Skillget

Praxis

agent
Guvenlik Denetimi
Uyari
Health Uyari
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 6 days ago
  • Low visibility — Only 5 GitHub stars
Code Gecti
  • Code scan — Scanned 3 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This is a multi-agent pipeline designed to help users build a permanent career knowledge base and generate highly tailored, ATS-friendly resumes using adversarial AI agents.

Security Assessment
Overall Risk: Low. The tool processes highly sensitive personal data, such as career history, PDFs, and LinkedIn exports, but stores this information locally on your machine. The light code scan found no dangerous patterns, hardcoded secrets, or dangerous permission requests. There are no signs of unauthorized network requests, malicious shell execution, or data exfiltration. However, as with any tool processing your personal data, you should verify exactly how your preferred LLM API provider handles the prompts and text sent to it.

Quality Assessment
Quality is decent but held back by its novelty. The project is actively maintained, with its last push occurring just 6 days ago. It is properly licensed under the standard MIT license and has a very thorough, professional README. The main drawback is extremely low community visibility; it currently only has 5 GitHub stars. This means the codebase has not been extensively peer-reviewed by the broader developer community.

Verdict
Safe to use, but keep in mind that the project is very new and has not yet been widely validated by a large audience.
SUMMARY

Adversarial, Multi-Agent Career Knowledge Base & Resume Pipeline

README.md
Praxis - AI Multi-Agent Resume Builder and Career Knowledge Base

Praxis: AI-Powered Resume Builder & Multi-Agent Career Knowledge Base

Defeating ATS bots, AI hallucinations, and the blank-page problem through rigorous orchestration.

🧠 The Blank Page Problem is Dead.

👉 Read the full Getting Started Guide here!

Using a single-shot prompt to ask an LLM to "write my resume" results in three catastrophic failures: prompt to ask an LLM to "write my resume" results in three catastrophic failures:

  1. Summarization Loss: LLMs inherently compress facts, stripping away the exact metrics, technologies, and scale that actually get you hired.
  2. Sycophancy & Hallucination: AI invents "synergistic paradigms" and hallucinates responsibilities to make you sound good, causing you to fail rigorous technical interviews.
  3. Context Collapse: When recruiters call back a month later, you have no idea what resume you sent them or what the job description even was.

Praxis isn't just another AI resume writer. It is a localized, multi-agent pipeline designed to solve these exact failures. It builds a permanent, lossless Career Knowledge Base and deploys adversarial AI agents to meticulously tailor your history to specific roles, generate interview prep sheets, and organize your job hunt perfectly.

✨ Core Capabilities

1. 🗄️ Permanent, Lossless Career Knowledge Base

Instead of summarizing your history into Markdown, Praxis iteratively ingests raw data (PDFs, GitHub exports, LinkedIn CSVs) into a strict, loss-proof relational database (.praxis/data/knowledge_base.json). It maps every tool, skill, and metric to the exact project where it was used, ensuring you never lose the hard numbers that prove your impact.

2. 🔄 The Self-Enriching Flywheel (Skill Gap Interview)

Every time you apply for a job, your Knowledge Base gets stronger. If Praxis detects a required skill in a job description that isn't in your database, it pauses and asks you: "Did you use [Skill] at a previous company? How?"
You reply with a rough brain-dump. Praxis hands your raw text to the adversarial agent panel to format into a perfect STAR-method bullet in your exact tone of voice, and then permanently injects that new bullet and skill into your Knowledge Base. Over time, your career database continually grows deeper, richer, and more detailed with zero extra effort on your part.

3. ⚖️ Adversarial AI Agents (Pathos & Logos)

When tailoring an ATS-friendly resume for a specific job description, Praxis employs a rigorous two-agent adversarial loop:

  • praxis-pathos (The Visionary & Coach): Drafts the resume using your saved Voice Profile and the STAR method, focusing on compelling narrative and impact.
  • praxis-logos (The Truth-Teller): Acts as a brutal auditor, rejecting any bullet point that hallucinates facts or uses AI-speak not explicitly backed by your Knowledge Base. They iterate until a mathematically honest, perfectly targeted document is produced.

