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.
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 skills add bradygaster/squad --skill tiered-memorygit 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/tiered-memory)<a href="https://agentmods.dev/skills/bradygaster/squad/tiered-memory"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/tiered-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bradygaster/squad/tiered-memory"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/tiered-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00020 | $0.01775 |
| Opus 5.5 | $0.00008 | $0.00710 |
| Sonnet 5.5 | $0.00004 | $0.00355 |
| Haiku 4.5 | $0.00002 | $0.00178 |
Grade A, and why
tiered-memory 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 1mo 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.
This is a copy
100% identical to tiered-memory — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Tiered Agent Memory
Status (v0.10.0): This skill describes a design proposal, not a shipped runtime. Skill files install via
squad init/upgrade, but the underlying tier scaffolding (.squad/memory/hot/,cold/,wiki/), Scribe promotion logic, and spawn-template tier-aware reads are tracked in bradygaster/squad#1264. Until those land, agents continue to load fullhistory.md+decisions.mdon every spawn.
Overview
Squad agents today load their full context history on every spawn, which grows unboundedly across sessions. The Tiered Agent Memory model proposes a three-tier separation so agents only load the bytes that are actually relevant to the current task, with older context kept available on demand.
Memory Tiers
🔥 Hot Tier — Current Session Context
- Size target: keep small (~2–4KB typical)
- Load policy: Always loaded. Every spawn includes hot memory by default.
- Contents: Current task description, active decisions made this session, immediate blockers, last 3–5 actions taken, who you are talking to right now.
- Lifetime: Current session only. Discarded after session ends (Scribe promotes relevant parts to Cold).
- Purpose: Provide immediate task context without any latency or load decision.
❄️ Cold Tier — Summarized Cross-Session History
- Size target: larger summary, not full transcript (~8–12KB typical)
- Load policy: Load on demand. Include only when the task explicitly needs history.
- Contents: Summarized past sessions (compressed by Scribe), cross-session decisions, recurring patterns, unresolved issues from prior work.
- Lifetime: Rolling window (default proposal: 30 days). Eligible entries are then promoted to Wiki.
- Purpose: Answer "what have we tried before?" and "what was decided?" without replaying full transcripts.
- How to include: Pass
--include-coldin spawn template or add## Cold Memorysection.
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.
- 1mo ago Changed · -1 lines e35e50f23e63
- 1mo ago First seen · 222 lines · 20 tokens per session scan A f9aa7ffa3685
tiered-memory is a skill published in the GitHub repository bradygaster/squad (3,295 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 1,775 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to tiered-memory, differing in 9 lines, and is treated as a copy.
Other skills, from other repositories
apex-context-management
UTILITY SKILL — Two-mode context-window management. RUNTIME: artifact compression (full/summarized/minimal) for orchestrator and codegen agents. AUDIT: post-mortem analysis of Copilot debug logs (token profiling, redundancy, hand-off gaps) for 11-Context Optimizer. WHEN: "context optimization", "token budget"…
squads-learn
Capture learnings after completing work. Use when finishing a task, fixing a bug, discovering a pattern, or learning something worth remembering for future sessions. Helps build institutional memory.
ce-compound
Document a recently solved problem or durable project vocabulary in docs/solutions/ or CONCEPTS.md. Use when capturing a learning after work.
loadout-remember
Save a durable, cross-project user preference into loadout's global fragments/profiles — or update the fragment it contradicts. Use when the user states lasting guidance that should follow them across projects and agents ("always X", "never Y", "stop doing Z"), especially when it conflicts with guidance the loadout…
end-of-session
Captures session learnings into persistent project memory before closing. Updates task files, project knowledge, and configuration so the next session starts with full context instead of from scratch.
resume
Restore context from a previous session and continue work.