Trellis Check

OrganizationPopular

fy-agent

trellis-check

Comprehensive quality verification: spec compliance, lint, type-check, tests, cross-layer data flow, code reuse, and consistency checks. Use when code is written and needs quality verification, before committing changes, or to catch context drift during long sessions.

Overview

Publisher	fy-agent
Repository	`fyagent`
Skill name	`trellis-check`
Stars	1.7K
Forks	139
Bundled files	Instructions only
Links	Skill source Repository Homepage

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 fy-agent on GitHub. Read the source before you install it.

Installation

Install the Trellis Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/fy-agent/fyagent/tree/main/.agents/skills/trellis-check
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.

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/fy-agent/fyagent.git /tmp/fyagent
mkdir -p .claude/skills
cp -r /tmp/fyagent/.agents/skills/trellis-check .claude/skills/trellis-check

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trellis Check 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 Trellis Check 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 Trellis Check 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.

Code Quality Check

Comprehensive quality verification for recently written code. Combines spec compliance, cross-layer safety, and pre-commit checks.

Step 1: Identify What Changed

bash
git diff --name-only HEAD
git status

Step 2: Read Task Artifacts and Applicable Specs

Read the current task artifacts in order:

prd.md
design.md if present
implement.md if present

bash
python ./.trellis/scripts/get_context.py --mode packages

For each changed package/layer, read the spec index and follow its Quality Check section:

bash
cat .trellis/spec/<package>/<layer>/index.md

Read the specific guideline files referenced — the index is a pointer, not the goal.

Step 3: Run Project Checks

Run the project's lint, type-check, and test commands. Fix any failures before proceeding.

Step 4: Review Against Checklist

Code Quality

Test Coverage

New function → unit test added?
Bug fix → regression test added?
Changed behavior → existing tests updated?

Spec Sync

Does .trellis/spec/ need updates? (new patterns, conventions, lessons learned)

"If I fixed a bug or discovered something non-obvious, should I document it so future me won't hit the same issue?" → If YES, update the relevant spec doc.

Scope Discipline

Any tidying of code the task did not require?
Any abstraction, config or extension point added for a case that does not exist yet?
Any speculative fallback for a state that cannot occur?
Any file changed that the acceptance criteria do not mention?
Any workaround added at the caller instead of a fix where the behavior actually lives?

Step 5: Cross-Layer Dimensions (if applicable)

Skip this step if your change is confined to a single layer.

A. Data Flow (changes touch 3+ layers)

Read flow traces correctly: Storage → Service → API → UI
Write flow traces correctly: UI → API → Service → Storage
Types/schemas correctly passed between layers?
Errors properly propagated to caller?

B. Code Reuse (modifying constants, creating utilities)

Searched for existing similar code before creating new?

bash
grep -r "pattern" src/

If the same value repeats, does it represent one stable concept whose callers must change together? Extract only then — two literals that merely happen to match today should stay separate.
After batch modification, all occurrences updated?

C. Import/Dependency (creating new files)

Correct import paths (relative vs absolute)?
No circular dependencies?

D. Same-Layer Consistency

Other places using the same concept are consistent?

Step 6: Report and Fix

Report every violation you find. Then:

Mechanical and local (lint nit, missing type, wrong import, dead branch, failing assertion) → fix in place, then re-run project checks.
Design or judgment (naming a shared concept, moving a module boundary, changing a public interface, reassigning where behavior lives) → record the evidence and your recommendation, and stop. Do not rewrite it silently.

If a fix would touch files outside the current task's scope, say so and stop instead of widening the change.

Frequently asked questions

What does the Trellis Check AI skill do?

Why use Trellis Check on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fy-agent/fyagent/tree/main/.agents/skills/trellis-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Trellis Check?

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 Trellis Check?

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

Is the Trellis Check AI skill free?

It is published on GitHub by fy-agent. Check the repository for licensing terms. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Code Quality Check

Step 1: Identify What Changed

Step 2: Read Task Artifacts and Applicable Specs

Step 3: Run Project Checks

Step 4: Review Against Checklist

Code Quality

Test Coverage

Spec Sync

Scope Discipline

Step 5: Cross-Layer Dimensions (if applicable)

A. Data Flow (changes touch 3+ layers)

B. Code Reuse (modifying constants, creating utilities)

C. Import/Dependency (creating new files)

D. Same-Layer Consistency

Step 6: Report and Fix

Frequently asked questions

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