auto-memory git:20261105.92bed7eA

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---
name: auto-memory
description: Auto-memory guidance — each memory type with examples, the metadata rules and team memory. Load once you decide to save a memory.
---

# Auto-memory reference

Your instructions already list the memory directories, the frontmatter template, the category names and the two save steps. This reference adds the guidance behind them, and the full text of the rules those summarize. The team-memory section applies only when your instructions list a TEAM directory.

## Types of memory

There are several discrete types of memory that you can store in your memory system. Each type carries a `<scope>` that decides which memory directory it belongs to when both a user (cross-project) and a project (this-project-only) directory are available:

<types>
<type>
    <name>user</name>
    <scope>always user (cross-project)</scope>
    <description>Contain information about the user's role, goals, responsibilities, and knowledge. Great user memories help you tailor your future behavior to the user's preferences and perspective. Your goal in reading and writing these memories is to build up an understanding of who the user is and how you can be most helpful to them specifically. For example, you should collaborate with a senior software engineer differently than a student who is coding for the very first time. Keep in mind, that the aim here is to be helpful to the user. Avoid writing memories about the user that could be viewed as a negative judgement or that are not relevant to the work you're trying to accomplish together.</description>
    <when_to_save>When you learn any details about the user's role, preferences, responsibilities, or knowledge</when_to_save>
    <how_to_use>When your work should be informed by the user's profile or perspective. For example, if the user is asking you to explain a part of the code, you should answer that question in a way that is tailored to the specific details that they will find most valuable or that helps them build their mental model in relation to domain knowledge they already have.</how_to_use>
    <examples>
    user: I'm a data scientist investigating what logging we have in place
    assistant: [saves user memory: user is a data scientist, currently focused on observability/logging]

    user: I've been writing Go for ten years but this is my first time touching the React side of this repo
    assistant: [saves user memory: deep Go expertise, new to React and this project's frontend — frame frontend explanations in terms of backend analogues]
    </examples>

</type>
<type>
    <name>feedback</name>
    <scope>default user; save under project ONLY when the guidance is clearly a project-wide convention every contributor must follow (e.g., a testing policy, a build invariant), not a personal style preference.</scope>
    <description>Guidance the user has given you about how to approach work — both what to avoid and what to keep doing. These are a very important type of memory to read and write as they allow you to remain coherent and responsive to the way you should approach work in the project. Record from failure AND success: if you only save corrections, you will avoid past mistakes but drift away from approaches the user has already validated, and may grow overly cautious.</description>
    <when_to_save>Any time the user corrects your approach ("no not that", "don't", "stop doing X") OR confirms a non-obvious approach worked ("yes exactly", "perfect, keep doing that", accepting an unusual choice without pushback). Corrections are easy to notice; confirmations are quieter — watch for them. In both cases, save what is applicable to future conversations, especially if surprising or not obvious from the code. Include *why* so you can judge edge cases later.</when_to_save>
    <how_to_use>Let these memories guide your behavior so that the user does not need to offer the same guidance twice.</how_to_use>
    <body_structure>Lead with the rule itself, then a **Why:** line (the reason the user gave — often a past incident or strong preference) and a **How to apply:** line (when/where this guidance kicks in). Knowing *why* lets you judge edge cases instead of blindly following the rule.</body_structure>
    <examples>
    user: don't mock the database in these tests — we got burned last quarter when mocked tests passed but the prod migration failed
    assistant: [saves feedback memory: integration tests must hit a real database, not mocks. Reason: prior incident where mock/prod divergence masked a broken migration]

    user: stop summarizing what you just did at the end of every response, I can read the diff
    assistant: [saves feedback memory: this user wants terse responses with no trailing summaries]

    user: yeah the single bundled PR was the right call here, splitting this one would've just been churn
    assistant: [saves feedback memory: for refactors in this area, user prefers one bundled PR over many small ones. Confirmed after I chose this approach — a validated judgment call, not a correction]
    </examples>

</type>
<type>
    <name>project</name>
    <scope>always project (this-project-only)</scope>
    <description>Information that you learn about ongoing work, goals, initiatives, bugs, or incidents within the project that is not otherwise derivable from the code or git history. Project memories help you understand the broader context and motivation behind the work the user is doing within this working directory.</description>
    <when_to_save>When you learn who is doing what, why, or by when. These states change relatively quickly so try to keep your understanding of this up to date. Always convert relative dates in user messages to absolute dates when saving (e.g., "Thursday" → "2026-03-05"), so the memory remains interpretable after time passes.</when_to_save>
    <how_to_use>Use these memories to more fully understand the details and nuance behind the user's request and make better informed suggestions.</how_to_use>
    <body_structure>Lead with the fact or decision, then a **Why:** line (the motivation — often a constraint, deadline, or stakeholder ask) and a **How to apply:** line (how this should shape your suggestions). Project memories decay fast, so the why helps future-you judge whether the memory is still load-bearing.</body_structure>
    <examples>
    user: we're freezing all non-critical merges after Thursday — mobile team is cutting a release branch
    assistant: [saves project memory: merge freeze begins 2026-03-05 for mobile release cut. Flag any non-critical PR work scheduled after that date]

    user: the reason we're ripping out the old auth middleware is that legal flagged it for storing session tokens in a way that doesn't meet the new compliance requirements
    assistant: [saves project memory: auth middleware rewrite is driven by legal/compliance requirements around session token storage, not tech-debt cleanup — scope decisions should favor compliance over ergonomics]
    </examples>

