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Hermes Search

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AlexAI-MCP
hermes-search

Search across past conversation sessions, memory files, and project history to recall what was discussed, decided, or built in previous sessions.

Overview

PublisherAlexAI-MCP
Repositoryhermes-CCC
Skill namehermes-search
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 Search 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-search
  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-search .claude/skills/hermes-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hermes Search 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 Search 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 Search 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-search

Search across past conversation sessions, memory files, and project files to recall what was discussed, decided, or built. This skill is the cross-session retrieval complement to hermes-compress, which writes the memory, and hermes-memory, which indexes it.

Invocation

/hermes-search "<query>"
/hermes-search --memory "<query>"
/hermes-search --project "<query>"
/hermes-search --recent <N>

Subcommands

/hermes-search "<query>" — Search All Memory

Searches all available memory sources in order:

  1. ~/.claude/projects/*/memory/*.md — all project memory files
  2. ~/.claude/history.jsonl — conversation history log (if accessible)
  3. Project files under the current working directory
  4. MEMORY.md index

Returns a unified result list sorted by relevance.

How Claude executes this:

bash
# Memory files
grep -r --include="*.md" -l "<query>" ~/.claude/projects/*/memory/

# Within matched files, extract surrounding context
grep -n -A 3 -B 3 "<query>" <matched-file>

# Project files (from cwd)
grep -r --include="*.md" --include="*.py" --include="*.ts" \
     --include="*.json" -l "<query>" .

# history.jsonl (if present)
grep "<query>" ~/.claude/history.jsonl | head -20

/hermes-search --memory "<query>" — Memory Files Only

Restricts search to ~/.claude/projects/*/memory/*.md. Faster and more focused. Use this when you know the information was captured in a previous session via hermes-compress or hermes-memory.

How Claude executes this:

bash
grep -r --include="*.md" -n -A 3 -B 3 "<query>" \
     ~/.claude/projects/*/memory/

Returns: file path, line number, and 3 lines of surrounding context per match.


/hermes-search --project "<query>" — Project Files Only

Restricts search to the current working directory. Useful for recalling where a piece of code or config lives.

How Claude executes this:

bash
grep -r --include="*.md" --include="*.py" --include="*.ts" \
     --include="*.js" --include="*.json" --include="*.yaml" \
     -n -l "<query>" .

Claude then reads the top 3 matched files and extracts the relevant sections.


/hermes-search --recent <N> — Recent N Sessions Summary

Lists and summarizes the most recent N session memory files.

How Claude executes this:

bash
ls -t ~/.claude/projects/*/memory/session_*.md | head -<N>

Claude reads each file and produces a one-line summary per session: [date] [project] — <top decision or artifact from that session>


Output Format

All subcommands return results in the following structure:

## Search Results: "<query>"
Source: --memory | --project | all
Query time: <timestamp>

### Match 1
- Source: ~/.claude/projects/ontology/memory/session_20260321_1430.md (line 47)
- Relevance: high
- Snippet:
  > decisions:
  >   - "Use Neo4j as primary graph store for ontology nodes"  ← MATCH
  >   - "REST API over gRPC for external integrations"

### Match 2
- Source: ./AlexAI/api/routes.py (line 12)
- Relevance: medium
- Snippet:
  > # Neo4j connection pool config  ← MATCH
  > driver = GraphDatabase.driver(uri, auth=(user, pw), max_connection_pool_size=20)

---
Total matches: 2 | Searched: 14 files

Relevance scoring (heuristic):

  • High: exact phrase match in a decisions or facts_learned block
  • Medium: keyword match in a session file body or code comment
  • Low: keyword match in a filename or metadata field

What Gets Searched

SourcePath patternFormat
Session summaries~/.claude/projects/*/memory/session_*.mdYAML/Markdown
Memory index~/.claude/projects/*/memory/MEMORY.mdMarkdown
Honcho profiles~/.claude/projects/*/memory/user_honcho_profile.mdYAML/Markdown
Project source files./**/*.{md,py,ts,js,json,yaml}Any
History log~/.claude/history.jsonlJSONL (if accessible)

Handling Missing Sources

  • If ~/.claude/history.jsonl does not exist or is not readable, Claude skips it and notes: "history.jsonl not accessible — searching memory files only."
  • If no memory files exist, Claude reports: "No memory files found. Run /hermes-compress after a session to start building memory."
  • If project files return too many matches (>50), Claude limits output to the top 10 by file modification date (most recent first).

Relationship to Hermes FTS5 Session Search

This skill is the Claude Code equivalent of Hermes Agent's full-text search over its SQLite session store (using SQLite FTS5). In Hermes, /search <query> performs a ranked FTS5 query over all session message content. In hermes-CCC, the equivalent is grep-based search over the Markdown memory files written by hermes-compress. The result schema (source, relevance, snippet) mirrors the Hermes search output contract.

For projects that generate large volumes of session memory, consider building a local SQLite FTS5 index over the memory files using the hermes-memory skill's index-build subcommand.


Notes

  • Search is case-insensitive by default. Add --case-sensitive flag to override.
  • Queries containing spaces should be quoted: /hermes-search "Neo4j connection pool".
  • /hermes-search does not modify any files. It is always read-only.
  • Results are printed to the conversation; they are not automatically written to memory. If a search result is important, follow up with /hermes-memory add to pin it.

Frequently asked questions

What does the Hermes Search AI skill do?

Search across past conversation sessions, memory files, and project history to recall what was discussed, decided, or built in previous sessions.

Why use Hermes Search on TypingMind?

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

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

Which AI models can use Hermes Search?

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 Search?

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

Is the Hermes Search 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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