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 jnPiyush/AgentX --skill cognitive-architecturegit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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/jnpiyush/agentx/cognitive-architecture)<a href="https://agentmods.dev/skills/jnpiyush/agentx/cognitive-architecture"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/cognitive-architecture/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/jnpiyush/agentx/cognitive-architecture"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/cognitive-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.01051 |
| Opus 5.5 | $0.00024 | $0.00420 |
| Sonnet 5.5 | $0.00012 | $0.00210 |
| Haiku 4.5 | $0.00006 | $0.00105 |
Grade A, and why
cognitive-architecture 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cognitive Architecture
Purpose: Patterns for the cognitive components of AI agents: Memory, Knowledge (RAG), and Reasoning.
When to Use This Skill
- Designing Memory Systems (Conversation history, User profiles, Entity tracking).
- Building RAG Pipelines (Chunking, Embedding, Retrieval, Reranking).
- Managing Agent State across sessions.
- Selecting Vector Databases for knowledge retrieval.
Decision Tree
Designing agent cognition?
+-- Need conversation history? -> Short-term memory (context window)
+-- Need factual knowledge? -> RAG pipeline (vector store + retrieval)
+-- Need user preferences across sessions? -> Long-term memory (database-backed)
+-- Need entity tracking? -> Episodic memory (structured state store)
+-- Need all of the above? -> Full cognitive architecture (all three layers)
+-- Unsure where to start? -> Start with RAG, add memory as needed
Table of Contents
- Cognitive Components
- Reference Patterns
- Troubleshooting
Cognitive Components
A complete agent "brain" consists of three layers:
- Context (Short-term Memory): The active context window (conversation history).
- Knowledge (Long-term Memory/RAG): Static facts retrieved from vector stores or databases.
- State (Episodic Memory): Structured data about the user or task progress persisted indefinitely.
Core Rules
- Layer separation - Keep context, knowledge, and state as independent modules with clear interfaces
- Context window budget - Allocate token budgets per cognitive layer and never exceed the model context limit
- Retrieval before generation - Always retrieve relevant knowledge before generating a response
- Grounded responses - Instruct the model to use only retrieved context and refuse when context is insufficient
- Memory lifecycle - Define TTL and eviction policies for each memory layer (short-term expires, long-term persists)
- Metadata on every chunk - Store source, timestamp, and relevance score with all knowledge chunks
- Test each layer independently - Evaluate retrieval quality, memory recall, and state consistency separately
What ships with it
3 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.
- 1mo ago Changed d93788e194f8
- 1mo ago First seen · 110 lines · 59 tokens per session scan A ae496d17af0c
cognitive-architecture is a skill published in the GitHub repository jnPiyush/AgentX (17 stars, last pushed 2d ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,051 once invoked, about $0.0002 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.
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