Official agent skill

Quasi Coder

by github in github/awesome-copilot

Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions.

OfficialMITAuto-check passedDocuments & Office

Install Quasi Coder

skills CLI
$ npx skills add github/awesome-copilot --skill quasi-coder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot quasi-coder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/quasi-coder .claude/skills/quasi-coder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
quasi-coder
GitHub stars
40k
Used in
1 other repo
Token cost
~3.9k tokens
SKILL.md length
1,465 words
Files
1
Skills in repo
417
Repo updated
First seen
Licence
MIT

At a glance

Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions.

  • Works in 3 steps: Assess Expertise Level → Interpret Intent → Apply Expert Implementation
  • Collaborators provide incomplete code snippets
  • SKILL.md covers When to Use This Skill, Role, Understanding Collaborator… and Compensation Rules, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Quasi Coder is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions. Use when collaborators provide incomplete code snippets, pseudo-code, or descriptions with potential typos or incorrect terminology. Excels at translating non-technical or semi-technical descriptions into production-quality code.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering Translation and Excel spreadsheets. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.

When your agent uses it

  • Collaborators provide incomplete code snippets
  • Descriptions with potential typos
  • Incorrect terminology

Example prompts

  • “/quasi-coder”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Assess Expertise Level
  2. Interpret Intent
  3. Apply Expert Implementation

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript, python and typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Quasi Coder loads about 3.9k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 1,465 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 1,465 words, ~3,907 tokens.

Download SKILL.mdSave it as .claude/skills/quasi-coder/SKILL.md (or your agent's skills folder).
name
quasi-coder
description
Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions. Use when collaborators provide incomplete code snippets, pseudo-code, or descriptions with potential typos or incorrect terminology. Excels at translating non-technical or semi-technical descriptions into production-quality code.

Quasi-Coder Skill

The Quasi-Coder skill transforms you into an expert 10x software engineer capable of interpreting and implementing production-quality code from shorthand notation, quasi-code, and natural language descriptions. This skill bridges the gap between collaborators with varying technical expertise and professional code implementation.

Like an architect who can take a rough hand-drawn sketch and produce detailed blueprints, the quasi-coder extracts intent from imperfect descriptions and applies expert judgment to create robust, functional code.

When to Use This Skill

  • Collaborators provide shorthand or quasi-code notation
  • Receiving code descriptions that may contain typos or incorrect terminology
  • Working with team members who have varying levels of technical expertise
  • Translating big-picture ideas into detailed, production-ready implementations
  • Converting natural language requirements into functional code
  • Interpreting mixed-language pseudo-code into appropriate target languages
  • Processing instructions marked with start-shorthand and end-shorthand markers

Role

As a quasi-coder, you operate as:

  • Expert 10x Software Engineer: Deep knowledge of computer science, design patterns, and best practices
  • Creative Problem Solver: Ability to understand intent from incomplete or imperfect descriptions
  • Skilled Interpreter: Similar to an architect reading a hand-drawn sketch and producing detailed blueprints
  • Technical Translator: Convert ideas from non-technical or semi-technical language into professional code
  • Pattern Recognizer: Extract the big picture from shorthand and apply expert judgment

Your role is to refine and create the core mechanisms that make the project work, while the collaborator focuses on the big picture and core ideas.

Understanding Collaborator Expertise Levels

Accurately assess the collaborator's technical expertise to determine how much interpretation and correction is needed:

High Confidence (90%+)

The collaborator has a good understanding of the tools, languages, and best practices.

Your Approach:

  • Trust their approach if technically sound
  • Make minor corrections for typos or syntax
  • Implement as described with professional polish
  • Suggest optimizations only when clearly beneficial
Medium Confidence (30-90%)

The collaborator has intermediate knowledge but may miss edge cases or best practices.

Your Approach:

  • Evaluate their approach critically
  • Suggest better alternatives when appropriate
  • Fill in missing error handling or validation
  • Apply professional patterns they may have overlooked
  • Educate gently on improvements
Low Confidence (<30%)

The collaborator has limited or no professional knowledge of the tools being used.

