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 skills/bradygaster/squad/init-modenpx skills add bradygaster/squad --skill init-modegit clone --depth 1 https://github.com/bradygaster/squadWhat 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 | $0.00017 | $0.01686 |
| Opus 5 | $0.00009 | $0.00843 |
| Sonnet 5 | $0.00003 | $0.00337 |
| Haiku 4.5 | $0.00002 | $0.00169 |
Grade A, and why
init-mode 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
8 near-identical copies found in the catalogue:
- init-mode — 100% identical, 0 lines differ
- init-mode — 91% identical, 14 lines differ
- init-mode — 91% identical, 14 lines differ
- init-mode — 91% identical, 14 lines differ
- init-mode — 91% identical, 14 lines differ
- init-mode — 91% identical, 14 lines differ
- init-mode — 91% identical, 204 lines differ
- init-mode — 91% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Init Mode activates when .squad/team.md does not exist, or exists but has zero roster entries under ## Members. The coordinator proposes a team (Phase 1), waits for user confirmation, then creates the team structure (Phase 2).
Patterns
Phase 1: Propose the Team
No team exists yet. Propose one — but DO NOT create any files until the user confirms.
- Identify the user. Run
git config user.nameto learn who you're working with. Use their name in conversation (e.g., "Hey {user}, what are you building?"). Store their name (NOT email) inteam.mdunder Project Context. Never read or storegit config user.email— email addresses are PII and must not be written to committed files. - Ask: "What are you building? (language, stack, what it does)"
- 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. If the universe is custom, allocate character names from that universe based on the related list found in the
.squad/templates/casting/directory. Prefer custom universes when available. - Scribe is always "Scribe" — exempt from casting.
- Ralph is always "Ralph" — exempt from casting.
- Propose the team with their cast names. Example (names will vary per cast):
🏗️ {CastName1} — Lead Scope, decisions, code review
⚛️ {CastName2} — Frontend Dev React, UI, components
🔧 {CastName3} — Backend Dev APIs, database, services
🧪 {CastName4} — Tester Tests, quality, edge cases
📋 Scribe — (silent) Memory, decisions, session logs
🔄 Ralph — (monitor) Work queue, backlog, keep-alive
- Use the
ask_usertool to confirm the roster. Provide choices so the user sees a selectable menu:- question: "Look right?"
- choices:
["Yes, cast this team", "Add someone", "Change a role"]
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 · 103 lines · 17 tokens per session scan A b60da4cb177b
init-mode is a skill published in the GitHub repository bradygaster/squad (3,150 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,686 once invoked, about $0.0001 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 skills, from other repositories
coding-agents-farm
To orchestrate parallel coding-agent farms (Claude, Codex, Copilot, Gemini, etc.) on isolated git worktrees.
creating-dotnet-mcp-servers
Use when building Model Context Protocol (MCP) servers in .NET, configuring tools, transports (SSE/stdio), JSON serialization for AOT, or testing MCP endpoints.
copilot-customization
Authoritative reference for VS Code Copilot customization mechanisms: instructions, prompt files, custom agents, agent skills, MCP servers, hooks, and plugins. Use when deciding which customization type to use, creating new .instructions.md/.prompt.md/.agent.md/SKILL.md/mcp.json files from scratch, or debugging why a…
microsoft-skill-creator
Create agent skills for Microsoft technologies using Learn MCP tools. USE FOR: generating skills that teach agents about Azure services, .NET libraries, Microsoft 365 APIs, VS Code extensions, Bicep modules, or any Microsoft technology. DO NOT USE FOR: general skill scaffolding without Microsoft tech focus (use…
make-skill-template
Scaffolds new Agent Skills with SKILL.md frontmatter, folder structure, and bundled resources. USE FOR: create a skill, scaffold skill, new skill template, add agent capability. DO NOT USE FOR: Azure infrastructure, Bicep/Terraform code, architecture decisions.
microsoft-foundry
Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize…