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Memory

Community
boshu2
memory

Find or curate supported lessons and agent rules. Use when: asked to remember, trace a corrected claim, audit instructions or write an agent rule.

Overview

Publisherboshu2
Repositoryagentops
Skill namememory
Stars
450
Forks
42
Bundled files
8
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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by boshu2 on GitHub. Read the source before you install it.

Installation

Install the Memory 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/boshu2/agentops/tree/main/plugin/skills/memory
  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/boshu2/agentops.git /tmp/agentops
mkdir -p .claude/skills
cp -r /tmp/agentops/plugin/skills/memory .claude/skills/memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory 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 Memory 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 Memory 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.

Memory

Use maintained experience only when it changes an action. Memory is optional: no mandatory recall at RPI entry, lesson at completion, worksheet, page quota or background mining. A trivial edit can proceed directly to implementation.

Rules for any saved lesson or rule

Apply these whenever a request would save or promote a lesson, including a request to turn an incident into a rule for an instruction file:

  1. Look for an existing entry first. Search the selected .context/ pages, external topic pages or the target instruction file (AGENTS.md, CLAUDE.md, a team rules file). Amend the entry that already covers the behavior; add a new one only when none fits. If the final code, tests, comments or a doc already explain the reasoning, point there rather than save a copy; save only what they cannot show a later reader, such as ruled-out causes, why the fix works and what brings the failure back. Effort, a finished task or a large diff never qualifies a lesson. If nothing qualifies, save nothing and say why.
  2. Size the claim to its evidence. One incident supports a narrow observation scoped to the conditions it actually had. A universal rule needs repeated independent occurrences and later reapplication. When a recurring correction is a mechanical slip that a lint rule, test or hook could catch, propose that check alongside the prose rule or in its place, as Postmortem does for such a miss; a check fails where a rule can only ask.
  3. State all five fields: applicability, action, support, limits and invalidation. Use the template below; an entry with no invalidation is incomplete. For an incident, applicability includes the exact error text or observable symptom, the words a later search will use. An instruction file such as AGENTS.md loads into every session, so its entry is usually one line: the action and when it applies, worded as information rather than an order to read, linking the project .context/ page or the doc beside the governed code (never an external-bundle page from a checked-in file) that carries all five fields. A line that only restates what agents already do by default earns no place.
  4. Review before admission. Draft outside Git. A fresh reviewer who is not the author checks factual support and disclosure of the exact text, paths and destination before it enters .context/, an instruction file such as AGENTS.md, or any Git object. Promoting an entry into an instruction file is a separate policy change owned by that file.
  5. Saving proves nothing. Only later work can show that reuse changed an action and helped. Until then a saved rule is an untested hypothesis.
markdown
### <one-line claim>
- Applies when: <the conditions the evidence covered>
- Action: <the check or step to take>
- Support: <cited sources; number of independent occurrences>
- Limits: <where it is untested or may not hold>
- Invalidate when: <the change or contrary evidence that retires it, and how to check it>

Keep rare useful constraints; age or low frequency alone is no reason to delete them. Learning may also simplify or remove rules.

Choose one operation

NeedRead on demand
An earlier constraint or source map may change the next actionFind / recall
Capture useful evidence from selected sources, episodes or correctionsCapture / mine / learn
Update, qualify, consolidate or retire a supported claimCurate / qualify / retire
Rank repeated operational friction in supplied historyToil evidence
Trace a corrected claim or audit the loaded instruction setInstruction sources

Memory owns these operations; other roles link here instead of keeping their own procedures. Capture includes bounded source maps, verdicts, corrections and failed or harmful reuse; it is never a required completion step. Load only the selected operation's reference. The optional OKF page profile checks structure only.

One authority per fact

BD or the caller's tracker owns work, status, dependencies and handoffs; Git owns content and delivery history; native sessions and CASS own episode evidence. Reviewed Markdown topic pages in a selected project .context/ or an external bundle hold reusable claims as evidence, not another work account. Existing docs, ADRs and code keep their declared authority; a page points to those owners instead of copying their policy. Do not make one lesson file per session, copy a transcript lake, or silently initialize a memory store.

For a selected project .context/, start at its small authored README.md map when relevant, then read likely pages and their current source owners with ordinary filesystem tools such as rg and cat; this needs neither BD nor AO. Reads create no directory, index or private import. The optional ao config context route supports external bundles and an explicitly bound canonical direct <consumer>/.context; it requires native BD and preserves the policy and identity bindings. Other consumer-overlapping roots remain refused.

Access, storage and honest limits

Use only sources already authorized for the task, owner, model/provider and exact destination. Read permission does not imply publication or Git storage. This lean path supports public or already-cleared trial inputs only. Neither this skill nor a prompt, worktree or same-user shell enforces restricted-source access, so do not retrieve restricted material through this path. The existing ao session read-source supported profile grants no broader or automatic transcript access. Unavailable and denied evidence remain explicit gaps; do not fetch then redact.

Drafts and review proof stay in caller-selected protected external non-Git storage. Missing routing does not authorize a workspace fallback. Preserve requested legacy .agents/ proof and unique evidence under owner policy. No blind TTL or delete operation is part of Memory. Labels and structural parsers do not prove isolation. docs/adr/ADR-0016-state-tiers.md owns these boundaries in a repository checkout; the operation references carry the installed rules.

Saved pages, retrieval counts and structural checks prove no benefit. Only later work can demonstrate that reuse changed an action and helped its outcome; keep failed, harmful and no-change results. Mining is separately budgeted off-path and cannot delay finishing an already authorized change or alter its verdict.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Memory AI skill do?

Find or curate supported lessons and agent rules. Use when: asked to remember, trace a corrected claim, audit instructions or write an agent rule.

Why use Memory on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boshu2/agentops/tree/main/plugin/skills/memory. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Memory?

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 Memory?

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

Is the Memory AI skill free?

Yes. It is published on GitHub by boshu2 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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