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Hermes Insights

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AlexAI-MCP
hermes-insights

Generate insights about your Claude Code usage — what topics you work on most, common patterns, productivity trends.

Overview

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

Use it in TypingMind

Enable Hermes Insights 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 Insights 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 Insights 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-insights

Analyze your Claude Code usage patterns across sessions and produce a structured insight report: what topics you work on most, which skills you use, what types of projects dominate your workflow, and where your productivity is strongest.

Invocation

/hermes-insights
/hermes-insights --days <N>

Subcommands

/hermes-insights — Full Analysis

Analyzes all available memory files and session history with no time constraint.

/hermes-insights --days <N> — Time-Bounded Analysis

Restricts analysis to session files created in the last N days.

How Claude filters by date:

bash
find ~/.claude/projects/*/memory/session_*.md \
     -mtime -<N> -type f

Data Sources and What Claude Analyzes

1. Memory Files — Topic and Theme Extraction

bash
ls ~/.claude/projects/*/memory/session_*.md

For each session file, Claude reads the decisions, facts_learned, artifacts_created, and open_issues sections and extracts noun phrases as topic signals. Topics are then counted and ranked by frequency across all sessions.

Output: Top 10 topics, each with session count and a representative example decision or fact.

2. Skills Used — Skill Utilization

bash
ls ~/.claude/skills/
ls ~/.claude/projects/*/skills/ 2>/dev/null

Claude counts which skill directories exist and cross-references session memory files for any /skill-name invocation patterns mentioned in the decisions or facts_learned fields.

Output: Skill utilization table (skill name, invocation count estimate, last used date).

3. Project Types and Domains

Claude reads each session file's project: field (from the hermes-compress YAML) and groups sessions by project slug. It then infers the domain from artifact paths and topic keywords (e.g., .py artifacts + "Neo4j" keywords → "graph database / Python backend").

Output: Project breakdown table (project, session count, primary domain, last active date).

4. Conversation Patterns

Claude counts per-session: number of decisions made, artifacts created, problems solved, and open issues left unresolved. These become productivity metrics.

Output: Per-week averages for decisions, artifacts, and resolution rate (problems solved / open issues ratio).

5. Data Hygiene and Confidence Rules

  • Skip any session file that is missing the expected hermes-compress YAML block and record how many files were excluded.
  • Normalize topic strings by lowercasing, trimming punctuation, and folding obvious singular/plural variants before counting.
  • Prefer explicit evidence from decisions and artifacts_created over weak inference from prose when assigning project domains.
  • Mark a skill as inferred if the file only mentions the skill name indirectly and no /skill-name invocation is present.
  • Downgrade trend claims to low confidence when fewer than 5 sessions match the selected date range.
  • Report no sessions matched the filter instead of fabricating empty charts when --days <N> returns zero files.
  • Treat duplicate session paths with identical timestamps as one observation so repeated syncs do not inflate counts.
  • Fall back to unknown project when the project: field is absent and artifact paths do not provide a clear slug.
  • Separate unresolved carry-over work from newly opened issues so the resolution rate is not overstated.

Full Output Structure

## Claude Code Usage Insights
Generated: 2026-04-07 | Sessions analyzed: 23 | Date range: 2026-02-14 – 2026-04-07

### Top Topics
| Rank | Topic              | Sessions | Example |
|------|--------------------|----------|---------|
| 1    | Neo4j / graph DB   | 14       | "Use Neo4j as primary ontology store" |
| 2    | FastAPI / Python   | 11       | "REST endpoints for /query and /ingest" |
| 3    | Vercel deployment  | 7        | "Deploy to Vercel with vercel link --repo" |
| 4    | OpenCrab ontology  | 6        | "Fallback to neo4j when opencrab returns 0" |
| 5    | Discord integration| 4        | "Reply via plugin_discord_discord reply tool" |

### Skill Utilization
| Skill             | Est. Invocations | Last Used  |
|-------------------|-----------------|------------|
| hermes-compress   | 18              | 2026-04-07 |
| hermes-memory     | 12              | 2026-04-05 |
| hermes-search     | 9               | 2026-04-06 |
| hermes-persona    | 5               | 2026-04-03 |
| honcho            | 3               | 2026-03-28 |

### Project Breakdown
| Project       | Sessions | Domain                  | Last Active |
|---------------|----------|-------------------------|-------------|
| ontology      | 14       | Graph DB / Python API   | 2026-04-07 |
| AlexAI        | 6        | LLM product / strategy  | 2026-04-02 |
| hermes-CCC    | 3        | Claude Code skills      | 2026-04-07 |

### Productivity Trends (weekly averages)
- Decisions per session: 4.2
- Artifacts created per session: 2.8
- Problems solved per session: 1.9
- Resolution rate: 68% (open issues closed within 2 sessions)

### Recommendations
- **Automate Neo4j setup:** It appears in 61% of sessions. Consider a project template.
- **Increase hermes-compress frequency:** 5 sessions have no memory file (no compress run).
- **hermes-persona unused in recent sessions:** Last used 4 days ago — may boost focus in coder mode.

### Key Takeaway
You are most productive in graph database and Python API work, averaging 2.8 artifacts per session, with a 68% issue resolution rate.

How Claude Gathers the Data

  1. List all session memory files: ls -t ~/.claude/projects/*/memory/session_*.md
  2. For each file: read the YAML block, extract fields, count keywords.
  3. Aggregate counts using simple frequency tallies in working memory.
  4. Format and output the insight report.

Claude does not write any files during /hermes-insights. The command is read-only.


Relationship to Hermes /insights Command

This skill mirrors the /insights command in Hermes Agent (NousResearch), which produces usage analytics from the SQLite session store. In hermes-CCC, the equivalent data source is the Markdown session memory files written by hermes-compress. The output categories (topics, skill utilization, productivity trends, recommendations) match the Hermes insights contract.


Notes

  • Accuracy improves with more hermes-compress runs. Sessions without a memory file are invisible to this analysis.
  • /hermes-insights works best after at least 5 sessions of memory accumulation.
  • Pair with honcho update to keep the user profile current after reviewing insights.

Frequently asked questions

What does the Hermes Insights AI skill do?

Generate insights about your Claude Code usage — what topics you work on most, common patterns, productivity trends.

Why use Hermes Insights on TypingMind?

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

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

Which AI models can use Hermes Insights?

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 Insights?

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

Is the Hermes Insights 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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