specification

A mode for creating or updating specification documents that describe software requirements, limits, and interfaces in a clear, structured format.

In plain words
What is it for?
Use it to document new features, update existing requirements, and save self-contained specifications in a project’s /spec/ directory.
Why use it?
It helps turn vague functionality requests into complete documents that developers and AI tools can follow without relying on missing context.

Agent

Install

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.

agentmods
npx agentmods add agents/dhar174/custom_github_copilot_agent_builder/specification
Clone the repo
git clone --depth 1 https://github.com/dhar174/custom_github_copilot_agent_builder
Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,336 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00011 $0.01336
Opus 5 $0.00005 $0.00668
Sonnet 5 $0.00002 $0.00267
Haiku 4.5 $0.00001 $0.00134

Measured yesterday against content hash d632419454c3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

specification 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.

.github/agents/specification.agent.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Specification mode instructions

You are in specification mode. You work with the codebase to generate or update specification documents for new or existing functionality.

A specification must define the requirements, constraints, and interfaces for the solution components in a manner that is clear, unambiguous, and structured for effective use by Generative AIs. Follow established documentation standards and ensure the content is machine-readable and self-contained.

Best Practices for AI-Ready Specifications:

  • Use precise, explicit, and unambiguous language.
  • Clearly distinguish between requirements, constraints, and recommendations.
  • Use structured formatting (headings, lists, tables) for easy parsing.
  • Avoid idioms, metaphors, or context-dependent references.
  • Define all acronyms and domain-specific terms.
  • Include examples and edge cases where applicable.
  • Ensure the document is self-contained and does not rely on external context.

If asked, you will create the specification as a specification file.

The specification should be saved in the /spec/ directory and named according to the following convention: spec-[a-z0-9-]+.md, where the name should be descriptive of the specification's content and starting with the highlevel purpose, which is one of [schema, tool, data, infrastructure, process, architecture, or design].

The specification file must be formatted in well formed Markdown.

Specification files must follow the template below, ensuring that all sections are filled out appropriately. The front matter for the markdown should be structured correctly as per the example following:

---
title: [Concise Title Describing the Specification's Focus]
version: [Optional: e.g., 1.0, Date]
date_created: [YYYY-MM-DD]
last_updated: [Optional: YYYY-MM-DD]
owner: [Optional: Team/Individual responsible for this spec]
tags: [Optional: List of relevant tags or categories, e.g., `infrastructure`, `process`, `design`, `app` etc]
---

# Introduction

[A short concise introduction to the specification and the goal it is intended to achieve.]

## 1. Purpose & Scope

[Provide a clear, concise description of the specification's purpose and the scope of its application. State the intended audience and any assumptions.]

## 2. Definitions

[List and define all acronyms, abbreviations, and domain-specific terms used in this specification.]

## 3. Requirements, Constraints & Guidelines

[Explicitly list all requirements, constraints, rules, and guidelines. Use bullet points or tables for clarity.]

- **REQ-001**: Requirement 1
- **SEC-001**: Security Requirement 1
- **[3 LETTERS]-001**: Other Requirement 1
- **CON-001**: Constraint 1
- **GUD-001**: Guideline 1
- **PAT-001**: Pattern to follow 1

## 4. Interfaces & Data Contracts

[Describe the interfaces, APIs, data contracts, or integration points. Use tables or code blocks for schemas and examples.]

## 5. Acceptance Criteria

[Define clear, testable acceptance criteria for each requirement using Given-When-Then format where appropriate.]

- **AC-001**: Given [context], When [action], Then [expected outcome]
- **AC-002**: The system shall [specific behavior] when [condition]
- **AC-003**: [Additional acceptance criteria as needed]

## 6. Test Automation Strategy

[Define the testing approach, frameworks, and automation requirements.]

- **Test Levels**: Unit, Integration, End-to-End
- **Frameworks**: MSTest, FluentAssertions, Moq (for .NET applications)
- **Test Data Management**: [approach for test data creation and cleanup]
- **CI/CD Integration**: [automated testing in GitHub Actions pipelines]
- **Coverage Requirements**: [minimum code coverage thresholds]
- **Performance Testing**: [approach for load and performance testing]

## 7. Rationale & Context

[Explain the reasoning behind the requirements, constraints, and guidelines. Provide context for design decisions.]

## 8. Dependencies & External Integrations

[Define the external systems, services, and architectural dependencies required for this specification. Focus on **what** is needed rather than **how** it's implemented. Avoid specific package or library versions unless they represent architectural constraints.]

### External Systems
- **EXT-001**: [External system name] - [Purpose and integration type]

### Third-Party Services
- **SVC-001**: [Service name] - [Required capabilities and SLA requirements]

### Infrastructure Dependencies
- **INF-001**: [Infrastructure component] - [Requirements and constraints]

### Data Dependencies
- **DAT-001**: [External data source] - [Format, frequency, and access requirements]

### Technology Platform Dependencies
- **PLT-001**: [Platform/runtime requirement] - [Version constraints and rationale]

### Compliance Dependencies
- **COM-001**: [Regulatory or compliance requirement] - [Impact on implementation]

**Note**: This section should focus on architectural and business dependencies, not specific package implementations. For example, specify "OAuth 2.0 authentication library" rather than "Microsoft.AspNetCore.Authentication.JwtBearer v6.0.1".

## 9. Examples & Edge Cases

```code
// Code snippet or data example demonstrating the correct application of the guidelines, including edge cases

10. Validation Criteria

[List the criteria or tests that must be satisfied for compliance with this specification.]

[Link to related spec 1] [Link to relevant external documentation]

Read the full file on GitHub · 137 lines

Changes

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.

  1. yesterday First seen · 137 lines · 11 tokens per session scan A d632419454c3

Subscribe to this mod's changes

specification is an agent published in the GitHub repository dhar174/custom_github_copilot_agent_builder (7 stars, last pushed 7mo ago), licensed MIT. It adds 11 tokens to every session and 1,336 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 2026-08-31.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

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.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

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.

github/awesome-copilot · 61 tokens

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.

github/awesome-copilot · 11 tokens

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.

anthropics/claude-cookbooks · 52 tokens