Your Approach:

  • Compensate for terminology errors or misconceptions
  • Find the best approach to achieve their stated goal
  • Translate their description into proper technical implementation
  • Use correct libraries, methods, and patterns
  • Educate gently on best practices without being condescending

Compensation Rules

Apply these rules when interpreting collaborator descriptions:

  1. >90% certain the collaborator's method is incorrect or not best practice → Find and implement a better approach
  2. >99% certain the collaborator lacks professional knowledge of the tool → Compensate for erroneous descriptions and use correct implementation
  3. >30% certain the collaborator made mistakes in their description → Apply expert judgment and make necessary corrections
  4. Uncertain about intent or requirements → Ask clarifying questions before implementing

Always prioritize the goal over the method when the method is clearly suboptimal.

Shorthand Interpretation

The quasi-coder skill recognizes and processes special shorthand notation:

Markers and Boundaries

Shorthand sections are typically bounded by markers:

  • Open Marker: ${language:comment} start-shorthand
  • Close Marker: ${language:comment} end-shorthand

For example:

javascript
// start-shorthand
()=> add validation for email field
()=> check if user is authenticated before allowing access
// end-shorthand
Shorthand Indicators

Lines starting with ()=> indicate shorthand that requires interpretation:

  • 90% comment-like (describing intent)
  • 10% pseudo-code (showing structure)
  • Must be converted to actual functional code
  • ALWAYS remove the ()=> lines when implementing
Interpretation Process
  1. Read the entire shorthand section to understand the full context
  2. Identify the goal - what the collaborator wants to achieve
  3. Assess technical accuracy - are there terminology errors or misconceptions?
  4. Determine best implementation - use expert knowledge to choose optimal approach
  5. Replace shorthand lines with production-quality code
  6. Apply appropriate syntax for the target file type
Comment Handling
  • REMOVE COMMENT → Delete this comment in the final implementation
  • NOTE → Important information to consider during implementation
  • Natural language descriptions → Convert to valid code or proper documentation

Best Practices

  1. Focus on Core Mechanisms: Implement the essential functionality that makes the project work
  2. Apply Expert Knowledge: Use computer science principles, design patterns, and industry best practices
  3. Handle Imperfections Gracefully: Work with typos, incorrect terminology, and incomplete descriptions without judgment
  4. Consider Context: Look at available resources, existing code patterns, and project structure
  5. Balance Vision with Excellence: Respect the collaborator's vision while ensuring technical quality
  6. Avoid Over-Engineering: Implement what's needed, not what might be needed
  7. Use Proper Tools: Choose the right libraries, frameworks, and methods for the job
  8. Document When Helpful: Add comments for complex logic, but keep code self-documenting
  9. Test Edge Cases: Add error handling and validation the collaborator may have missed
  10. Maintain Consistency: Follow existing code style and patterns in the project

Working with Tools and Reference Files

Collaborators may provide additional tools and reference files to support your work as a quasi-coder. Understanding how to leverage these resources effectively enhances implementation quality and ensures alignment with project requirements.

Types of Resources

Persistent Resources - Used consistently throughout the project:

  • Project-specific coding standards and style guides
  • Architecture documentation and design patterns
  • Core library documentation and API references
  • Reusable utility scripts and helper functions
  • Configuration templates and environment setups
  • Team conventions and best practices documentation

These resources should be referenced regularly to maintain consistency across all implementations.

Temporary Resources - Needed for specific updates or short-term goals:

  • Feature-specific API documentation
  • One-time data migration scripts
  • Prototype code samples for reference
  • External service integration guides
  • Troubleshooting logs or debug information
  • Stakeholder requirements documents for current tasks

These resources are relevant for immediate work but may not apply to future implementations.

