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/.github/skills/reflect/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/reflect)<a href="https://agentmods.dev/skills/bradygaster/squad/reflect"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/reflect/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/reflect"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/reflect.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 | $0.00066 | $0.01729 |
| Opus 5.5 | $0.00026 | $0.00692 |
| Sonnet 5.5 | $0.00013 | $0.00346 |
| Haiku 4.5 | $0.00007 | $0.00173 |
Grade A, and why
reflect 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 22d 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 reflect — 51 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect Skill
Critical learning capture system for Squad. Prevents repeating mistakes and preserves successful patterns across sessions.
Analyze conversations and propose improvements to squad knowledge based on what worked, what didn't, and edge cases discovered. Every correction is a learning opportunity.
Integration with Squad Architecture
Reflect complements existing Squad knowledge systems:
- Governed runtime memory — Durable observations and decisions persisted through configured state tools
- Tracked governing source — Coordinator prompts, skills, and other behavior-defining files changed through focused pull requests
reflectskill — Identifies learnings that may warrant either runtime memory or an approved tracked behavior change
Workflow:
- Use
reflectduring work to capture learnings - At session end, review captured learnings
- Persist evidence through governed runtime memory when available
- Promote accepted behavior changes through the accountable specialist as tracked source changes in a focused PR
- Report adjacent improvements separately; do not expand the current scope without explicit user approval
Triggers
🔴 HIGH Priority (Invoke Immediately)
| Trigger | Example | Why Critical |
|---|---|---|
| User correction | "no", "wrong", "not like that", "never do" | Captures mistakes to prevent repetition |
| Architectural insight | "you removed that without understanding why" | Documents design decisions (Chesterton's Fence) |
| Immediate fixes | "debug", "root cause", "fix all" | Learns from errors in real-time |
🟡 MEDIUM Priority (Invoke After Multiple)
| Trigger | Example | Why Important |
|---|---|---|
| User praise | "perfect", "exactly", "great" | Reinforces successful patterns |
| Tool preferences | "use X instead of Y", "prefer" | Builds workflow preferences |
| Edge cases | "what if X happens?", "don't forget", "ensure" | Captures scenarios to handle |
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.
- 22d ago Changed · +3 lines 56a68807c843
- 1mo ago First seen · 230 lines · 66 tokens per session scan A a844dcf82122
reflect is a skill published in the GitHub repository bradygaster/squad (3,300 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 1,729 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to reflect, differing in 51 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.