bradygaster/squad is a tool that creates a human-directed team of AI development agents inside a project repository through GitHub Copilot. Developers use it to delegate work among persistent specialists such as frontend, backend, testing, and lead agents while retaining responsibility for decisions and review. The catalogue entries are the skills, agents, and instructions that define and coordinate those team members.
Borrowing it
Nothing to install: this file belongs to bradygaster/squad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bradygaster/squad/dev/.squad/skills/economy-mode/SKILL.mdgit clone --depth 1 https://github.com/bradygaster/squadWrote 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/skills/bradygaster/squad/economy-mode)<a href="https://agentmods.dev/skills/bradygaster/squad/economy-mode"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/economy-mode.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00022 | $0.01292 |
| Opus 5 | $0.00011 | $0.00646 |
| Sonnet 5 | $0.00004 | $0.00258 |
| Haiku 4.5 | $0.00002 | $0.00129 |
Grade A, and why
economy-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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SCOPE
✅ THIS SKILL PRODUCES:
- A modified Layer 3 model selection table applied when economy mode is active
economyMode: truewritten to.squad/config.jsonwhen activated persistently- Spawn acknowledgments with
💰indicator when economy mode is active
❌ THIS SKILL DOES NOT PRODUCE:
- Code, tests, or documentation
- Cost reports or billing artifacts
- Changes to Layer 0, Layer 1, or Layer 2 resolution (user intent always wins)
Context
Economy mode shifts Layer 3 (Task-Aware Auto-Selection) to lower-cost alternatives. It does NOT override persistent config (defaultModel, agentModelOverrides) or per-agent charter preferences — those represent explicit user intent and always take priority.
Use this skill when the user wants to reduce costs across an entire session or permanently, without manually specifying models for each agent.
Activation Methods
| Method | How |
|---|---|
| Session phrase | "use economy mode", "save costs", "go cheap", "reduce costs" |
| Persistent config | "economyMode": true in .squad/config.json |
| CLI flag | squad --economy |
Deactivation: "turn off economy mode", "disable economy mode", or remove economyMode from config.json.
Economy Model Selection Table
When economy mode is active, Layer 3 auto-selection uses this table instead of the normal defaults:
| Task Output | Normal Mode | Economy Mode |
|---|---|---|
| Writing code (implementation, refactoring, bug fixes) | gpt-5.6-terra |
gpt-5.6-luna |
| Writing prompts or agent designs | gpt-5.6-terra |
gpt-5.6-luna |
| Docs, planning, triage, changelogs, mechanical ops | gpt-5.6-luna |
gpt-5.6-luna |
| Visual/design work requiring image analysis | gpt-5.6-sol |
gpt-5.6-terra |
| Architecture, code review, security audits | gpt-5.6-sol |
gpt-5.6-terra |
| Scribe / logger / mechanical file ops | gpt-5.6-luna |
gpt-5.6-luna |
Prefer gpt-5.6-luna for all economy-mode tasks where cost is the priority.
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.
- 8d ago First seen · 116 lines · 22 tokens per session scan A 5b7aee3204c9
economy-mode is a skill published in the GitHub repository bradygaster/squad (3,163 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 1,292 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.
ralphctl-test-driven-development
Execute-phase skill — write the failing test before the code that makes it pass; for bug fixes, this is the reproduction test itself. Use for any logic change, bug fix, or behavioural modification; for the full root-cause triage pipeline around an unexpected failure, see ralphctl-debugging-and-error-recovery.
ralphctl-code-review-and-quality
Multi-phase code-quality skill — primary frame for the evaluator role in Execute, the architecture axis in Plan, and correctness/readability in Refine. Multi-axis code review with severity vocabulary. Use when you are the evaluator assessing a generator's output, and when reviewing any change before signalling…
dos-goal-fleet
Launch multiple goal-scoped workers in waves, each with a witness-gated stop condition and dos arbitrate file-tree safety. Use when an operator asks to run several independent goals in parallel and fold only verified ships.
dos-dispatch-loop
Run recurring dos-dispatch cycles, switching to dos-replan when the backlog drains and stopping on the kernel's loop verdict. Use for unattended dispatch->replan->dispatch work across disjoint lanes.
dos-goal-gate
Ground a keep-working goal in evidence the worker did not author by wiring dos hook stop to refuse false done claims. Use for one self-stopping agent or loop worker; use dos-witness-claim for fold barriers.