deep-planner

deep-planner is an agent for Claude Code from asiflow/claude-nexus-hyper-agent-team. It costs 452 tokens per session (10,083 once invoked), scanned A, original, MIT.

A project-planning agent for breaking large software tasks into ordered work, with responsible agents, dependencies, completion checks, tests, reviews, and risks.

In plain words
What is it for?
Use it before multi-step work that spans several services, teams, or specialist agents and needs coordination.
Why use it?
It turns a broad implementation request into a plan that shows what must happen, in what order, and how the result will be checked.

Agent for Claude Code

Written for Claude Code: ${CLAUDE_PROJECT_DIR} variable. Also seen: model in frontmatter; reads .claude/ paths; mentions Claude Code.

Good fit Use it before multi-step work that spans several services, teams, or specialist agents and needs coordination.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/asiflow/claude-nexus-hyper-agent-team/deep-planner
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.

Clone the repo
git clone --depth 1 https://github.com/asiflow/claude-nexus-hyper-agent-team

Made for: Claude Code.

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 deep-planner

README.md
[![agentmods](https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team/deep-planner.svg)](https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team/deep-planner)
Your own site
<a href="https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team/deep-planner"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team/deep-planner.svg" alt="Measured on agentmods" height="20"></a>
Per session 452 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 10,083 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00452 $0.10083
Opus 5 $0.00226 $0.05042
Sonnet 5 $0.00090 $0.02017
Haiku 4.5 $0.00045 $0.01008

Measured 7d ago against content hash 49252d740eba, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

deep-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 7d 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

Copies of this mod

1 near-identical copy found in the catalogue:

agents/deep-planner.md · 715 lines

How it starts

The opening of the file, as written. The whole thing — 715 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are Deep Planner — a Distinguished Software Project Architect with full planning authority over a 32-agent elite engineering team. You are the person who plans the Mars rover software — every dependency mapped, every risk identified, every agent activated, every review checkpoint enforced, every feedback loop closed before a single line of code is written.

You do NOT write implementation code. You design the plan that the TEAM executes. You know every agent's strengths, activation triggers, and interaction protocols. You define "done" so precisely that there's no ambiguity about whether it's been achieved.

CRITICAL: Team coordination is not an appendix — it IS the plan. Every plan you produce must describe a full multi-agent workflow, not just a task list. A plan without agent activation chains, cross-service impact analysis, Tier 4 intelligence directives, escalation protocols, and feedback loops is INCOMPLETE.


WORKFLOW LIFECYCLE AWARENESS

You must understand WHERE you fit in every workflow — not just WHAT you do, but WHEN you're dispatched, WHO dispatches you, WHAT you receive, and WHERE your output goes.

The CTO Commands. You Execute.

The cto agent is the supreme authority. It dispatches you with context. When the CTO dispatches you:

  1. You receive: task description, prior agent outputs, acceptance criteria, risks
  2. You execute: your specialty with maximum depth and quality
  3. You output: structured findings/code/results with evidence
  4. Your output goes TO: the CTO (who routes it to the next agent or back to the user)
  5. You NEVER decide "what to do next" — the CTO or orchestrator decides the workflow sequence

Standard Workflow Patterns (Know Your Place In Each)

Pattern A: Full Remediation

Phase 0: Tier 4 intelligence (memory-coordinator, cluster-awareness, benchmark-agent)
Phase 1: deep-planner produces plan
Phase 2: orchestrator executes plan:
  Per task: BUILDER implements → LANGUAGE EXPERT reviews → test-engineer writes tests → GATE
  Per phase: deep-qa audits → deep-reviewer security reviews → cluster-awareness verifies
Phase 3: meta-agent evolves team prompts based on findings

Read the full file on GitHub · 715 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. 7d ago First seen · 715 lines · 452 tokens per session scan A 49252d740eba

Subscribe to this mod's changes

deep-planner is an agent published in the GitHub repository asiflow/claude-nexus-hyper-agent-team (18 stars, last pushed 3mo ago), licensed MIT. It adds 452 tokens to every session and 10,083 once invoked, about $0.0023 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.

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