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/ruslan-korneev/claude-plugins/planning-agentgit clone --depth 1 https://github.com/ruslan-korneev/claude-pluginsWrote 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/ruslan-korneev/claude-plugins/planning-agent)<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>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 | $0.00024 | $0.01104 |
| Opus 5 | $0.00012 | $0.00552 |
| Sonnet 5 | $0.00005 | $0.00221 |
| Haiku 4.5 | $0.00002 | $0.00110 |
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.
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:
-
Understand existing patterns
- How are similar features implemented?
- What conventions are followed?
- What dependencies are used?
-
Identify integration points
- Which models need modification?
- Which services need extension?
- What new endpoints are required?
-
Find potential issues
- Breaking changes?
- Migration requirements?
- Performance concerns?
Phase 2: Clarifying Questions
Use AskUserQuestion to clarify:
-
Functional requirements
- What should happen in edge cases?
- What validation rules apply?
- What error messages are expected?
-
Non-functional requirements
- Performance expectations?
- Security requirements?
- Backward compatibility needs?
-
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
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.
- 3d ago First seen · 206 lines · 24 tokens per session scan A 19e357a7a08e
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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