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Memoose Sessions

Community
AndrewNgo-ini
memoose-sessions

Track a working session in memoose and distill it into lessons at the end. Use at the start of a task (load standing rules and preferences), at each step of a multi-step task (declare your Position and read the guidance), whenever the user states a goal, rule, preference, or environment fact, and when a task or conversation ends (declare the outcome, distill lessons).

Overview

PublisherAndrewNgo-ini
Repositorymemoose
Skill namememoose-sessions
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 Sessions 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-sessions
  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-sessions .claude/skills/memoose-sessions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memoose Sessions 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 Sessions 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 Sessions 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.

Sessions in memoose

A session is one conversation's short-lived memory: turns plus typed context entries. At the end it is distilled into a few durable lessons that join the permanent graph. memoose stores and packs; you curate and write.

At the start of work

  1. session_start(session_id?). Use a stable id per conversation if the host gives you one.
  2. Read standing_context (goals, rules, preferences, lessons_learned, tool_rules, environment_facts) and recent_lessons. Follow the rules and preferences without being asked.
  3. recall anything the task depends on.

During work

Record, do not narrate. Use session_set_context(session_id, section, content, confidence):

sectionput here
goalswhat the user is trying to achieve in this session
rulesconstraints the user states ("never force-push", "tests before commit")
preferencesstyle and tooling preferences ("uv not pip", "short answers")
environment_factsfacts about the machine, repo, services discovered while working
workflow_statewhere a multi-step task stands, so a later session can resume
tool_ruleshow a specific tool must be used here
success_patterns / failure_lessonswhat worked or failed and why
feedback+Name / -Name to raise or lower how strongly an entity is recalled

Replace an outdated entry with retire_entry_id instead of adding a second one. Add turns with session_add_turn only for exchanges worth distilling later: decisions, discoveries, corrections. Not every message.

Where you are

When the work is a multi-step task, declare your Position on the turn that reaches a step memory knows: session_add_turn(session_id, "assistant", "tests are green", position="run the test suite") (CLI: memoose session turn <id> --text ... --at "run the test suite"). The reply carries the Guidance from there: the Transitions two hops out with their Condition, Advice, Pitfall and how past runs ended. Read it, then decide; it is memory, not an instruction. Declaring is what builds the session's Trace, and the Trace is the only thing a later distillation can learn a procedure from. If the step is not a Procedure yet, the call says so; remember it first if the chain is worth keeping, otherwise leave position off.

At the end of work: distillation

  1. session_timeline(session_id). It returns batches, prior_lessons, and the rules below.
  2. Curate (per batch): propose lessons that are general beyond this session, actionable next time, supported by what actually happened, and not already in prior_lessons. Short title, one to three sentences, the evidence line. Fewer and sharper beats many. Never invent.
  3. Write or reject (per lesson): recall related lessons and facts. Reject if a prior lesson already says it or the evidence is thin. Otherwise write the text so it stands alone and list the entities it applies to (systems, technologies, people, conventions).
  4. publish_lessons(session_id, lessons) with applies_to names and entity_types for any name not yet remembered.
  5. session_end(session_id, outcome) with the Outcome: succeeded, failed, or abandoned (CLI: memoose session end <id> --outcome failed). Every Transition the Trace traversed counts it, and the reply returns the Trace and says what to distil from it.

Lessons about how, from contrast

When a session held a failure followed by a success, or the user corrected a step, the lesson is procedural and belongs in the graph as Procedures and Transitions, not as a sentence. Take the Trace session_end returned and guidance() from its first Position, and compare: which Transition did the failed run take that the successful one did not, or take too early, or skip? Write the difference as Transitions with condition, advice and pitfall (CLI --when, --do, --avoid). If a Transition memory already holds is what led astray, supersede it with the corrected one rather than adding a second; the failed route stays queryable as history. If the chain was right and the run failed for another reason, dismiss("transition:<id>", reason) so the next pass does not re-judge it. A Transition with several failed and no succeeded counts is the first place to look. This is how a procedure improves from experience without anyone editing it by hand; the store counts, you judge.

Ask before distilling anything personal the user did not ask you to keep.

Frequently asked questions

What does the Memoose Sessions AI skill do?

Track a working session in memoose and distill it into lessons at the end. Use at the start of a task (load standing rules and preferences), at each step of a multi-step task (declare your Position and read the guidance), whenever the user states a goal, rule, preference, or environment fact, and when a task or conversation ends (declare the outcome, distill lessons).

Why use Memoose Sessions on TypingMind?

Because you install it once and use it with any model. Memoose Sessions 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 Sessions in TypingMind?

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

Which AI models can use Memoose Sessions?

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 Sessions?

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

Is the Memoose Sessions 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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