How does this skill help with context switching between different projects?

This skill eliminates 'context amnesia' by automatically scanning your local session logs for relevant past decisions and definitions, injecting them into your current prompt so you don't have to manually re-explain your project constraints.

Which AI agent frameworks are compatible with this memory extension?

The extension is compatible with any agent framework that supports TypeScript execution and maintains local JSONL session logs, including most popular open-source autonomous agent stacks.

Does the memory retrieval process pose a security risk for my private data?

It includes a privacy redaction layer that automatically identifies and masks common sensitive formats (like API keys or secret tokens) within your history before the data is synthesized by a subagent.

Can I control how much data the skill processes to avoid high latency or token costs?

Yes, you can configure resource bounding to set strict limits on file count, byte size, and search timeouts to ensure the memory retrieval process doesn't slow down your agent's response time.

What exactly is included in the purchase of this skill?

You receive the complete `extensions/agent-memory-access.ts` source code, configuration documentation, and lifetime access to version updates as the memory synthesis logic is refined.

Will the agent get confused if my past instructions contradict my current prompt?

The skill is designed to treat historical data as 'read-only' reference material; it explicitly ignores old instructions found in logs to prevent 'ghost' commands from interfering with your current task.

Agent Memory Access

Example session with this skill installed

I'm ready to continue work on the authentication module. What were the specific edge cases we decided to handle for the JWT refresh flow last Tuesday?

Read your context and instructions
Compiled the agent memory access

I have retrieved the relevant notes from your session on Tuesday regarding the JWT refresh implementation.

Historical Context: JWT Refresh Decisions

Category	Decision / Edge Case
Token Reuse	Implement a 30-second grace period for old refresh tokens to handle concurrent network requests.
Revocation	Immediate invalidation of the entire refresh chain if a reused token is detected outside the grace window.
Expiry	Set sliding window expiration: 7 days max age, 24 hours idle timeout.
Security	Store `jti` in a Redis-backed allow-list rather than a block-list for faster validation.
Client Handling	Return a specific `401.13` sub-status code to trigger a full re-login on the frontend.

Next steps

Review the current auth-service/middleware.ts to see if the jti allow-list logic is already present.
Implement the 30-second grace period logic in the token exchange function.
Update the frontend interceptor to handle the 401.13 sub-status code.

Connects securely to your tools. The creator never sees your data.

About this skill

Context switching between projects often leads to repetitive onboarding of your own AI. This skill enables agents to automatically retrieve relevant definitions, past decisions, and preferences from previous sessions without manual copy-pasting.

What it does:

Automated search scans local session history for keywords matching the current prompt.
Privacy redaction filters out common secret formats before data is processed by subagents.
Smart synthesis uses a subagent to summarize historical excerpts into concise notes.
Resource bounding enforces strict limits on file counts, byte sizes, and search timeouts.
Context injection provides the main agent with relevant history without treating it as new instructions.

How it works:

The extension triggers automatically on every prompt before the main agent processes the request.
It searches configured JSONL session files, ranking matches by term relevance.
Historical data is sent to a memory subagent for synthesis into high-density context.
The synthesized notes are injected into the current prompt as reference material.

Frameworks & tools: This skill is built for agents utilizing local session logging. It requires a TypeScript runtime to execute the extensions/agent-memory-access.ts component and supports various LLM providers for synthesis.

Why this beats prompting it yourself: Manual context management is slow and prone to human error. This skill automates the retrieval process at the system level, ensuring your agent remembers specific technical decisions or project constraints you established weeks ago without you having to find the old logs.

Use cases:

Resuming complex coding tasks across different days or sessions.
Recalling specific architectural decisions made in previous project phases.
Maintaining consistent formatting preferences across disparate chat threads.
Referencing internal definitions established in past research sessions.

Known limitations: Memory is treated as untrusted reference data only. Historical instructions are ignored to prevent prompt injection. Performance depends on local file indexing speeds.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Agent Memory Access