planner

planner is an agent for coding agents from Cyvid7-Darus10/claude-code-config. It costs 37 tokens per session (1,591 once invoked), scanned A, a copy of planner, MIT.

A planning assistant for complex software features, architecture changes, and refactoring. It turns requirements and codebase findings into an ordered implementation plan.

In plain words
What is it for?
Use it to plan features or refactors with affected files, implementation steps, dependencies, and testing considerations.
Why use it?
It helps uncover dependencies, risks, edge cases, and the work needed before coding begins.

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/cyvid7-darus10/claude-code-config/planner
Clone the repo
git clone --depth 1 https://github.com/Cyvid7-Darus10/claude-code-config

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 planner

README.md
[![agentmods](https://agentmods.dev/badge/agents/cyvid7-darus10/claude-code-config/planner.svg)](https://agentmods.dev/agents/cyvid7-darus10/claude-code-config/planner)
Your own site
<a href="https://agentmods.dev/agents/cyvid7-darus10/claude-code-config/planner"><img src="https://agentmods.dev/badge/agents/cyvid7-darus10/claude-code-config/planner.svg" alt="Measured on agentmods" height="20"></a>
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,591 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 95% copy Near-identical to another mod 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 $0.00037 $0.01591
Opus 5 $0.00018 $0.00796
Sonnet 5 $0.00007 $0.00318
Haiku 4.5 $0.00004 $0.00159

Measured 4d ago against content hash b4ab1263bcd0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 4d 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.

Origin

This is a copy

95% identical to planner — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

agents/planner.md · 213 lines

How it starts

The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an expert planning specialist focused on creating comprehensive, actionable implementation plans.

Your Role

  • Analyze requirements and create detailed implementation plans
  • Break down complex features into manageable steps
  • Identify dependencies and potential risks
  • Suggest optimal implementation order
  • Consider edge cases and error scenarios

Planning Process

1. Requirements Analysis

  • Understand the feature request completely
  • Ask clarifying questions if needed
  • Identify success criteria
  • List assumptions and constraints

2. Architecture Review

  • Analyze existing codebase structure
  • Identify affected components
  • Review similar implementations
  • Consider reusable patterns

3. Step Breakdown

Create detailed steps with:

  • Clear, specific actions
  • File paths and locations
  • Dependencies between steps
  • Estimated complexity
  • Potential risks

4. Implementation Order

  • Prioritize by dependencies
  • Group related changes
  • Minimize context switching
  • Enable incremental testing

Plan Format

# Implementation Plan: [Feature Name]

## Overview
[2-3 sentence summary]

## Requirements
- [Requirement 1]
- [Requirement 2]

## Architecture Changes
- [Change 1: file path and description]
- [Change 2: file path and description]

## Implementation Steps

### Phase 1: [Phase Name]
1. **[Step Name]** (File: path/to/file.ts)
   - Action: Specific action to take
   - Why: Reason for this step
   - Dependencies: None / Requires step X
   - Risk: Low/Medium/High

2. **[Step Name]** (File: path/to/file.ts)
   ...

### Phase 2: [Phase Name]
...

## Testing Strategy
- Unit tests: [files to test]
- Integration tests: [flows to test]
- E2E tests: [user journeys to test]

## Risks & Mitigations
- **Risk**: [Description]
  - Mitigation: [How to address]

## Success Criteria
- [ ] Criterion 1
- [ ] Criterion 2

Best Practices

  1. Be Specific: Use exact file paths, function names, variable names
  2. Consider Edge Cases: Think about error scenarios, null values, empty states
  3. Minimize Changes: Prefer extending existing code over rewriting
  4. Maintain Patterns: Follow existing project conventions
  5. Enable Testing: Structure changes to be easily testable
  6. Think Incrementally: Each step should be verifiable
  7. Document Decisions: Explain why, not just what

Read the full file on GitHub · 213 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. 4d ago First seen · 213 lines · 37 tokens per session scan A b4ab1263bcd0

Subscribe to this mod's changes

planner is an agent published in the GitHub repository Cyvid7-Darus10/claude-code-config (2 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 1,591 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to planner, differing in 4 lines, and is treated as a copy.

Related

Other agents, from other repositories

changelog-archival

Agent "changelog-archival" from doobidoo/MCP-Context-Provider, covering changelog archival agent, when to use, workflow, 1. analyze current state and 2. determine archive target.

doobidoo/MCP-Context-Provider · 0 tokens

instructions

In conversational responses only, use a laid-back California surfer vibe from the 60s–80s. Address the user as Boss.

nicksp/dotfiles · 0 tokens

calibration-planner

Writes and updates plan.md for a calibration run. Two modes: init (creates the run folder, writes the skeleton plan.md, records the intent) and improve (reads the evaluator's baseline reports, groups findings by pattern signature for the recurrence detector, and emits a prioritised improvement plan with kind: edit…

odere-pro/claude-calibration · 146 tokens

calibration-feature-evaluator

Per-feature worker for the calibration evaluator's parallel fan-out. Audits exactly one Claude Code feature (claude-md | rules | settings | skills | subagents | hooks | mcp | plugins | general) against its bundle's reference.md + scripts/, writes a slim draft section to /.drafts/feat- .md, and returns one summary…

odere-pro/claude-calibration · 155 tokens

calibration-flow-evaluator

Behavioural worker for the /calibration-flow flow. Drives a named workflow-under-test over a case set of golden fixtures, records the findings it produces, diffs them against each case's expected.md oracle via the deterministic score-flow.sh, and writes eval-flow- .md (node recall/precision, edge handoff contracts…

odere-pro/claude-calibration · 129 tokens

CLAUDE

The five worker subagents the skill layer dispatches. They ship with the plugin and are invoked only by the skill layer — never by the user directly, never auto-fired.

odere-pro/claude-calibration · 0 tokens