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/hainamchung/agent-assistant/plannergit clone --depth 1 https://github.com/hainamchung/agent-assistantWrote 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/hainamchung/agent-assistant/planner)<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>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 | $0.00012 | $0.01707 |
| Opus 5 | $0.00006 | $0.00853 |
| Sonnet 5 | $0.00002 | $0.00341 |
| Haiku 4.5 | $0.00001 | $0.00171 |
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 --> 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
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.
- 4d ago First seen · 212 lines · 12 tokens per session scan C fe5187d9523e
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.
Other agents, from other repositories
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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.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.