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Hermes Memory

Community
AlexAI-MCP
hermes-memory

Manage durable project memory for Claude Code. Use when prefetching relevant history before work, syncing stable decisions after work, nudging for unsaved lessons, compressing noisy notes, or auditing memory quality.

Overview

PublisherAlexAI-MCP
Repositoryhermes-CCC
Skill namehermes-memory
Stars
134
Forks
22
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 AlexAI-MCP on GitHub. Read the source before you install it.

Installation

Install the Hermes 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/AlexAI-MCP/hermes-CCC/tree/master/skills/hermes-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/AlexAI-MCP/hermes-CCC.git /tmp/hermes-CCC
mkdir -p .claude/skills
cp -r /tmp/hermes-CCC/skills/hermes-memory .claude/skills/hermes-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hermes 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 Hermes 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 Hermes 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.

Hermes Memory

Purpose

  • Keep long-lived project facts out of ephemeral chat history.
  • Load only the memory that matters for the current task.
  • Save reusable lessons instead of repeating the same investigation later.
  • Prevent memory sprawl by separating durable facts from temporary chatter.
  • Make project knowledge auditable, editable, and diffable.

What Counts As Durable Memory

  • architecture decisions
  • naming conventions
  • repository-specific workflows
  • known failure signatures and fixes
  • user preferences that change how work should be done
  • stable environment constraints
  • reusable commands or validation routines
  • external integration quirks that repeatedly matter

What Does Not Belong In Memory

  • one-off task status
  • temporary branch names
  • speculative ideas that were never adopted
  • verbose transcripts
  • sensitive secrets or tokens
  • logs that can be regenerated
  • transient failures without a confirmed lesson

Suggested Layout

  • memory/MEMORY.md as the index
  • one file per topic or subsystem
  • stable headings inside each memory file
  • short descriptions in the index so prefetch remains cheap

Canonical Headings

  • ## Decisions
  • ## Preferences
  • ## Tooling Notes
  • ## Error Patterns
  • ## Validation
  • ## Open Questions

Operations

Prefetch

  • Use before nontrivial work.
  • Read the memory index first.
  • Select only files relevant to the current task.
  • Summarize the relevant items into a short working context.
  • Do not dump full memory files into the answer unless asked.

Sync

  • Use after completing work with durable lessons.
  • Review what changed in understanding, not just what files changed.
  • Merge new facts into the most specific existing file.
  • Create a new file only when no current topic fits cleanly.
  • Update MEMORY.md if a new file is created.

Nudge

  • Use after complex work when valuable lessons exist but should not be saved automatically.
  • Produce candidates and proposed destinations.
  • Ask for confirmation when the workflow or environment requires explicit approval.
  • Prefer small curated memory candidates over bulk transcript dumps.

Compress

  • Use when memory files become repetitive or bloated.
  • Merge duplicate bullets.
  • Promote stable headings.
  • Remove stale or contradicted material.
  • Replace long narratives with short factual bullets.

Status

  • Use to audit coverage and hygiene.
  • Check for missing files referenced by the index.
  • Check for oversized files that need compression.
  • Check for duplicate topics across files.

Prefetch Procedure

  1. Read the index.
  2. Match the current task to likely topics.
  3. Open only the top few relevant files.
  4. Extract facts that constrain the task.
  5. Separate hard constraints from softer preferences.
  6. Return a compact memory-context block.
  7. List what files were checked and which were actually relevant.

Sync Procedure

  1. Review the completed work.
  2. Ask what lesson would help six weeks from now.
  3. Reject task-local noise.
  4. Map each accepted lesson to an existing topic file.
  5. Add or edit concise bullets under stable headings.
  6. Update the index if the topic is new.
  7. Report what was saved and where.

Compression Rules

  • Keep one canonical phrasing for each stable fact.
  • Merge near-duplicate fixes into one general error pattern when possible.
  • Keep commands that were verified.
  • Remove commands that no longer match the current environment.
  • Prefer "why plus what" over long narratives.

Memory Entry Style

  • Write factual bullets, not paragraphs of storytelling.
  • Include dates when change over time matters.
  • Mention affected subsystem, file family, or command family.
  • Keep each bullet independently useful.
  • If a rule has exceptions, state them directly.

Example Entry

markdown
## Error Patterns

- 2026-04-07: PowerShell profile loading can fail under sandboxed runs. Use `login=false` for non-interactive repository inspection commands when possible.

Reporting Format For Prefetch

markdown
```memory-context
- Fact: project uses repo-local AGENTS instructions for coding behavior
- Preference: keep edits minimal and verify with targeted checks
- Error pattern: sandboxed PowerShell sessions may need profile loading disabled
```

Files checked:
- memory/MEMORY.md
- memory/tooling.md
- memory/repo-workflow.md

Relevant files:
- memory/tooling.md

Reporting Format For Sync

markdown
Saved memory updates:
- memory/tooling.md: added note about PowerShell profile failures in sandboxed runs
- memory/repo-workflow.md: added validation preference for targeted checks before broad test suites

Decision Rules

  • Save a fact only if it is likely to matter again.
  • Save a preference only if the user has shown it consistently.
  • Save a fix only if root cause or remedy is reasonably understood.
  • Save a command only if it was actually used or confidently verified.
  • Prefer edit-in-place over creating memory fragments.

Failure Modes

  • Saving too much and creating memory fatigue
  • Saving volatile details that go stale quickly
  • Creating duplicate files for the same topic
  • Failing to update the index when new files appear
  • Prefetching too much and turning memory into noise

Recovery Moves

  • If memory feels noisy, run compress before adding more.
  • If two files overlap, merge them into the more canonical topic.
  • If a past note is wrong, correct it directly and mention the superseding fact.
  • If uncertainty remains, store it under Open Questions rather than presenting it as settled.

Good Triggers

  • "load context before you start"
  • "remember this for next time"
  • "save the lesson from this bug"
  • "what project memory do we already have about this"
  • "compress the memory index"
  • "audit our memory files"

Checklist

  1. Decide whether the action is prefetch, sync, nudge, compress, or status.
  2. Read the index first.
  3. Keep scope narrow.
  4. Save only durable material.
  5. Use stable headings.
  6. Update the index when needed.
  7. Report the changes clearly.
  8. Leave the memory corpus smaller and more useful than before.

Frequently asked questions

What does the Hermes Memory AI skill do?

Manage durable project memory for Claude Code. Use when prefetching relevant history before work, syncing stable decisions after work, nudging for unsaved lessons, compressing noisy notes, or auditing memory quality.

Why use Hermes Memory on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlexAI-MCP/hermes-CCC/tree/master/skills/hermes-memory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hermes 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 Hermes Memory?

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

Is the Hermes Memory AI skill free?

Yes. It is published on GitHub by AlexAI-MCP 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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