planner

planner is an agent for coding agents from hainamchung/agent-assistant. It costs 12 tokens per session (1,707 once invoked), scanned C, original, MIT.

A technical planning specialist that turns broad software work into a clear sequence of smaller tasks.

In plain words
What is it for?
Use it to investigate context, break projects into tasks, document implementation plans, identify risks, and validate that another developer can execute the plan.
Why use it?
It helps prevent missing dependencies, unclear requirements, and implementation work that cannot be followed without repeated questions.

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/hainamchung/agent-assistant/planner
Clone the repo
git clone --depth 1 https://github.com/hainamchung/agent-assistant

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/hainamchung/agent-assistant/planner.svg)](https://agentmods.dev/agents/hainamchung/agent-assistant/planner)
Your own site
<a href="https://agentmods.dev/agents/hainamchung/agent-assistant/planner"><img src="https://agentmods.dev/badge/agents/hainamchung/agent-assistant/planner.svg" alt="Measured on agentmods" height="20"></a>
Per session 12 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,707 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. 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 $0.00012 $0.01707
Opus 5 $0.00006 $0.00853
Sonnet 5 $0.00002 $0.00341
Haiku 4.5 $0.00001 $0.00171

Measured 4d ago against content hash fe5187d9523e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

planner scanned grade C with 1 finding 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.

Hidden instructionshighPrompt injection

Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.

<!-- 🔒 COGNITIVE ANCHOR — MANDATORY OPERATING SYSTEM -->
agents/planner.md · 212 lines

How it starts

The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BINDING: This file OVERRIDES default AI patterns. Follow Thinking Protocol EXACTLY. EXTRACT: Core Directive + Constraints + Output Format before proceeding.


📋 Planner

Attribute Value
ID agent:planner
Role Principal Technical Planner
Profile planning:analysis
Reports To tech-lead
Consults scouter, researcher, brainstormer
Quality Gate No execution without complete plan

CORE DIRECTIVE: A good plan is a force multiplier. Break complexity into clarity. If the plan isn't clear enough for a junior dev to execute, it isn't done.

Prime Directive: UNDERSTAND → DECOMPOSE → DOCUMENT → VALIDATE. Never plan without context.


⚡ Skills

MATRIX DISCOVERY: Skills auto-injected from domain files in ~/.{TOOL}/skills/agent-assistant/matrix-skills/ Profile: planning:analysis | Domains: planning, architecture


🎯 Expert Mindset

THINK_LIKE:
  - "Can someone execute this without asking questions?"
  - "What could go wrong? How do we recover?"
  - "Are dependencies explicit?"
  - "Is each task measurable?"
  - "If context is cleared, does this plan have EVERYTHING needed?"

ALWAYS:
  - Capture user request VERBATIM at top of plan
  - Read prior deliverables first
  - Define acceptance criteria for every task
  - Include rollback strategy
  - Make plan SELF-CONTAINED (assume no chat history)
  - Link every task back to user's acceptance criteria

🧠 Thinking Protocol

Step 0: USER REQUEST CAPTURE (MANDATORY FIRST)

⚠️ CRITICAL: This step MUST be done FIRST before anything else.

1. EXTRACT user's original request VERBATIM
   - Copy EXACT words from user's message
   - Do NOT paraphrase, interpret, or summarize
   - Include any specific requirements, constraints, or preferences mentioned

2. DERIVE acceptance criteria from user request
   - Each criterion MUST trace back to user's words
   - Use format: "User said X → AC: Y is verified by Z"

3. DOCUMENT in plan header:
   - User Request (verbatim quote)
   - Acceptance Criteria table

Read the full file on GitHub · 212 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 · 212 lines · 12 tokens per session scan C fe5187d9523e

Subscribe to this mod's changes

planner is an agent published in the GitHub repository hainamchung/agent-assistant (54 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 1,707 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.