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Code Completion Semantic Constraints

Community
ArabelaTso
code-completion-semantic-constraints

Automatically complete partial code snippets while satisfying semantic constraints including variable types, invariants, pre/post-conditions, interface contracts, and expected input/output behavior. Use when users provide incomplete code with specific requirements like "complete this function that takes a list and returns sorted unique elements" or "fill in this method body that must maintain the invariant that x stays positive" or "implement this interface method with these type constraints." Produces compilable, executable code with tests and a constraint satisfaction report.

Overview

PublisherArabelaTso
RepositorySkills-4-SE
Skill namecode-completion-semantic-constraints
Stars
258
Forks
25
Bundled files
Instructions only
LicenseApache-2.0
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 ArabelaTso on GitHub. Read the source before you install it.

Installation

Install the Code Completion Semantic Constraints 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/ArabelaTso/Skills-4-SE/tree/main/skills/code-completion-semantic-constraints
  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/ArabelaTso/Skills-4-SE.git /tmp/Skills-4-SE
mkdir -p .claude/skills
cp -r /tmp/Skills-4-SE/skills/code-completion-semantic-constraints .claude/skills/code-completion-semantic-constraints
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Completion Semantic Constraints 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 Code Completion Semantic Constraints 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 Code Completion Semantic Constraints 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 Completion with Semantic Constraints

Overview

Complete partial code snippets while satisfying specified semantic constraints. Produces compilable code, verification tests, and a detailed report explaining how each constraint was satisfied.

Workflow

1. Parse Input

Extract and categorize the provided information:

Partial Code: Identify the incomplete code structure (function signature, class skeleton, method stub, etc.)

Semantic Constraints: Categorize constraints by type:

  • Type constraints: Variable types, return types, generic bounds
  • Invariants: Pre-conditions, post-conditions, loop invariants
  • Behavioral constraints: Expected input/output pairs, edge case handling
  • Interface contracts: Method signatures, protocol conformance
  • Performance constraints: Time/space complexity requirements

2. Analyze Constraints

For each constraint:

  • Determine if it's satisfiable
  • Identify dependencies between constraints
  • Note any conflicts or ambiguities
  • If constraints are unclear or conflicting, ask for clarification before proceeding

3. Complete the Code

Generate code that:

  • Compiles without errors in the target language
  • Satisfies all specified constraints
  • Follows language idioms and best practices
  • Includes necessary imports, type annotations, and error handling
  • Uses minimal complexity (avoid over-engineering)

4. Generate Verification Tests

Create minimal test cases that verify:

  • Type constraints are respected
  • Pre-conditions and post-conditions hold
  • Expected input/output behavior is correct
  • Edge cases are handled properly
  • Performance constraints are met (if specified)

5. Produce Constraint Satisfaction Report

Document how each constraint was satisfied:

  • Map each constraint to the code that satisfies it
  • Explain the reasoning for implementation choices
  • Note any assumptions made
  • Highlight any constraints that required trade-offs

Examples

Example 1: Type and Behavioral Constraints

Input:

python
def process_items(items):
    # TODO: complete this function
    pass

Constraints:

  • items is a list of integers
  • Return a list of unique integers in ascending order
  • Handle empty list (return empty list)
  • Time complexity: O(n log n)

Output: Completed function with sorting logic, tests for empty/normal/duplicate cases, and report explaining constraint satisfaction.

Example 2: Interface Contract

Input:

java
public class DataCache implements Cache {
    // TODO: implement required methods
}

Constraints:

  • Implement Cache interface (get, put, remove methods)
  • Thread-safe operations
  • LRU eviction policy with max size 100
  • Return null for missing keys

Output: Complete class implementation, concurrency tests, and report mapping each interface method to implementation.

Example 3: Invariant Preservation

Input:

c
void update_balance(Account* acc, int amount) {
    // TODO: complete
}

Constraints:

  • Pre-condition: acc != NULL && acc->balance >= 0
  • Post-condition: acc->balance >= 0 (balance never negative)
  • If amount would make balance negative, set to 0 instead

Output: Implementation with bounds checking, tests for edge cases, and report showing invariant preservation.

Output Format

Provide three components:

1. Completed Code

  • Fully compilable and executable
  • Includes necessary imports, type annotations, error handling
  • Follows language conventions

2. Verification Tests

  • Minimal test suite covering constraint verification
  • Include edge cases and boundary conditions
  • Use appropriate testing framework for the language

3. Constraint Satisfaction Report

  • Table or list mapping each constraint to implementation details
  • Explanation of design choices
  • Any assumptions or trade-offs made

Language Support

This skill works with any programming language. Adapt constraint types and verification approaches to language-specific features (e.g., type systems, contract programming, assertion libraries).

Frequently asked questions

What does the Code Completion Semantic Constraints AI skill do?

Automatically complete partial code snippets while satisfying semantic constraints including variable types, invariants, pre/post-conditions, interface contracts, and expected input/output behavior. Use when users provide incomplete code with specific requirements like "complete this function that takes a list and returns sorted unique elements" or "fill in this method body that must maintain the invariant that x stays positive" or "implement this interface method with these type constraints." Produces compilable, executable code with tests and a constraint satisfaction report.

Why use Code Completion Semantic Constraints on TypingMind?

Because you install it once and use it with any model. Code Completion Semantic Constraints 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 Code Completion Semantic Constraints in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/code-completion-semantic-constraints. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Code Completion Semantic Constraints?

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 Code Completion Semantic Constraints?

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

Is the Code Completion Semantic Constraints AI skill free?

Yes. It is published on GitHub by ArabelaTso under the Apache-2.0 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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