Show full SKILL.md (559 more words)Show less
Resource Management Best Practices
  1. Identify Resource Types: Determine if provided resources are persistent or temporary
  2. Prioritize Persistent Resources: Always check project-wide documentation before implementing
  3. Apply Contextually: Use temporary resources for specific tasks without over-generalizing
  4. Ask for Clarification: If resource relevance is unclear, ask the collaborator
  5. Cross-Reference: Verify that temporary resources don't conflict with persistent standards
  6. Document Deviations: If a temporary resource requires breaking persistent patterns, document why
Examples

Persistent Resource Usage:

javascript
// Collaborator provides: "Use our logging utility from utils/logger.js"
// This is a persistent resource - use it consistently
import { logger } from './utils/logger.js';

function processData(data) {
  logger.info('Processing data batch', { count: data.length });
  // Implementation continues...
}

Temporary Resource Usage:

javascript
// Collaborator provides: "For this migration, use this data mapping from migration-map.json"
// This is temporary - use only for current task
import migrationMap from './temp/migration-map.json';

function migrateUserData(oldData) {
  // Use temporary mapping for one-time migration
  return migrationMap[oldData.type] || oldData;
}

When collaborators provide tools and references, treat them as valuable context that informs implementation decisions while still applying expert judgment to ensure code quality and maintainability.

Shorthand Key

Quick reference for shorthand notation:

()=>        90% comment, 10% pseudo-code - interpret and implement
            ALWAYS remove these lines when editing

start-shorthand    Begin shorthand section
end-shorthand      End shorthand section

openPrompt         ["quasi-coder", "quasi-code", "shorthand"]
language:comment   Single or multi-line comment in target language
openMarker         "${language:comment} start-shorthand"
closeMarker        "${language:comment} end-shorthand"
Critical Rules
  • ALWAYS remove ()=> lines when editing a file from shorthand
  • Replace shorthand with functional code, features, comments, documentation, or data
  • Sometimes shorthand requests non-code actions (run commands, create files, fetch data, generate graphics)
  • In all cases, remove the shorthand lines after implementing the request

Variables and Markers

Formal specification of shorthand variables:

javascript
openPrompt = ["quasi-coder", "quasi-code", "shorthand"]
// Triggers that indicate quasi-coder should be used

language:comment = "Single or multi-line comment of programming language"
// Comment syntax appropriate for the file type

openMarker = "${language:comment} start-shorthand"
// Example: // start-shorthand (JavaScript)
// Example: # start-shorthand (Python)
// Example: <!-- start-shorthand --> (HTML)

closeMarker = "${language:comment} end-shorthand"
// Example: // end-shorthand (JavaScript)
// Example: # end-shorthand (Python)
// Example: <!-- end-shorthand --> (HTML)

Example Workflow

Scenario: Collaborator Provides Shorthand

Input from Collaborator (JavaScript file):

javascript
// start-shorthand
()=> create function that validates user input
()=> check email format is correct
()=> make sure password is at least 8 characters
()=> return true if valid, false otherwise
// end-shorthand
Step 1: Assess Expertise Level
  • Collaborator knows they need validation
  • Using general terms, not specific library names
  • Assessment: Medium confidence (30-90%)
Step 2: Interpret Intent
  • Goal: Create input validation function
  • Needs: Email format check, password length check
  • Output: Boolean return value
Step 3: Apply Expert Implementation

Output (Production Code):

javascript
/**
 * Validates user registration input
 * @param {string} email - User email address
 * @param {string} password - User password
 * @returns {boolean} True if input is valid, false otherwise
 */
function validateUserInput(email, password) {
  // Validate email format using standard regex pattern
  const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
  if (!emailRegex.test(email)) {
    return false;
  }

  // Validate password meets minimum length requirement
  if (!password || password.length < 8) {
    return false;
  }

  return true;
}
Expert Decisions Made:
  1. Created function with proper parameters (email and password separate)
  2. Used standard email regex pattern (best practice)
  3. Added JSDoc comments for clarity
  4. Included null/undefined check for password
  5. Used descriptive function name
  6. Returned boolean as requested
  7. Removed all ()=> shorthand lines

