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 pinkpixel-dev/skills-collection-1 --skill agent-memory-systemsgit clone --depth 1 https://github.com/pinkpixel-dev/skills-collection-1Wrote 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/pinkpixel-dev/skills-collection-1/agent-memory-systems)<a href="https://agentmods.dev/skills/pinkpixel-dev/skills-collection-1/agent-memory-systems"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/agent-memory-systems/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/pinkpixel-dev/skills-collection-1/agent-memory-systems"><img src="https://agentmods.dev/badge/skills/pinkpixel-dev/skills-collection-1/agent-memory-systems.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 | $0.00048 | $0.00470 |
| Opus 5.5 | $0.00019 | $0.00188 |
| Sonnet 5.5 | $0.00010 | $0.00094 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
agent-memory-systems 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
83% identical to agent-memory-systems — 7 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.
What it actually says
Agent Memory Systems
You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.
Your core insight: Memory failures look like intelligence failures. When an agent "forgets" or gives inconsistent answers, it's almost always a retrieval problem, not a storage problem. You obsess over chunking strategies, embedding quality, and
Capabilities
- agent-memory
- long-term-memory
- short-term-memory
- working-memory
- episodic-memory
- semantic-memory
- procedural-memory
- memory-retrieval
- memory-formation
- memory-decay
Patterns
Memory Type Architecture
Choosing the right memory type for different information
Vector Store Selection Pattern
Choosing the right vector database for your use case
Chunking Strategy Pattern
Breaking documents into retrievable chunks
Anti-Patterns
❌ Store Everything Forever
❌ Chunk Without Testing Retrieval
❌ Single Memory Type for All Data
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | ## Contextual Chunking (Anthropic's approach) |
| Issue | high | ## Test different sizes |
| Issue | high | ## Always filter by metadata first |
| Issue | high | ## Add temporal scoring |
| Issue | medium | ## Detect conflicts on storage |
| Issue | medium | ## Budget tokens for different memory types |
| Issue | medium | ## Track embedding model in metadata |
Related Skills
Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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 · 73 lines · 48 tokens per session scan A 80e40d0d528c
agent-memory-systems is a skill published in the GitHub repository pinkpixel-dev/skills-collection-1 (9 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 470 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to agent-memory-systems, differing in 7 lines, and is treated as a copy.
Other skills, from other repositories
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memory-retain
Save what was learned to Hindsight long-term memory. Use when a task finishes, when the user states a fact, decision or preference, and when you discover something a later run or another Bot would need.
last30Days
Resolve "last30Days" to a concrete ISO date range relative to your run time — a rolling 30-day window ending today. Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a "last 30 days" / trailing-month task…
thisWeek
Resolve "thisWeek" to a concrete ISO date range relative to your run time — this week so far (Monday → today). Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a week-to-date task (this week's activity…
kb-research
Structured approach to finding and synthesizing information from the user's knowledge base.
md-fetch-summarize
Fetch a URL and return a concise markdown summary of its content. Read-only: no files are written; the summary is returned as output only. Use when asked to "fetch and summarize", "summarize this URL", "what does this page say", or "get the content of ". Proactively suggest when the user pastes a URL and asks what it…