# Memory Management

> Guide the agent to recall, remember, and route durable learning into Memory, Skills, Scheduled Tasks, or Tape.

- **Type:** Skill
- **Install:** `agentstack add skill-thinkinaixyz-deepchat-memory-management`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ThinkInAIXYZ](https://agentstack.voostack.com/s/thinkinaixyz)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [ThinkInAIXYZ](https://github.com/ThinkInAIXYZ)
- **Source:** https://github.com/ThinkInAIXYZ/deepchat/tree/dev/resources/skills/memory-management
- **Website:** https://deepchat.thinkinai.xyz/

## Install

```sh
agentstack add skill-thinkinaixyz-deepchat-memory-management
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Memory Management

Use this skill when a task may produce durable learning or when the user asks you to recall, remember, continue earlier work, preserve an exact statement, capture a reusable procedure, or handle a recurring need.

## Recall

Rely on automatic memory injection for ordinary context. Use `memory_recall` when the user refers to previous work with cues such as again, last time, before, continue, same project, remember, or asks what you already know.

Use `tape_search` and then `tape_context` when the user needs source evidence, exact wording, logs, command output, file snippets, or why a prior decision was made. Memory is a durable conclusion layer, not the raw transcript.

## Remember

Use `memory_remember` only for durable conclusions that should change future behavior. Choose the most specific category:

- `user_preference`: stable user preferences, constraints, communication style, environment choices.
- `project_fact`: durable project conventions, architecture entry points, commands, dependencies, paths, or operational constraints.
- `task_outcome`: completed, blocked, or deliberately deferred task results. Include status, outcome, and blocker in prose when relevant.
- `heuristic`: reusable troubleshooting strategy, workflow, decision rule, or engineering lesson.
- `anti_pattern`: repeated mistake, unsafe approach, brittle pattern, stale assumption, or thing to avoid.

Do not remember raw tool results, bash output, grep output, file contents, transient mechanics, one-off failures, secrets, credentials, hidden reasoning, or anything only useful for the current turn.

## Verbatim Scope

Store exact wording only when the user explicitly asks you to remember a sentence or phrase verbatim. In that case, keep the requested text intact and make the surrounding content minimal.

Automatic extraction is different: it should normalize durable facts into concise memory content, deduplicate related entries, and avoid preserving raw transcript text.

## Procedures -> Skill

When the useful learning is a reusable multi-step procedure, prefer drafting a skill with `skill_manage` instead of stuffing the full procedure into Memory. Memory may keep a short pointer or heuristic, but the repeatable workflow belongs in a Skill.

Use `skill_manage` for draft skills only. Do not modify installed skills unless the user explicitly asks through the supported review flow.

## Recurring -> Scheduled Task

When the user asks for a periodic, low-frequency, or future recurring action, suggest creating a Scheduled Task in settings. Memory does not wake the agent, schedule future work, or create automation side effects.

## End-of-task Learning Check

Before finishing a non-trivial task, check whether there is one durable lesson to save:

1. Did the user reveal a stable preference or constraint?
2. Did you learn a durable project fact?
3. Is there a task outcome, blocker, or explicit deferral worth preserving?
4. Did a reusable heuristic work?
5. Did an anti-pattern or stale assumption become clear?
6. Is this actually a reusable procedure for `skill_manage` or a recurring need for Scheduled Tasks rather than Memory?

Remember only the smallest durable conclusion. Leave raw process in Tape.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ThinkInAIXYZ](https://github.com/ThinkInAIXYZ)
- **Source:** [ThinkInAIXYZ/deepchat](https://github.com/ThinkInAIXYZ/deepchat)
- **License:** Apache-2.0
- **Homepage:** https://deepchat.thinkinai.xyz/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-thinkinaixyz-deepchat-memory-management
- Seller: https://agentstack.voostack.com/s/thinkinaixyz
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.