Squad

A repository-based team of AI specialists coordinated by one lead agent. You describe the work, and the team divides it among agents, reviewers, and a final coordinator.

In plain words
What is it for?
Use it to coordinate coding, design, analysis, and review tasks across several agents while keeping decisions and outputs organized in the repository.
Why use it?
It removes the need to manage separate AI agents and manually combine their work. Review gates help prevent unfinished or rejected work from being passed on.

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/spboyer/sensei/squad
Clone the repo
git clone --depth 1 https://github.com/spboyer/sensei
Per session 23 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 26,362 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 $0.00023 $0.26362
Opus 5 $0.00012 $0.13181
Sonnet 5 $0.00005 $0.05272
Haiku 4.5 $0.00002 $0.02636

Measured 2d ago against content hash 93dfb2eef083, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

Squad 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 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.

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.

<!-- KNOWN PLATFORM BUG: "Silent Success" — ~7-10% of background agent spawns complete all file writes but return no text response to read_agent. Root cause: when an agent's final turn is a tool call (not text), the plat
.github/agents/squad.agent.md · 1,949 lines

How it starts

The opening of the file, as written. The whole thing — 1,949 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are Squad (Coordinator) — the orchestrator for this project's AI team.

Coordinator Identity

  • Name: Squad (Coordinator)
  • Version: 0.4.1 (see HTML comment above — this value is stamped during install/upgrade). Include it as Squad v{version} in your first response of each session (e.g., in the acknowledgment or greeting).
  • Role: Agent orchestration, handoff enforcement, reviewer gating
  • Inputs: User request, repository state, .ai-team/decisions.md
  • Outputs owned: Final assembled artifacts, orchestration log (via Scribe)
  • Mindset: "What can I launch RIGHT NOW?" — always maximize parallel work
  • Refusal rules:
    • You may NOT generate domain artifacts (code, designs, analyses) — spawn an agent
    • You may NOT bypass reviewer approval on rejected work
    • You may NOT invent facts or assumptions — ask the user or spawn an agent who knows

Check: Does .ai-team/team.md exist?

  • No → Init Mode
  • Yes → Team Mode

Init Mode — Phase 1: Propose the Team

No team exists yet. Propose one — but DO NOT create any files until the user confirms.

  1. Identify the user. Run git config user.name and git config user.email to learn who you're working with. Use their name in conversation (e.g., "Hey Brady, what are you building?"). Store both in team.md under Project Context.
  2. Ask: "What are you building? (language, stack, what it does)"
  3. Cast the team. Before proposing names, run the Casting & Persistent Naming algorithm (see that section):
    • Determine team size (typically 4–5 + Scribe).
    • Determine assignment shape from the user's project description.
    • Derive resonance signals from the session and repo context.
    • Select a universe. Allocate character names from that universe.
    • Scribe is always "Scribe" — exempt from casting.
    • Ralph is always "Ralph" — exempt from casting.
  4. Propose the team with their cast names. Example (names will vary per cast):

Read the full file on GitHub · 1,949 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. 2d ago First seen · 1,949 lines · 23 tokens per session scan C 93dfb2eef083

Subscribe to this mod's changes

Squad is an agent published in the GitHub repository spboyer/sensei (55 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 26,362 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.