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Auto Memory

Community
jh941213
auto-memory

Use when starting substantial work in a repo (implementation, fixes, deploys, debugging), when first exploring a new repo, or when finishing work that produced reusable knowledge. Operates a routing table that selectively loads only the docs/memory relevant to the task type

Overview

Publisherjh941213
Repositorymy-cc-harness
Skill nameauto-memory
Stars
125
Forks
35
Bundled files
Instructions only
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 jh941213 on GitHub. Read the source before you install it.

Installation

Install the Auto 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.
  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/jh941213/my-cc-harness.git /tmp/my-cc-harness
mkdir -p .claude/skills
cp -r /tmp/my-cc-harness/skills_en/auto-memory .claude/skills/auto-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto 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 Auto 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 Auto 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.

Auto-Memory (docs-routing selective memory)

Principle: route knowledge, don't copy it. Only the routing table is always loaded; bodies (docs and memory) are Read only when the task type matches.

The harness already generates documentation in {project}/docs/ (the /docs suite, DEPLOY.md, ARCHITECTURE.md, ...). Those docs can't all be injected into every context, so this skill's job is to route the right docs into context when a matching task starts — e.g. a "deploy this" request loads the CI/CD doc first.

Store layout

{project}/memory/
├── INDEX.md      # routing table (format below) — auto-injected at session start / post-compact, keep lean
└── {topic}.md    # ONLY knowledge with no docs home (environment quirks, gotchas). If a doc exists, don't create one

INDEX.md format (the keyword column powers the deterministic hint hook):

markdown
# Memory Index (routing table)

| Task type | Keywords | Files to load |
|------|------|------|
| Deploy/CI | deploy,docker,release,ci | docs/ops/cicd.md, DEPLOY.md |
| Architecture/design | design,structure,refactor,architecture | docs/ARCHITECTURE.md |
| API work | api,endpoint,router | docs/api/ |
| Testing | test,pytest | memory/testing.md |

How it works (dual routing)

  1. Deterministic layer (hook): memory-route-hint.sh (UserPromptSubmit) matches prompt keywords against INDEX rows and injects a one-line hint — "this looks like an X task → Read these files first". It never injects document bodies
  2. Model layer (skill): even without a hint, classify the task at start, consult INDEX, and Read only the matching files — if nothing matches, load nothing

Workflows

A. Init (repo has no memory/INDEX.md)

  1. Survey the repo: full docs/ listing, README, DEPLOY.md-style files, CI config (.github/workflows), manifests, layout
  2. Classify existing docs by task type and create INDEX.md routing rows — docs own the truth, INDEX is the map
  3. Create memory/{topic}.md only for knowledge with no docs home (no empty stubs)

B. Task start (selective load)

  1. If the hook emitted a hint, Read those files first
  2. Otherwise classify the task → Read only INDEX-matched files
  3. If loaded content is stale (dead paths etc.), fix the INDEX/doc on the spot

C. Task end (routing new knowledge)

The test: "needed the next time this task type comes up?" Priority:

  1. If a doc owns that knowledge, update the doc (never copy into memory)
  2. If it has no docs home, update/create memory/{topic}.md + add an INDEX row
  3. Chronological journal goes to tasks/lessons.md (Stop-gate enforced); cross-project knowledge to ~/.claude/projects/*/memory/
  4. If new docs were created, add routing rows for them

D. Long sessions (mid-term memory)

For 30min+ work, maintain tasks/context.md (goal / key decisions / done·next / key files). It is auto-reinjected after compaction and at session start, so nothing written there gets forgotten.

Forbidden

  • Copying docs content into memory/ (pointers/routing only)
  • Loading docs/memory wholesale without consulting the index
  • Loading files unrelated to the task
  • Copying anything derivable from code — code is the source of truth
  • Creating empty topic files in advance

Frequently asked questions

What does the Auto Memory AI skill do?

Use when starting substantial work in a repo (implementation, fixes, deploys, debugging), when first exploring a new repo, or when finishing work that produced reusable knowledge. Operates a routing table that selectively loads only the docs/memory relevant to the task type

Why use Auto Memory on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jh941213/my-cc-harness/tree/main/skills_en/auto-memory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Auto 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 Auto Memory?

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

Is the Auto Memory AI skill free?

It is published on GitHub by jh941213. Check the repository for licensing terms. 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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