4. 🎯 Hyper-Targeted Markdown to PDF Resumes

Say goodbye to generic applications. Simply provide a job description URL (/praxis <job-url>), and Praxis will run a precision Skill Gap Analysis. It strategically selects the most relevant facts from your history to generate a highly targeted, ATS-optimized PDF designed specifically to beat the bots for that exact role.

5. 🎤 Automated Interview Prep Sheets

Beyond just getting the interview, Praxis helps you pass it. For every targeted resume generated, Praxis builds a comprehensive Interview Guideline & Prep Sheet. This document explicitly maps your past experience and metrics directly to the requirements in the job description, serving as a rapid orientation brief when the recruiter calls.

6. 📂 Context-Preserving Organization

"Which version of my resume did I send to AcmeCorp again?"
Praxis automatically organizes your generated resumes, tailored cover letters, and Interview Prep Sheets into dedicated company folders (e.g., assets/AcmeCorp/). You can instantly pull up the folder to see exactly what the job description was, what resume you sent, and the mapped talking points.

🏗️ Architecture & Commands

Praxis installs directly into your local AI CLI environment (e.g., opencode, Claude Code, GitHub Copilot) as a skill.

The Intake Engine: /praxis

Runs a deterministic Deep Harvest extraction across your root directory for raw exports, parsing data into fact pools and pushing it into your Knowledge Base.

The Knowledge Updater: /praxis <text>

Quickly appends specific accomplishments, metrics, or corrections using natural language without requiring a full CV re-upload (e.g., /praxis at ACME co., I managed a team of 50).

The Baseline Generator: /praxis resume

Explicitly regenerates your general baseline resume (assets/Resume.md) directly from your knowledge base data.

The Forge: /praxis <job-url>

Executes the Skill Gap Analysis and the Pathos/Logos adversarial loop. Generates the targeted PDF, the Interview Prep Sheet, and organizes them perfectly into the target company's folder.

graph LR
    A[Raw Career Data] --> B[(Local Knowledge Base)]
    B --> C[praxis-pathos: Drafter]
    C <--> D[praxis-logos: Auditor]
    D --> E[ATS-Optimized PDF]

📋 Prerequisites

  • Node.js (v18+): Praxis uses a deterministic Node.js script (evaluate_resume.js) as an adversarial validation loop to strictly enforce ATS compliance and guarantee no facts are hallucinated or dropped during generation. You must have Node installed on your machine.
  • An AI CLI Harness: OpenCode, Claude Code, GitHub Copilot CLI, etc.

🚀 Installation

# Clone the repository
git clone [email protected]:ksmeltzer/Praxis.git
cd Praxis

To install Praxis, follow your specific AI Agent Harness (e.g., opencode, Claude Code, or GitHub Copilot) tool's guide for installing local agents, skills, and custom commands from a project directory.

Note: In many modern AI CLI tools (like Claude Code and OpenCode), you can simply open the Praxis directory and the agents, slash commands, and skills are already pre-configured to work automatically via local environment files (.claude/prompts/ and .opencode/).

🔒 Privacy & Security

Praxis is designed with absolute privacy in mind. Your raw data, API keys, and generated JSON databases are intentionally .gitignore'd. Your career data never leaves your local machine unless you explicitly configure an external model API.

🤖 Configuring Agent Models

The Praxis pipeline relies on two adversarial agents defined in the .agents/ directory:

  • .agents/praxis-pathos.md (The Drafter)
  • .agents/praxis-logos.md (The Auditor)

By default, these agents are configured to use specific models (e.g., github-copilot/claude-sonnet-4.6 and github-copilot/gpt-4o) because our testing proved that Claude 4.6 Sonnet is vastly superior at strict markdown template adherence, while GPT-4o is excellent at auditing and reasoning.

Important: You may need to update the model: string inside these files to match the exact model identifier used by your specific AI provider. If you do not know the correct model string for your provider, you can look it up at https://models.dev.

Yorumlar (0)

Sonuc bulunamadi