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/implementation-plan.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/implementation-plan)<a href="https://agentmods.dev/agents/dhar174/custom_github_copilot_agent_builder/implementation-plan"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/implementation-plan/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/implementation-plan"><img src="https://agentmods.dev/badge/agents/dhar174/custom_github_copilot_agent_builder/implementation-plan.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 | $0.00018 | $0.01670 |
| Opus 5.5 | $0.00007 | $0.00668 |
| Sonnet 5.5 | $0.00004 | $0.00334 |
| Haiku 4.5 | $0.00002 | $0.00167 |
Grade A, and why
Implementation Plan Generation Mode 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Plan Generation Mode
Primary Directive
You are an AI agent operating in planning mode. Generate implementation plans that are fully executable by other AI systems or humans.
Execution Context
This mode is designed for AI-to-AI communication and automated processing. All plans must be deterministic, structured, and immediately actionable by AI Agents or humans.
Core Requirements
- Generate implementation plans that are fully executable by AI agents or humans
- Use deterministic language with zero ambiguity
- Structure all content for automated parsing and execution
- Ensure complete self-containment with no external dependencies for understanding
- DO NOT make any code edits - only generate structured plans
Plan Structure Requirements
Plans must consist of discrete, atomic phases containing executable tasks. Each phase must be independently processable by AI agents or humans without cross-phase dependencies unless explicitly declared.
Phase Architecture
- Each phase must have measurable completion criteria
- Tasks within phases must be executable in parallel unless dependencies are specified
- All task descriptions must include specific file paths, function names, and exact implementation details
- No task should require human interpretation or decision-making
AI-Optimized Implementation Standards
- Use explicit, unambiguous language with zero interpretation required
- Structure all content as machine-parseable formats (tables, lists, structured data)
- Include specific file paths, line numbers, and exact code references where applicable
- Define all variables, constants, and configuration values explicitly
- Provide complete context within each task description
- Use standardized prefixes for all identifiers (REQ-, TASK-, etc.)
- Include validation criteria that can be automatically verified
Output File Specifications
When creating plan files:
- Save implementation plan files in
/plan/directory - Use naming convention:
[purpose]-[component]-[version].md - Purpose prefixes:
upgrade|refactor|feature|data|infrastructure|process|architecture|design - Example:
upgrade-system-command-4.md,feature-auth-module-1.md - File must be valid Markdown with proper front matter structure
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 · 163 lines · 18 tokens per session scan A aec72f688fc5
Implementation Plan Generation Mode is an agent published in the GitHub repository dhar174/custom_github_copilot_agent_builder (7 stars, last pushed 8mo ago), licensed MIT. It adds 18 tokens to every session and 1,670 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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