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Active Memory Reminder

Community
Archive228
active-memory-reminder

Before compaction Loopkit extracts decisions into claude-decisions.json (machine-readable). Read it alongside claude-progress.txt at session start — prose is for humans, JSON is for the loop.

Overview

PublisherArchive228
Repositoryloopkit
Skill nameactive-memory-reminder
Stars
755
Forks
124
Bundled files
Instructions only
LicenseMIT
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 Archive228 on GitHub. Read the source before you install it.

Installation

Install the Active Memory Reminder 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/Archive228/loopkit/tree/main/skills/active-memory-reminder
  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/Archive228/loopkit.git /tmp/loopkit
mkdir -p .claude/skills
cp -r /tmp/loopkit/skills/active-memory-reminder .claude/skills/active-memory-reminder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Active Memory Reminder 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 Active Memory Reminder 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 Active Memory Reminder 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.

Active Memory Reminder

Loopkit splits shift-notes across two files on purpose:

  • claude-progress.txt — free-form prose. Human-readable narrative of what the last session did, what is in flight, what to pick up next. Wins for context and intent. Loses when the model needs to decide whether a decision was made.
  • claude-decisions.json — machine-readable array of {ts, decision} entries, appended by the loopkit pre-compact hook every time the transcript is about to be compacted. Wins for durability of specific choices ("chose Postgres over SQLite because…", "rejected the polling approach", "tried htmx and switched to Alpine").

Compaction is where reasoning dies. The summarizer keeps the shape of the work but strips the why. claude-decisions.json is the durable side-channel that survives that. This is the same pattern Meta's NapMem paper calls out for behavioral-state-decay in long-running agents: prose degrades faster than structured facts because the model rewrites prose freely and edits structured data carefully (also why loopkit uses JSON for feature_list.json; see [[feature-list-json]]).

When to apply

  • Session start / post-/clear / post-compact. Read claude-decisions.json right after claude-progress.txt and before you look at code. If both files disagree, the JSON is the more recent hard record of a specific choice; the prose gives you the reason.
  • Before re-litigating a design choice. If you're about to propose "let's use X instead of Y," grep claude-decisions.json first. If a prior session already rejected X and wrote it down, don't burn the token budget re-deriving that.
  • When picking up mid-implementation work. Decisions frame constraints the progress file may not restate.

File contract

claude-decisions.json is a JSON array. Every entry is {ts, decision}:

json
[
  {"ts": "2026-07-14T18:22:09Z", "decision": "chose Playwright MCP over Puppeteer because Puppeteer's alert-modal blind spot bit us in run 41"},
  {"ts": "2026-07-14T20:04:11Z", "decision": "rejected the daemon-per-project approach; switched to a single supervisor with per-project subdirs"},
  {"ts": "2026-07-15T09:31:44Z", "decision": "tried and failed to cache the plan across sessions — plan went stale within two sessions, dropped"}
]

Constraints:

  • Append-only in normal operation. The pre-compact hook appends; sessions read.
  • You MAY add a decision by hand if you make one mid-session that the hook won't catch (e.g., a decision made in code, not prose). Use the same shape. Do not rewrite or delete existing entries — they are the durable record.
  • If entries contradict, newer wins, but note the contradiction in claude-progress.txt so the reason is captured.

The hook

.claude/hooks/pre-compact runs on both manual (/compact) and auto compaction. It skims the transcript for phrases matching decided|chose|rejected|tried and failed|switched from .* to|going with|not going to, dedupes, caps at 20 per compaction, timestamps each, and appends. It is silent-safe: any failure logs to stderr and exits 0 so compaction is never blocked.

Anti-patterns

  • Reading claude-progress.txt and skipping claude-decisions.json. You will re-open settled questions. The prose file often omits why we didn't do the other thing — that's what the JSON is for.
  • Editing or deleting past decisions to "clean them up." Same rule as feature_list.json steps and descriptions: durable structured records are load-bearing. Contradict them in a new entry; don't erase the old one.
  • Storing prose in claude-decisions.json. It is not a second progress file. Entries are single decisions with a timestamp, nothing else.
  • Assuming the hook caught it. The regex catches most decisions but not all. If you made a decision the transcript won't obviously match, add it by hand before ending the session.

Related

  • [[progress-reading-protocol]] — session-opening sequence; step 2b reads this file.
  • [[feature-list-json]] — the other structured-over-prose file loopkit relies on.
  • [[shift-notes]] — the prose companion this file is paired with.
  • [[clean-state-contract]] — session-end discipline that keeps the pairing usable.

Frequently asked questions

What does the Active Memory Reminder AI skill do?

Before compaction Loopkit extracts decisions into claude-decisions.json (machine-readable). Read it alongside claude-progress.txt at session start — prose is for humans, JSON is for the loop.

Why use Active Memory Reminder on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Archive228/loopkit/tree/main/skills/active-memory-reminder. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Active Memory Reminder?

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 Active Memory Reminder?

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

Is the Active Memory Reminder AI skill free?

Yes. It is published on GitHub by Archive228 under the MIT 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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