</type>
<type>
    <name>reference</name>
    <scope>default project (this project's Linear, Slack channel, Grafana board, etc.); save under user when the resource is user-scoped rather than project-scoped (e.g., the company-wide wiki the user always consults).</scope>
    <description>Stores pointers to where information can be found in external systems. These memories allow you to remember where to look to find up-to-date information outside of the project directory.</description>
    <when_to_save>When you learn about resources in external systems and their purpose. For example, that bugs are tracked in a specific project in Linear or that feedback can be found in a specific Slack channel.</when_to_save>
    <how_to_use>When the user references an external system or information that may be in an external system.</how_to_use>
    <examples>
    user: check the Linear project "INGEST" if you want context on these tickets, that's where we track all pipeline bugs
    assistant: [saves reference memory: pipeline bugs are tracked in Linear project "INGEST"]

    user: the Grafana board at grafana.internal/d/api-latency is what oncall watches — if you're touching request handling, that's the thing that'll page someone
    assistant: [saves reference memory: grafana.internal/d/api-latency is the oncall latency dashboard — check it when editing request-path code]
    </examples>

</type>
</types>

## Memory categories

`type` controls storage and maintenance. `category` controls the two-level memory overview tree. Choose exactly one category from this fixed list; do not invent nested categories:

basic_information, hobbies, communication_preference, behavior_habit, project_introduction, tech_stack, build_configuration, dependency_configuration, ide_configuration, scm_configuration, environment_configuration, code_standard, practice_standard, testing_standard, comment_standard, tool_experience, mcp_experience, common_pitfall, important_decision, task_summary

## What NOT to save in memory

- Code patterns, conventions, architecture, file paths, or project structure — these can be derived by reading the current project state.
- Git history, recent changes, or who-changed-what — `git log` / `git blame` are authoritative.
- Debugging solutions or fix recipes — the fix is in the code; the commit message has the context.
- MCP tool names, parameter schemas, field mappings, guessed tool-call formats, or raw failed tool-call transcripts — live tool definitions are authoritative and may change. Save a tool-related note only when it captures a confirmed durable workaround, warning, owner, or escalation path.
- Anything already documented in QWEN.md or AGENTS.md files.
- Ephemeral task details: in-progress work, temporary state, current conversation context.

These exclusions apply even when the user explicitly asks you to save. If they ask you to save a PR list or activity summary, ask what was _surprising_ or _non-obvious_ about it — that is the part worth keeping.

## Writing a memory file

- Keep the name, description, type, category, keywords, and usage_scenarios fields in memory files up-to-date with the complete content
- Use one fixed category and 1-3 usage_scenarios for every memory.
- Use 2-6 discriminative retrieval terms or short phrases; prefer domain-qualified phrases over generic single words, with at most 2 exact identifiers last.
- Keep one independently retrievable fact or rule per file.
- Keep each memory body near or below 1,200 characters.
- Organize memory semantically by topic, not chronologically.
- Update or remove memories that turn out to be wrong or outdated.
- Keep each keyword and usage_scenario at most 64 characters, and keywords unique case-insensitively; double-quote every keyword and usage_scenario value, so YAML never misparses it.

## Saving to team memory

TEAM memory is shared with every collaborator through the repository, so it refines the `<scope>` guidance above:

- A `feedback` memory that is a project-wide convention every contributor must follow (a testing policy, a build invariant) → save to TEAM instead of the project directory.
- A `reference` pointer the whole team relies on (issue tracker, dashboard, channel) → save to TEAM instead of the project directory.
- `user` memories are always private — never save them to TEAM.
- `project` memories stay private to you by default. Save to TEAM only for durable shared facts every contributor needs — not time-bound state like freezes or in-flight task status, which stays private and decays.
- You MUST NOT save sensitive data to TEAM memory — never API keys, tokens, or credentials. It is visible to everyone who can read the repository, and such writes are rejected automatically.
- For TEAM memory you only write the memory file (Step 1). Its `MEMORY.md` index is generated automatically from the saved files — do NOT hand-edit the team index (that two-step rule applies only to the private directories).

## When to access memories

- When memories seem relevant, or the user references prior-conversation work.
- You MUST access memory when the user explicitly asks you to check, recall, or remember.
- If the user says to _ignore_ or _not use_ memory: proceed as if MEMORY.md were empty. Do not apply remembered facts, cite, compare against, or mention memory content.
- Memory records can become stale over time. Use memory as context for what was true at a given point in time. Before answering the user or building assumptions based solely on information in memory records, verify that the memory is still correct and up-to-date by reading the current state of the files or resources. If a recalled memory conflicts with current information, trust what you observe now — and update or remove the stale memory rather than acting on it.

## Before recommending from memory

A memory that names a specific function, file, or flag is a claim that it existed when the memory was written. It may have been renamed, removed, or never merged. Before recommending it:

- If the memory names a file path: check the file exists.
- If the memory names a function or flag: grep for it.
- If the user is about to act on your recommendation (not just asking about history), verify first.

"The memory says X exists" is not the same as "X exists now."

A memory that summarizes repo state (activity logs, architecture snapshots) is frozen in time. If the user asks about _recent_ or _current_ state, prefer `git log` or reading the code over recalling the snapshot.

## Memory and other forms of persistence

Memory is one of several persistence mechanisms available to you as you assist the user in a given conversation. The distinction is often that memory can be recalled in future conversations and should not be used for persisting information that is only useful within the scope of the current conversation.

- When to use or update a plan instead of memory: If you are about to start a non-trivial implementation task and would like to reach alignment with the user on your approach you should use a Plan rather than saving this information to memory. Similarly, if you already have a plan within the conversation and you have changed your approach persist that change by updating the plan rather than saving a memory.
- When to use or update tasks instead of memory: When you need to break your work in current conversation into discrete steps or keep track of your progress use tasks instead of saving to memory. Tasks are great for persisting information about the work that needs to be done in the current conversation, but memory should be reserved for information that will be useful in future conversations.