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Memory

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yonatangross
memory

Read-side memory operations on the knowledge graph: search past decisions and patterns, load session context, view decision timelines, render Mermaid graph visualizations. Subcommands: search, load, history, viz, status. Use when finding or reviewing what memory already holds. For storing new knowledge instead, invoke remember; for retrieval internals, memory-fabric; for cleanup, dream.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namememory
Stars
298
Forks
35
Bundled files
13
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.

  • 13 bundled files

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

  • Open source

    Published by yonatangross 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/yonatangross/orchestkit/tree/main/plugins/ork/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/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/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 - Read & Access Operations

Unified read-side memory skill with subcommands for searching, loading, syncing, history, and visualization.

Cross-session read strategy (Opus 5.5 / CC 2.1.111+): Opus 5.5 reads filesystem memory reliably across sessions. When loading context at session start, prefer the layered read order:

  1. ~/.claude/projects/<slug>/memory/MEMORY.md (durable index — load first, always)
  2. .claude/chain/state.json + most recent NN-*.json handoff (session continuation)
  3. MCP mcp__memory__search_nodes for anything the filesystem index doesn't answer (typed graph traversal)

Layer 1 is cheap (small index file), Layer 2 is scoped (session-specific), Layer 3 is selective (only when needed). Avoid dumping the full knowledge graph into context — use the index to narrow the search first.

Argument Resolution

python
SUBCOMMAND = "$ARGUMENTS[0]"  # First token: search, load, history, viz, status
QUERY = "$ARGUMENTS[1]"       # Second token onward: search query or flags
# $ARGUMENTS is the full string (CC 2.1.59 indexed access)

Usage

bash
memory search <query>  # Search knowledge graph
memory load             # Load context at session start
memory history          # View decision timeline
memory viz              # Visualize knowledge graph
memory status           # Show memory system health

CRITICAL: Use AskUserQuestion When No Subcommand

If invoked without a subcommand, ask the user what they want:

python
AskUserQuestion(
  questions=[{
    "question": "What memory operation do you need?",
    "header": "Operation",
    "options": [
      {"label": "search", "description": "Search decisions and patterns in knowledge graph"},
      {"label": "load", "description": "Load relevant context for this session"},
      {"label": "history", "description": "Decision timeline + knowledge-graph viz (--mermaid)"},
      {"label": "status", "description": "Check memory system health"}
    ],
    "multiSelect": false
  }]
)

Subcommands

Load details: Read("references/memory-commands.md") for full usage, flags, output formats, and context-aware result limits for each subcommand.

SubcommandPurpose
searchSearch past decisions, patterns, entities. Supports --category (maps to metadata.category), --limit, --agent (scopes by agent_id), --global filter flags
loadAuto-load relevant memories at session start. Supports --project, --global
historyDecision timeline with table, Mermaid, or JSON output. Supports --since, --mermaid
vizRender knowledge graph as Mermaid diagram. See also Read("references/mermaid-patterns.md")
statusMemory system health check

Scheduled Sweeps (cron)

Nightly KG staleness report

Cron job in .github/workflows/memory-staleness.yml runs nightly at 04:00 UTC. Calls scripts/staleness_cron.py to scan ALL memory MCP entities, flag those whose latest observation timestamp is older than 30 days, and write a Markdown report to docs/reports/memory-staleness-YYYY-MM-DD.md.

bash
python3 ${CLAUDE_SKILL_DIR}/scripts/staleness_cron.py docs/reports/ \
    --threshold-days 30 --limit 50

Auto-skip conditions (all exit 0, all WARN-logged):

Skip reasonTrigger
yg-mcp-core not importableyg-mcp-core>=0.3.0 not installed (orchestkit is public; yg-mcp-core lives on private pypi.yonyon.ai — HQ-only)
memory MCP unreachablememory MCP server down OR .mcp.json doesn't define memory

Report contents:

  • Total / stale / fresh entity counts
  • Top N stale entries sorted by staleness (no-timestamp first, then oldest → newest)
  • Each row: name, entityType, age in days, observations count
  • Suggested actions per age bucket (>90d review, >180d archive candidate)

Treats entities with no parsable timestamp as stale — operator intervention is the right outcome (backfill last_read observation OR prune).

Pure helpers (parse_iso_timestamp, latest_observation_timestamp, is_stale, build_report_payload, render_markdown) live in sibling staleness_lib.py for unit-testability.

Mirrors Yonatan-HQ/hq-ext-plugin#194 (audio_podcast) and orchestkit#1886 (post-synth podcast) + #1887 (memory writeback) pattern. Unblocked by Yonatan-HQ/core#993 (yg-mcp-core 0.3.0).


Workflow

1. Parse Subcommand

Extract first argument as subcommand
If no subcommand -> AskUserQuestion
Validate subcommand is one of: search, load, history, viz, status
Parse remaining flags
Check for --agent <agent-id> flag → agent_id: "ork:{agent-id}"

2. Execute Subcommand

Route to appropriate handler based on subcommand.

3. Report Results

Format output appropriate to the operation.


Rules Quick Reference

RuleImpactWhat It Covers
entity-extraction-patterns (load rules/entity-extraction-patterns.md)HIGHEntity types, relation types, graph query semantics
deduplication-strategy (load rules/deduplication-strategy.md)HIGHEdit-over-Write pattern, anchor-based insertion, verification

Session Resume

Load details: Read("references/session-resume-patterns.md") for CC 2.1.31 resume hints, context capture before ending, and resume workflows for PRs, issues, and implementations.


Related Skills

  • ork:remember - Store decisions and patterns (write-side)

Error Handling

  • If graph empty for viz: Show helpful message about using remember
  • If subcommand invalid: Show usage help
  • If memory files corrupt: Report and offer repair
  • If search query empty: Show recent entities instead
  • If no search results: Suggest alternatives

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?

Read-side memory operations on the knowledge graph: search past decisions and patterns, load session context, view decision timelines, render Mermaid graph visualizations. Subcommands: search, load, history, viz, status. Use when finding or reviewing what memory already holds. For storing new knowledge instead, invoke remember; for retrieval internals, memory-fabric; for cleanup, dream.

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/yonatangross/orchestkit/tree/main/plugins/ork/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 yonatangross 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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