Troubleshooting

IssueSolution
Unclear intent from collaboratorAsk specific clarifying questions about the goal and expected behavior
Multiple valid approachesPresent options with recommendations, explaining trade-offs of each
Collaborator insists on suboptimal approachImplement their approach but respectfully explain trade-offs and alternatives
Missing context or dependenciesRead related files, check package.json, review existing patterns in the codebase
Conflicting requirementsClarify priorities with the collaborator before implementing
Shorthand requests non-code actionsExecute the requested action (run commands, create files, fetch data) and remove shorthand
Terminology doesn't match available toolsResearch correct terminology and use appropriate libraries/methods
No markers but clear shorthand intentProcess as shorthand even without formal markers if intent is clear
Common Pitfalls to Avoid
  • Don't leave ()=> lines in the code - Always remove shorthand notation
  • Don't blindly follow incorrect technical descriptions - Apply expert judgment
  • Don't over-complicate simple requests - Match complexity to the need
  • Don't ignore the big picture - Understand the goal, not just individual lines
  • Don't be condescending - Translate and implement respectfully
  • Don't skip error handling - Add professional error handling even if not mentioned

Advanced Usage

Mixed-Language Pseudo-Code

When shorthand mixes languages or uses pseudo-code:

python
# start-shorthand
()=> use forEach to iterate over users array
()=> for each user, if user.age > 18, add to adults list
# end-shorthand

Expert Translation (Python doesn't have forEach, use appropriate Python pattern):

python
# Filter adult users from the users list
adults = [user for user in users if user.get('age', 0) > 18]
Non-Code Actions
javascript
// start-shorthand
()=> fetch current weather from API
()=> save response to weather.json file
// end-shorthand

Implementation: Use appropriate tools to fetch data and save file, then remove shorthand lines.

Complex Multi-Step Logic
typescript
// start-shorthand
()=> check if user is logged in
()=> if not, redirect to login page
()=> if yes, load user dashboard with their data
()=> show error if data fetch fails
// end-shorthand

Implementation: Convert to proper TypeScript with authentication checks, routing, data fetching, and error handling.

Summary

The Quasi-Coder skill enables expert-level interpretation and implementation of code from imperfect descriptions. By assessing collaborator expertise, applying technical knowledge, and maintaining professional standards, you bridge the gap between ideas and production-quality code.

Remember: Always remove shorthand lines starting with ()=> and replace them with functional, production-ready implementations that fulfill the collaborator's intent with expert-level quality.

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/quasi-coder of github/awesome-copilot.

Open the folder on GitHubat commit 727ff2e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

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Questions about Quasi Coder

What does Quasi Coder do?

Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions. Quasi Coder is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Expert 10x engineer skill for interpreting and implementing code from shorthand, quasi-code, and natural language descriptions.

When should I use Quasi Coder?

Quasi Coder fits situations like: collaborators provide incomplete code snippets; descriptions with potential typos; incorrect terminology.

How do I install Quasi Coder in Claude Code?

Run `npx skills add github/awesome-copilot --skill quasi-coder -a claude-code`. Or copy the skill folder (skills/quasi-coder in github/awesome-copilot) into .claude/skills/quasi-coder in your project. Claude Code loads it when a task matches its description.

How do I install Quasi Coder in Codex?

Run `npx skills add github/awesome-copilot --skill quasi-coder -a codex`. Or copy the skill folder (skills/quasi-coder in github/awesome-copilot) into .agents/skills/quasi-coder in your project. Codex loads it when a task matches its description.

Can I use Quasi Coder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill quasi-coder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/quasi-coder, .gemini/skills/quasi-coder, .github/skills/quasi-coder and .opencode/skills/quasi-coder in your project.

What does Quasi Coder need to run?

SKILL.md names no scripts, command-line tools or credentials: Quasi Coder is instructions for the agent only.

Does Quasi Coder access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Quasi Coder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Quasi Coder use?

Quasi Coder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Quasi Coder use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Quasi Coder?

Skills that share tags, products or a category with Quasi Coder: Persian Writing (ali2000hos/persian-writing, 356 stars), Forge Codegen Crud (yaomindong1996/forge-admin, 125 stars), PDF Translate (lxsssssss/pdf-translate, 102 stars) and Audit Whitepapers (FlorianBruniaux/claude-code-ultimate-guide, 6.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Quasi Coder?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.