Guide the agent to recall, remember, and route durable learning into Memory, Skills, Scheduled Tasks, or Tape.
Install
npx skillscat add thinkinaixyz/deepchat/memory-management Install via the SkillsCat registry.
Here's a thinking process: 1. **Analyze the Request:** - **Goal:** Write a 2-3 sentence plain-text summary in English. - **Subject:** An AI agent skill called `memory-management`. - **Source Material:** Provided description and SKILL.md excerpt. - **Key Points to Cover:** - What the skill does - What problem it solves - When an agent/developer should use it - **Constraints:** - Objective, factual tone - No marketing language, superlatives, calls to action - Natural prose, no bullet points, no headings, no markdown - No quotes wrapping - At most 60 words - **Output:** ONLY the summary text. 2.
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:
- Did the user reveal a stable preference or constraint?
- Did you learn a durable project fact?
- Is there a task outcome, blocker, or explicit deferral worth preserving?
- Did a reusable heuristic work?
- Did an anti-pattern or stale assumption become clear?
- Is this actually a reusable procedure for
skill_manageor a recurring need for Scheduled Tasks rather than Memory?
Remember only the smallest durable conclusion. Leave raw process in Tape.