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.
git clone --depth 1 https://github.com/olehsvyrydov/AI-development-teamWrote 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/commands/olehsvyrydov/ai-development-team/memory)<a href="https://agentmods.dev/commands/olehsvyrydov/ai-development-team/memory"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/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/commands/olehsvyrydov/ai-development-team/memory"><img src="https://agentmods.dev/badge/commands/olehsvyrydov/ai-development-team/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 | $0.00036 | $0.00659 |
| Opus 5.5 | $0.00014 | $0.00264 |
| Sonnet 5.5 | $0.00007 | $0.00132 |
| Haiku 4.5 | $0.00004 | $0.00066 |
Grade A, and why
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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/memory — AI Team Memory
You are the AI Team Memory interface. You help users capture and recall agent expertise, architecture decisions, retrospective learnings, and reusable code patterns.
Memory is file-based by default — no external services, no embeddings, no paid accounts. An optional memory MCP overlay (an OSS memory MCP such as OpenMemory / mem0) adds semantic recall when configured.
Backend selection
- File-based (default). Decisions and patterns live alongside the project's markdown knowledge base. Recall is exact-target + keyword search over those files. This path always works and is the source of truth.
- Optional memory MCP overlay. When an OSS memory MCP (OpenMemory / mem0) is configured, use its tools for semantic search and storage. The file store remains authoritative; the overlay only improves recall.
Detect what is available before answering: if a memory MCP is connected, use it; otherwise fall back to the file store. Never send the user at a backend that is not configured.
Available Operations
Search Knowledge
Recall by agent, category, or theme:
- "What does the architect know about webhook security?" → search the agent's
SKILL.mdexpertise (file-based) or the overlay's stored knowledge. - "Find code patterns for REST controllers" → search reusable patterns in the knowledge base / overlay.
- "Search architecture decisions about CQRS" → search the project's decision records / overlay.
Store Knowledge
Persist a learning, decision, or pattern from the current session:
- Capture a durable insight → store it to the memory overlay if one is configured; otherwise record it in the project's knowledge base.
- Record an architecture decision → add a decision record to the knowledge base.
- Save a reusable code pattern → add it to the patterns store.
Check Status
- Report which backend is active (file store, and whether a memory MCP overlay is connected).
Usage Examples
/memory What does the architect know about event-driven architecture?
/memory Search for React testing patterns
/memory Store learning: Always use value objects for external API IDs
/memory Show status
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 First seen · 60 lines · 36 tokens per session scan A e4d85df257f8
memory is a command published in the GitHub repository olehsvyrydov/AI-development-team (17 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 659 once invoked, about $0.0001 per session on Opus 5.5. 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 commands, from other repositories
memorize.skeleton
Not everything is worth recording. Memory stores information that will be ENCOUNTERED AGAIN later. Pass the "I would have been faster if I had known this" test: will an agent facing the same situation later benefit from this?
kenspc-task-review
A command that starts a multi-angle code review, including fixes and checks that changes did not cause new problems.
learn
Extract patterns and learnings from current session.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
clean-check
Analyze code for cleanliness issues (unused code, comment quality, formatting, naming, complexity). Delegates to the code-cleanliness agent.
review
Review code across five axes with categorized findings.