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Mentor

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smixs
mentor

Generate an /insights-style HTML report across MANY of your agent coding sessions (Claude Code and OpenAI Codex): what you work on, how you use the agent, your wins, your friction points, and concrete fixes to become more effective — precise goal-setting, not trusting output blindly, scoping, context hygiene, right-sizing models. Reads local session history and writes one self-contained report.html. Use when the user asks to "разбери мои сессии", "оцени как я работаю с агентом", "мой insights", "session insights report", "review how I work", "на что обратить внимание", or runs "/mentor", "/mentor claude", "/mentor codex", "/mentor both". Do NOT use for reviewing the CODE produced (that's code-review) or building the agent.

Overview

Publishersmixs
Repositorymentor
Skill namementor
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Bundled files
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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.

  • 5 bundled files

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

  • Open source

    Published by smixs on GitHub. Read the source before you install it.

Installation

Install the Mentor 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/smixs/mentor/tree/main/mentor
  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/smixs/mentor.git /tmp/mentor
mkdir -p .claude/skills
cp -r /tmp/mentor/mentor .claude/skills/mentor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mentor 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 Mentor 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 Mentor 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.

Mentor — /insights-style report on how you work with agents

Why this exists

Agents now do what juniors used to. But juniors became seniors by getting feedback from seniors. Give agents to juniors and fire the seniors, and nobody gets that feedback loop anymore. This skill is that loop: it reads your own local session history and hands back a single report — what you build, where you lose time, and concrete changes to get better. Effectiveness, not token-golf.

The deliverable is the rendered report.html file — always. The eight-section analysis is intermediate: it goes into analysis.json and gets rendered, never pasted into chat. A wall of analysis text in the reply is the failure this skill exists to fix. If you cannot run the scripts, say so plainly — do not substitute a text summary. Modeled on Claude Code's built-in /insights.

Agents

AgentargLogs
Claude Codeclaude~/.claude/projects/<slug>/<uuid>.jsonl
OpenAI Codexcodex~/.codex/sessions/**/rollout-*.jsonl (+ ~/.codex/archived_sessions/)
bothboth (default)union of the two

Workflow — build the report

  1. Collect every session in the window into one stats bundle (deterministic):

    bash
    uv run --no-project python ~/.claude/skills/mentor/scripts/collect.py \
      --agent <claude|codex|both> --days <N> --out /tmp/mentor-stats.json

    Default --agent both --days 30. It writes stats.json and prints a compact digest: headline numbers, tool mix, languages, projects, and every session's verbatim first prompt. Read that digest — it is your evidence.

  2. Analyze into eight sections. Read references/best-practices.md for the canon, then write /tmp/mentor-analysis.json (schema in scripts/render_report.py header). Ground every claim in the digest (numbers, project names, prompt patterns). The eight sections, mirroring /insights:

    • what_you_work_on — sessions clustered into themes, with rough share.
    • how_you_use — patterns: tool ratios (Bash/exec vs Read), languages, both-agent split, checkpoints.
    • impressive — real wins, handed back.
    • friction — where time leaks. Each: problem, evidence, concrete fix. This section earns the report.
    • features_to_try — existing features they under-use.
    • claude_md_additions — copy-paste CLAUDE.md lines that kill a recurring friction.
    • new_ways — new ways to use the agent(s).
    • on_the_horizon — where the workflow is heading. Also write a summary that does not flatter — name what is hindering them.
  3. Render the self-contained HTML:

    bash
    uv run --no-project python ~/.claude/skills/mentor/scripts/render_report.py \
      --stats /tmp/mentor-stats.json --analysis /tmp/mentor-analysis.json \
      --out ~/.claude/usage-data/mentor-report.html
  4. Deliver the file. Open or hand over ~/.claude/usage-data/mentor-report.html: in Claude Code use SendUserFile with display render; in Codex or a shell, open it (open <path>) and print the path. Then add at most two spoken lines: the single highest-leverage fix. The report carries the detail; the chat does not repeat it.

Quick single-session mode (only on explicit request)

Only when the user explicitly points at ONE session ("разбери вот эту сессию"), skip collect/render and run the per-session digest, then give a short text read (TL;DR, what worked, what dragged down, one habit). Any "разбери мои сессии" / "/mentor" goes through the report pipeline above, not this.

bash
uv run --no-project python ~/.claude/skills/mentor/scripts/digest.py <path|last> --agent <claude|codex>

Rules

  • The report.html is the answer. Never paste the eight sections as a chat message.
  • Analyze the process, not the code produced. Don't re-review what was built.
  • Evidence or it didn't happen — every section ties to the stats digest (numbers, projects, prompts). No generic advice.
  • Codex routes almost everything through one exec tool, so exec-heavy is normal there — judge read-before-edit, not tool name.
  • Some judgments are model-inferred (satisfaction, themes) — the report footer already flags this; don't overclaim.
  • Write the report content in the user's language, defaulting to English. Set the analysis lang field ("en" | "ru") so the static chrome matches. Keep prose clean, concrete, and free of AI-slop; for Russian, hold it to the humanizer-ru standard.
  • The friction section is the point. Each friction gets a concrete, copy-pasteable fix (a CLAUDE.md line, a hook, a habit).

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 Mentor AI skill do?

Generate an /insights-style HTML report across MANY of your agent coding sessions (Claude Code and OpenAI Codex): what you work on, how you use the agent, your wins, your friction points, and concrete fixes to become more effective — precise goal-setting, not trusting output blindly, scoping, context hygiene, right-sizing models. Reads local session history and writes one self-contained report.html. Use when the user asks to "разбери мои сессии", "оцени как я работаю с агентом", "мой insights", "session insights report", "review how I work", "на что обратить внимание", or runs "/mentor", "/...

Why use Mentor on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/smixs/mentor/tree/main/mentor. 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 Mentor?

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 Mentor?

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

Is the Mentor AI skill free?

Yes. It is published on GitHub by smixs 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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