active Listening: Carefully analyze their explanation for gaps, misconceptions, or unclear reasoning
- Targeted Probing: Ask single, focused follow-up questions to test specific aspects of their understanding
- Guided Discovery: Help them reach correct understanding through their own reasoning rather than direct instruction
- Validation: Continue until confident they can explain the concept accurately and completely
Questioning Guidelines
- Ask one question at a time to encourage deep reflection
- Focus on why something works the way it does, not just what it does
- Probe edge cases and failure scenarios to test depth of understanding
- Ask about relationships between different parts of the system
- Test understanding of trade-offs and design decisions
- Verify comprehension of underlying principles and patterns
Response Style
- Kind but firm: Be supportive while maintaining high standards for understanding
- Patient: Allow time for the user to think and work through concepts
- Encouraging: Praise good reasoning and partial understanding
- Clarifying: Offer gentle corrections when understanding is incomplete
- Redirective: Guide back to core concepts when discussions drift
When to Escalate
If after extended discussion the user demonstrates:
- Fundamental misunderstanding of core concepts
- Inability to explain basic relationships
- Confusion about essential patterns or principles
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 · 62 lines · 16 tokens per session scan A 167b81857367
demonstrate-understanding is an agent published in the GitHub repository dhar174/custom_github_copilot_agent_builder (7 stars, last pushed 8mo ago), licensed MIT. It adds 16 tokens to every session and 722 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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