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Trellis Update Spec

OrganizationPopular
fy-agent
trellis-update-spec

Captures executable contracts and coding conventions into .trellis/spec/ documents. Use when learning something valuable from debugging, implementing, or discussion that should be preserved for future sessions.

Overview

Publisherfy-agent
Repositoryfyagent
Skill nametrellis-update-spec
Stars
1.7K
Forks
139
Bundled files
Instructions only
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 fy-agent on GitHub. Read the source before you install it.

Installation

Install the Trellis Update Spec 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/fy-agent/fyagent/tree/main/.agents/skills/trellis-update-spec
  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/fy-agent/fyagent.git /tmp/fyagent
mkdir -p .claude/skills
cp -r /tmp/fyagent/.agents/skills/trellis-update-spec .claude/skills/trellis-update-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trellis Update Spec 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 Update Spec 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 Update Spec 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.

Update Code-Spec - Capture Executable Contracts

When you learn something valuable (from debugging, implementing, or discussion), use this to update the relevant code-spec documents.

Timing: After completing a task, fixing a bug, or discovering a new pattern


Code-Spec First Rule (CRITICAL)

In this project, "spec" for implementation work means code-spec:

  • Executable contracts (not principle-only text)
  • Concrete signatures, payload fields, env keys, and boundary behavior
  • Testable validation/error behavior

If the change touches infra or cross-layer contracts, code-spec depth is mandatory.

Mandatory Triggers

Apply code-spec depth when the change includes any of:

  • New/changed command or API signature
  • Cross-layer request/response contract change
  • Database schema/migration change
  • Infra integration (storage, queue, cache, secrets, env wiring)

Mandatory Output (7 Sections)

For triggered tasks, include all sections below:

  1. Scope / Trigger
  2. Signatures (command/API/DB)
  3. Contracts (request/response/env)
  4. Validation & Error Matrix
  5. Good/Base/Bad Cases
  6. Tests Required (with assertion points)
  7. Wrong vs Correct (at least one pair)

When to Update Code-Specs

TriggerExampleTarget Spec
Implemented a featureAdded a new integration or moduleRelevant spec file
Made a design decisionChose extensibility pattern over simplicityRelevant spec + "Design Decisions" section
Fixed a bugFound a subtle issue with error handlingRelevant spec (e.g., error-handling docs)
Discovered a patternFound a better way to structure codeRelevant spec file
Hit a gotchaLearned that X must be done before YRelevant spec + "Common Mistakes" section
Established a conventionTeam agreed on naming patternQuality guidelines
New thinking trigger"Don't forget to check X before doing Y"guides/*.md (as a checklist item)

Key Insight: Code-spec updates are NOT just for problems. Every feature implementation contains design decisions and contracts that future AI/developers need to execute safely.


Spec Structure Overview

.trellis/spec/
├── <layer>/           # Per-layer coding standards (e.g., backend/, frontend/, api/)
│   ├── index.md       # Overview and links
│   └── *.md           # Topic-specific guidelines
└── guides/            # Thinking checklists (NOT coding specs!)
    ├── index.md       # Guide index
    └── *.md           # Topic-specific guides

CRITICAL: Code-Spec vs Guide - Know the Difference

TypeLocationPurposeContent Style
Code-Spec<layer>/*.mdTell AI "how to implement safely"Signatures, contracts, matrices, cases, test points
Guideguides/*.mdHelp AI "what to think about"Checklists, questions, pointers to specs

Decision Rule: Ask yourself:

  • "This is how to write the code" → Put in a spec layer directory
  • "This is what to consider before writing" → Put in guides/

Example:

LearningWrong LocationCorrect Location
"Use API X not API Y for this task"❌ guides/ (too specific for a thinking guide)✅ Relevant spec file (concrete convention)
"Remember to check X when doing Y"❌ Spec file (too abstract for a spec)✅ guides/ (thinking checklist)

Guides should be short checklists that point to specs, not duplicate the detailed rules.


Update Process

Step 1: Identify What You Learned

Answer these questions:

  1. What did you learn? (Be specific)
  2. Why is it important? (What problem does it prevent?)
  3. Where does it belong? (Which spec file?)

Step 2: Classify the Update Type

TypeDescriptionAction
Design DecisionWhy we chose approach X over YAdd to "Design Decisions" section
Project ConventionHow we do X in this projectAdd to relevant section with examples
New PatternA reusable approach discoveredAdd to "Patterns" section
Forbidden PatternSomething that causes problemsAdd to "Anti-patterns" or "Don't" section
Common MistakeEasy-to-make errorAdd to "Common Mistakes" section
ConventionAgreed-upon standardAdd to relevant section
GotchaNon-obvious behaviorAdd warning callout

Step 3: Read the Target Code-Spec

Before editing, read the current code-spec to:

  • Understand existing structure
  • Avoid duplicating content
  • Find the right section for your update
bash
cat .trellis/spec/<category>/<file>.md

Step 4: Make the Update

Follow these principles:

  1. Be Specific: Include concrete examples, not just abstract rules
  2. Explain Why: State the problem this prevents
  3. Show Contracts: Add signatures, payload fields, and error behavior
  4. Show Code: Add code snippets for key patterns
  5. Keep it Short: One concept per section

Step 5: Update the Index (if needed)

If you added a new section or the code-spec status changed, update the category's index.md.


