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 akhilyad/deployy --skill memory-managementgit clone --depth 1 https://github.com/akhilyad/deployyWrote 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/akhilyad/deployy/memory-management)<a href="https://agentmods.dev/skills/akhilyad/deployy/memory-management"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/memory-management/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/akhilyad/deployy/memory-management"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/memory-management.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 | $0.00095 | $0.00775 |
| Opus 5.5 | $0.00038 | $0.00310 |
| Sonnet 5.5 | $0.00019 | $0.00155 |
| Haiku 4.5 | $0.00010 | $0.00077 |
Grade A, and why
memory-management 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 20d 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 memory-management — 24 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Management Skill
Purpose
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management.
When to Trigger
- need to store successful patterns
- searching for similar solutions
- semantic lookup of past work
- learning from previous tasks
- sharing knowledge between agents
- building knowledge base
When to Skip
- no learning needed
- ephemeral one-off tasks
- external data sources available
- read-only exploration
Commands
Store Pattern
Store a pattern or knowledge item in memory
npx @hyrex/cli memory store --key "[key]" --value "[value]" --namespace patterns
Example:
npx @hyrex/cli memory store --key "auth-jwt-pattern" --value "JWT validation with refresh tokens" --namespace patterns
Semantic Search
Search memory using semantic similarity
npx @hyrex/cli memory search --query "[search terms]" --limit 10
Example:
npx @hyrex/cli memory search --query "authentication best practices" --limit 5
Retrieve Entry
Retrieve a specific memory entry by key
npx @hyrex/cli memory get --key "[key]" --namespace [namespace]
Example:
npx @hyrex/cli memory get --key "auth-jwt-pattern" --namespace patterns
List Entries
List all entries in a namespace
npx @hyrex/cli memory list --namespace [namespace]
Example:
npx @hyrex/cli memory list --namespace patterns --limit 20
Delete Entry
Delete a memory entry
npx @hyrex/cli memory delete --key "[key]" --namespace [namespace]
Initialize HNSW Index
Initialize HNSW vector search index
npx @hyrex/cli memory init --enable-hnsw
Memory Stats
Show memory usage statistics
npx @hyrex/cli memory stats
Export Memory
Export memory to JSON
npx @hyrex/cli memory export --output memory-backup.json
Scripts
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 20d ago First seen · 127 lines · 95 tokens per session scan A c1dfe8cd233c
memory-management is a skill published in the GitHub repository akhilyad/deployy (0 stars, last pushed 5mo ago), licensed MIT. It adds 95 tokens to every session and 775 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to memory-management, differing in 24 lines, and is treated as a copy.
Other skills, from other repositories
AgentDB Memory Patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
pinecone-research
Agent RAG and long-term memory with Pinecone.
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG…
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
llm-wiki
Explain and apply the foundational LLM Wiki architecture and knowledge-management strategy. Use for setup, architecture, schema, or organization decisions; operational ingest/query/lint work uses dedicated skills.