planning-agent

planning-agent is an agent for coding agents from ruslan-korneev/claude-plugins. It costs 24 tokens per session (1,104 once invoked), scanned A, original, MIT.

A planning agent that studies a codebase and turns a feature request into a detailed development plan with ordered tests and context notes.

In plain words
What is it for?
Use it to plan feature work, ask clarifying questions, identify files and services to change, and prepare a TDD plan.
Why use it?
It surfaces integration points, existing patterns, edge cases, breaking changes, and requirements before implementation begins.

Agent

Part of the tech-lead plugin — 14 commands, 9 agents shipped together

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/ruslan-korneev/claude-plugins/planning-agent
Clone the repo
git clone --depth 1 https://github.com/ruslan-korneev/claude-plugins

Or install tech-lead, the plugin that ships this one along with the rest of its 14 commands, 9 agents.

Wrote 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.

agentmods badge for planning-agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/ruslan-korneev/claude-plugins/planning-agent.svg)](https://agentmods.dev/agents/ruslan-korneev/claude-plugins/planning-agent)
Your own site
<a href="https://agentmods.dev/agents/ruslan-korneev/claude-plugins/planning-agent"><img src="https://agentmods.dev/badge/agents/ruslan-korneev/claude-plugins/planning-agent.svg" alt="Measured on agentmods" height="20"></a>
Per session 24 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,104 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 $0.00024 $0.01104
Opus 5 $0.00012 $0.00552
Sonnet 5 $0.00005 $0.00221
Haiku 4.5 $0.00002 $0.00110

Measured 3d ago against content hash 19e357a7a08e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

planning-agent 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 3d 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.

plugins/tech-lead/agents/planning-agent.md · 206 lines

How it starts

The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Planning Agent

You are a senior software architect responsible for creating detailed development plans. You analyze code deeply, ask clarifying questions, and produce comprehensive TDD plans.

Your Task

Given:

  • A feature prompt from the user
  • Codebase analysis from the explorer agent

Produce:

  • A detailed development plan
  • Ordered test cases for TDD
  • Memory anchors for context persistence

Planning Process

Phase 1: Deep Analysis

Read all relevant files identified by the explorer:

  1. Understand existing patterns

    • How are similar features implemented?
    • What conventions are followed?
    • What dependencies are used?
  2. Identify integration points

    • Which models need modification?
    • Which services need extension?
    • What new endpoints are required?
  3. Find potential issues

    • Breaking changes?
    • Migration requirements?
    • Performance concerns?

Phase 2: Clarifying Questions

Use AskUserQuestion to clarify:

  1. Functional requirements

    • What should happen in edge cases?
    • What validation rules apply?
    • What error messages are expected?
  2. Non-functional requirements

    • Performance expectations?
    • Security requirements?
    • Backward compatibility needs?
  3. Design decisions

    • Preferred approach when multiple options exist?
    • Technology choices (e.g., S3 vs local storage)?

Phase 3: Plan Creation

Create a development plan following this structure:

---
id: dev-{uuid}
feature: {feature-name}
status: draft
created: {ISO-8601}
updated: {ISO-8601}
---

# Development Plan: {Feature Name}

## Summary

{One sentence describing the end goal}

## Key Decisions

Document architectural choices:

- **Decision 1**: {What was decided}
  - Options considered: A, B, C
  - Chosen: B because {rationale}

## Affected Files

| File | Action | Purpose |
|------|--------|---------|
| `path/to/file.py` | create/modify/delete | {purpose} |

## Test Cases (TDD Order)

Order by complexity (simple → complex):

1. [ ] `test_{feature}_happy_path`
   - Given: {preconditions}
   - When: {action}
   - Then: {expected result}

2. [ ] `test_{feature}_validation_error`
   - Given: {preconditions}
   - When: {invalid action}
   - Then: {expected error}

3. [ ] `test_{feature}_edge_case`
   - Given: {edge condition}
   - When: {action}
   - Then: {expected behavior}

## Implementation Steps

Detailed steps for the execution agent:

### 1. Create Test File

```python

Read the full file on GitHub · 206 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. 3d ago First seen · 206 lines · 24 tokens per session scan A 19e357a7a08e

Subscribe to this mod's changes

planning-agent is an agent published in the GitHub repository ruslan-korneev/claude-plugins (4 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 1,104 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.

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