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Memoose Upkeep

Community
AndrewNgo-ini
memoose-upkeep

Judge what memoose's store surfaces for maintenance. Use when remember returns hotspots or a superseded warning, when recall shows a fact flagged contested or several values for one relation, when the user says a stored fact is wrong or outdated, when the user asks to clean up or consolidate memory, after a large batch of remembers, or when a session start says upkeep is due.

Overview

PublisherAndrewNgo-ini
Repositorymemoose
Skill namememoose-upkeep
Stars
58
Forks
8
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AndrewNgo-ini on GitHub. Read the source before you install it.

Installation

Install the Memoose Upkeep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AndrewNgo-ini/memoose/tree/main/harness/skills/memoose-upkeep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AndrewNgo-ini/memoose.git /tmp/memoose
mkdir -p .claude/skills
cp -r /tmp/memoose/harness/skills/memoose-upkeep .claude/skills/memoose-upkeep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memoose Upkeep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Memoose Upkeep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Memoose Upkeep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Upkeep in memoose

memoose never decides what conflicts, what is a duplicate, or what deserves a connection. It surfaces candidates; you judge; you record the judgment. Nothing is deleted: superseded facts stay in history and leave default recall, and a declined candidate is remembered so it is not asked again.

memoose maintain prints every candidate in one worklist: hotspots, open contradictions, duplicate names, unconnected pairs, stale summaries, sessions that ended without lessons, and the reasons behind earlier dismissals. It changes nothing on its own. Without a shell, the tools behind it are contradiction_candidates and memify_candidates(kind).

Hotspots and contradictions

A hotspot is one subject holding several values for one relation. For each, decide:

  • Superseded in time: the newer fact replaced the older one (a new owner, a new database, a moved team). supersede(old_relation_id, new_relation_id, reason). Prefer this whenever valid_from, updated_at or the evidence shows an order.
  • Genuine contradiction: both claim to be current and cannot both be true. mark_contradiction(first, second, reason, confidence), then tell the user which two facts conflict, cite their evidence, and ask which is right. When they answer, supersede the wrong one with their answer as the reason.
  • Compatible: dismiss it (below). Many-valued relations (uses, knows, depends_on) legitimately hold several objects.

If a relation is single-valued by nature (owned_by, current_version, deployed_in, reports_to), declare_functional_relations([...]) once. New assertions then supersede old ones automatically and you will not be asked again. Never declare a many-valued relation functional.

Two facts contradict only when they cannot both be true at the same time about the same subject: mutually exclusive values, direct negations, logically incompatible statements. Do not mark facts that are different but compatible, a more and a less specific version of one fact, or paraphrases. Confidence: 0.9+ when the relation is obviously single-valued and both facts are explicit; 0.6 to 0.8 when it depends on interpretation; below 0.6, do not mark, ask the user.

A Procedure with several Transitions (leads_to, triggers, ...) is a branch, not a hotspot. The store does not list those, and you should not mark them.

Duplicates, connections, summaries

  1. Consolidate: near-duplicate names (same tokens, acronym and expansion, similar spelling, same type). Merge only when both names denote the same real thing: merge_entities(keep, drop) keeps the fuller, more-mentioned name and records the other as an alias. When unsure, ask the user; a wrong merge is hard to undo.

  2. Cross-connect: entity pairs that share source chunks but have no relation, with the shared context. Add a relation only when the context states a real relationship, with cross_connect: a snake_case name, a one-sentence description using both names, and the chunk or file as evidence. Skip pairs that merely co-occur.

  3. Global context: buckets (one per entity type) whose members changed since their summary, with the facts to summarise. Write each from the listed facts only, then set_bucket_summary:

    This bucket is about:
    - <Category>: <names>
    Facts:
    - <self-contained sentence>

    global_context() shows all buckets; recall(..., mode="summaries") returns them.

  4. Undistilled sessions: session_timeline then publish_lessons, per the sessions skill.

Declining a candidate

Judging a candidate and doing nothing means it comes back next time. When two names are not the same thing, a pair has no real relationship, a hotspot's values coexist, or a Transition was right and the run that failed on it was not: memoose dismiss <key> --reason "..." (tool: dismiss_candidate(key, reason)), with the key printed next to the item (transition:<relation_id> for a Transition). The candidate is filtered from later passes and the reason is shown under dismissed. Nothing about the graph changes.

Weights

Recall rank is multiplied by frequency (how often an entity is re-mentioned) and feedback (session_set_context(section="feedback", content="+Name -Other")). Use feedback when the user says a memory keeps surfacing wrongly or matters more than it ranks.

Reading recall output

contested: true means an open contradiction touches the fact. superseded: true appears only with include_superseded=true; use that for "what did we used to..." questions. history(relation_id=...) shows every assert, supersede and traverse with time and evidence.

When to run

When remember warns, when recall returns obvious duplicates, after ingesting a document or a long session, when the user asks for an overview and global_context reports stale buckets, or when the session-start context says upkeep is due. On hosts with subagents, hand the pass to memory-keeper so it does not occupy the main conversation.

Frequently asked questions

What does the Memoose Upkeep AI skill do?

Judge what memoose's store surfaces for maintenance. Use when remember returns hotspots or a superseded warning, when recall shows a fact flagged contested or several values for one relation, when the user says a stored fact is wrong or outdated, when the user asks to clean up or consolidate memory, after a large batch of remembers, or when a session start says upkeep is due.

Why use Memoose Upkeep on TypingMind?

Because you install it once and use it with any model. Memoose Upkeep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Memoose Upkeep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AndrewNgo-ini/memoose/tree/main/harness/skills/memoose-upkeep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memoose Upkeep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Memoose Upkeep?

As many as you like. As long as a model supports skills, you can use Memoose Upkeep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Memoose Upkeep AI skill free?

Yes. It is published on GitHub by AndrewNgo-ini under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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