Update Templates

Mandatory Template for Infra/Cross-Layer Work

markdown
## Scenario: <name>

### 1. Scope / Trigger
- Trigger: <why this requires code-spec depth>

### 2. Signatures
- Backend command/API/DB signature(s)

### 3. Contracts
- Request fields (name, type, constraints)
- Response fields (name, type, constraints)
- Environment keys (required/optional)

### 4. Validation & Error Matrix
- <condition> -> <error>

### 5. Good/Base/Bad Cases
- Good: ...
- Base: ...
- Bad: ...

### 6. Tests Required
- Unit/Integration/E2E with assertion points

### 7. Wrong vs Correct
#### Wrong
...
#### Correct
...

Adding a Design Decision

markdown
### Design Decision: [Decision Name]

**Context**: What problem were we solving?

**Options Considered**:
1. Option A - brief description
2. Option B - brief description

**Decision**: We chose Option X because...

**Example**:
\`\`\`typescript
// How it's implemented
code example
\`\`\`

**Extensibility**: How to extend this in the future...

Adding a Project Convention

markdown
### Convention: [Convention Name]

**What**: Brief description of the convention.

**Why**: Why we do it this way in this project.

**Example**:
\`\`\`typescript
// How to follow this convention
code example
\`\`\`

**Related**: Links to related conventions or specs.

Adding a New Pattern

markdown
### Pattern Name

**Problem**: What problem does this solve?

**Solution**: Brief description of the approach.

**Example**:
\`\`\`
// Good
code example

// Bad
code example
\`\`\`

**Why**: Explanation of why this works better.

Adding a Forbidden Pattern

markdown
### Don't: Pattern Name

**Problem**:
\`\`\`
// Don't do this
bad code example
\`\`\`

**Why it's bad**: Explanation of the issue.

**Instead**:
\`\`\`
// Do this instead
good code example
\`\`\`

Adding a Common Mistake

markdown
### Common Mistake: Description

**Symptom**: What goes wrong

**Cause**: Why this happens

**Fix**: How to correct it

**Prevention**: How to avoid it in the future

Adding a Gotcha

markdown
> **Warning**: Brief description of the non-obvious behavior.
>
> Details about when this happens and how to handle it.

Interactive Mode

If you're unsure what to update, answer these prompts:

  1. What did you just finish?

    • Fixed a bug
    • Implemented a feature
    • Refactored code
    • Had a discussion about approach
  2. What did you learn or decide?

    • Design decision (why X over Y)
    • Project convention (how we do X)
    • Non-obvious behavior (gotcha)
    • Better approach (pattern)
  3. Would future AI/developers need to know this?

    • To understand how the code works → Yes, update spec
    • To maintain or extend the feature → Yes, update spec
    • To avoid repeating mistakes → Yes, update spec
    • Purely one-off implementation detail → Maybe skip
  4. Which area does it relate to?

    • Backend code
    • Frontend code
    • Cross-layer data flow
    • Code organization/reuse
    • Quality/testing

Quality Checklist

Before finishing your code-spec update:

  • Is the content specific and actionable?
  • Did you include a code example?
  • Did you explain WHY, not just WHAT?
  • Did you include executable signatures/contracts?
  • Did you include validation and error matrix?
  • Did you include Good/Base/Bad cases?
  • Did you include required tests with assertion points?
  • Is it in the right code-spec file?
  • Does it duplicate existing content?
  • Would a new team member understand it?

Relationship to Other Commands

Development Flow:
  Learn something → `update-spec` (Trellis command) → Knowledge captured
       ↑                                  ↓
  `break-loop` (Trellis command) ←──────────────────── Future sessions benefit
  (deep bug analysis)
  • ``break-loop (Trellis command) - Analyzes bugs deeply, often reveals spec updates needed
  • ``update-spec (Trellis command) - Actually makes the updates
  • ``finish-work (Trellis command) - Reminds you to check if specs need updates

Core Philosophy

Code-specs are living documents. Every debugging session, every "aha moment" is an opportunity to make the implementation contract clearer.

The goal is institutional memory:

  • What one person learns, everyone benefits from
  • What AI learns in one session, persists to future sessions
  • Mistakes become documented guardrails

Frequently asked questions

What does the Trellis Update Spec AI skill do?

Captures executable contracts and coding conventions into .trellis/spec/ documents. Use when learning something valuable from debugging, implementing, or discussion that should be preserved for future sessions.

Why use Trellis Update Spec on TypingMind?

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

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

Which AI models can use Trellis Update Spec?

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 Update Spec?

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

Is the Trellis Update Spec 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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