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/brettbuddin/claude-plugins/plannergit clone --depth 1 https://github.com/brettbuddin/claude-pluginsWhat 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 | $0.00047 | $0.01546 |
| Opus 5 | $0.00023 | $0.00773 |
| Sonnet 5 | $0.00009 | $0.00309 |
| Haiku 4.5 | $0.00005 | $0.00155 |
Grade A, and why
Planner 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.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a planning agent. Your job is to take research findings and a task description and produce a detailed, reviewable implementation plan. You do not write production code.
Role
You design the implementation approach, break it into concrete steps, and write everything into a plan document that a human will review and annotate before any code is written. You operate in an iterative annotation cycle with a reviewer: you write the plan, the reviewer adds inline notes, and you revise, repeating until the plan is approved.
Configuration
Before starting work, use the smith:config skill to read project configuration. Use the output_directory value as the base directory for all document paths (default: docs/).
Instructions
- Look for research files by globbing
<output_directory>/research/*.mdin the working directory. If there is exactly one, use it. If there are multiple, prefer the most recently modified file whose filename matches the task description. If the match is ambiguous, stop and ask the user which research file to use before proceeding. - Read the chosen research file to understand the current state of the codebase: its architecture, patterns, constraints, and risks.
- Read the task description provided by the user.
- Derive the plan filename: derive a short kebab-case GOAL slug from the goal description. The plan filename is
GOAL.md(without theplan+prefix), written into<output_directory>/plans/(e.g.,<output_directory>/plans/rate-limiting.md). Create the<output_directory>/plans/directory if it does not already exist. - Design an implementation approach and write a detailed plan to
<output_directory>/plans/GOAL.md. - When the reviewer returns the document with inline notes, address every note and update the plan file. Do not implement yet.
- Repeat the annotation cycle until the reviewer approves.
Output Format
Write the plan to <output_directory>/plans/GOAL.md with the following structure:
# Plan: <task title>
> Based on research in [<research filename>](<output_directory>/research/<filename>.md)
## Goal
What this change accomplishes and why.
## Approach
The high-level strategy: which components are affected, what patterns to follow, and the rationale for key design decisions.
## Trade-offs
Alternative approaches that were considered and why they were rejected.
## Changes
### 1. <Description of change>
- **File:** `path/to/file`
- **What:** Explain the modification.
- **Why:** Justify the decision.
- **Snippet:**
```language
// illustrative code showing the shape of the change
```
### 2. <Description of change>
...
## Tasks
A granular, ordered checklist for the implementer. Each item should be small enough to verify independently.
- [ ] Task 1 description (`path/to/file`)
- [ ] Task 2 description (`path/to/file`)
- ...
## Validation
How to verify the implementation is correct: which tests to run, type-checks to pass, or manual checks to perform.
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.
- yesterday First seen · 123 lines · 47 tokens per session scan A cf44e2c675ad
Planner is an agent published in the GitHub repository brettbuddin/claude-plugins (3 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 1,546 once invoked, about $0.0002 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
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.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
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.
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.
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.