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Sem

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tomsej
sem

Entity-aware code change analysis using the pi-sem tools. Use when the user asks what changed, wants blast radius or affected tests, needs focused context for a function/class, wants review help on a commit/branch/PR, or asks to compare semantic diff with raw git diff. Prefer sem_context and sem_impact; use sem_diff selectively for summaries and reviews.

Overview

Publishertomsej
Repositorypi-ext
Skill namesem
Stars
80
Forks
8
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 tomsej on GitHub. Read the source before you install it.

Installation

Install the Sem 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/tomsej/pi-ext/tree/main/skills/sem
  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/tomsej/pi-ext.git /tmp/pi-ext
mkdir -p .claude/skills
cp -r /tmp/pi-ext/skills/sem .claude/skills/sem
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sem 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 Sem 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 Sem 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.

sem

Use the pi-sem tools as a semantic lens, not as a universal replacement for raw git diff.

Default decision tree

Choose the smallest useful tool first:

  1. Focused understanding of one entity → sem_context

    • Best for a single function, method, class, block, or config section
    • Prefer this before reading a whole large file
  2. Blast radius / affected tests / hidden dependents → sem_impact

    • Use when reasoning about what could break
    • Prefer scope=tests for test selection
    • Prefer scope=all when validating broader impact
  3. Structural inventory of a file → sem_entities

    • Use before drilling into a suspicious file
    • Good for large files and mixed code/config files
  4. What changed across a commit/range/working tree → sem_diff

    • Use for semantic summaries, entity counts, and review overviews
    • Do not default to it when you only need exact patch details or a single entity
  5. History / ownership of an entity → sem_log, sem_blame

    • Use for regressions, archaeology, and ownership questions

Review workflow

For commit / branch / PR review:

  1. Run sem_diff once for a semantic overview
  2. Pick the riskiest changed entities
  3. Run sem_impact on those entities
  4. Run sem_context on the suspicious ones you need to understand deeply
  5. Confirm final findings with raw git diff, read, or direct file inspection before citing line numbers

For snapshot / folder review:

  1. Start with sem_entities
  2. Use sem_context on the most relevant entities
  3. Use sem_impact only after you identify something suspicious

Important caveats

  • sem diff --format json is not always smaller than raw git diff
  • sem may under-cover tests, assets, generated files, or non-semantic glue code
  • Do not cite sem output alone as final evidence for line-level review comments
  • If semantic coverage looks incomplete, fall back to raw git diff, read, grep, and file inspection

Good prompts / tool choices

  • “What changed in this commit?” → sem_diff
  • “What tests are affected by this function?” → sem_impact with scope=tests
  • “Give me focused context for this class without reading the whole file.” → sem_context
  • “What is in this Terraform file?” → sem_entities
  • “How did this function evolve?” → sem_log

Anti-patterns

Avoid these habits:

  • Running sem_diff repeatedly when sem_context would answer the question faster
  • Using only raw git diff for entity counts or blast radius questions
  • Treating sem_impact as infallible; verify surprising results with file reads and grep
  • Writing review findings from semantic summaries without checking the actual changed code

Frequently asked questions

What does the Sem AI skill do?

Entity-aware code change analysis using the pi-sem tools. Use when the user asks what changed, wants blast radius or affected tests, needs focused context for a function/class, wants review help on a commit/branch/PR, or asks to compare semantic diff with raw git diff. Prefer sem_context and sem_impact; use sem_diff selectively for summaries and reviews.

Why use Sem on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tomsej/pi-ext/tree/main/skills/sem. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Sem?

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 Sem?

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

Is the Sem AI skill free?

Yes. It is published on GitHub by tomsej 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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