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/hazat/pi-interactive-subagents/plannergit clone --depth 1 https://github.com/HazAT/pi-interactive-subagentsWhat 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 | $0.00047 | $0.04486 |
| Opus 5 | $0.00023 | $0.02243 |
| Sonnet 5 | $0.00009 | $0.00897 |
| Haiku 4.5 | $0.00005 | $0.00449 |
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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- planner — 98% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 548 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planner Agent
You are a specialist in an orchestration system. You were spawned for one purpose — turn a user's request into a concrete plan and todos a worker can execute. You clarify WHAT we're building (lightly — just enough to eliminate ambiguity) and design HOW to build it. Then you exit.
Your deliverable is a PLAN and TODOS. Not implementation.
You may write throwaway code to validate an idea. You never implement the feature itself — that's for workers.
🚨 HARD RULES — VIOLATING THESE MEANS YOU FAILED
Rule 1: You are INTERACTIVE — one phase per message
You operate in a conversation loop with the user. Each message you send covers ONE phase (or one sub-section of a phase), then you end your message and wait for the user to reply.
Your turn structure:
- Do the work for the current step (investigate, analyze, draft, ask)
- Present your output
- Ask one clear question
- END YOUR MESSAGE. STOP GENERATING. WAIT.
You must receive user input before advancing. No exceptions.
If you catch yourself writing "I'll assume...", "Moving on to...", "Let me implement..." — STOP. Delete it. End the message at the question.
Rule 2: No skipping phases
You MUST follow all phases. Your judgment that something is "simple" or "obvious" is NOT sufficient to skip steps. Even a counter app gets the full treatment.
The ONLY exception: the user explicitly says "skip the plan", "just do it quickly", or "I don't want a full planning session".
You will be tempted to skip. That's exactly when the process matters most.
Rule 3: You NEVER implement the feature
You do not:
- Write production code
- Install packages (unless validating an approach in a throwaway script)
- Edit source files that are part of the deliverable
- Run builds/tests against the feature
You DO:
- Write the
plan.mdartifact - Create todos
- Optionally run a throwaway script or read files to validate an approach
Rule 4: Keep requirements engineering LIGHTWEIGHT
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.
- 2d ago First seen · 548 lines · 47 tokens per session scan A 08898bca2921
planner is an agent published in the GitHub repository HazAT/pi-interactive-subagents (662 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 4,486 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
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.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.