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🔍 Deep-diving into hardikpandya/stop-slop — fresh off the trending list.
🔗 https://github.com/hardikpandya/stop-slop
📝 A skill file for removing AI tells from prose
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The Stop Slop GitHub repo is all about helping you refine your writing by removing predictable patterns that are common in AI-generated content. This skill is designed to be used with large language models (LLMs) like Claude, teaching them to catch and remove AI tells such as clichéd phrases, structures, and rhythms.
To get started, you can add this skill to your Claude code, upload the core instructions and reference files to your project knowledge, or use API calls to include the skill in your system prompt. The repo includes a list of
The repo also includes a scoring system to help you evaluate your writing, with dimensions such as
The Stop Slop skill is perfect for anyone looking to improve their writing and make it sound more human. Use it to refine your content and take your writing to the next level - after all, good writing is rewriting.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/hardikpandya/stop-slop
📝 A skill file for removing AI tells from prose
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The Stop Slop GitHub repo is all about helping you refine your writing by removing predictable patterns that are common in AI-generated content. This skill is designed to be used with large language models (LLMs) like Claude, teaching them to catch and remove AI tells such as clichéd phrases, structures, and rhythms.
To get started, you can add this skill to your Claude code, upload the core instructions and reference files to your project knowledge, or use API calls to include the skill in your system prompt. The repo includes a list of
banned phrases and structural clichés to avoid, as well as sentence-level rules to improve your writing.The repo also includes a scoring system to help you evaluate your writing, with dimensions such as
Directness, Rhythm, Trust, Authenticity, and Density. The Stop Slop skill is perfect for anyone looking to improve their writing and make it sound more human. Use it to refine your content and take your writing to the next level - after all, good writing is rewriting.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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💡 affaan-m/ECC just hit the trending charts — here's why it matters.
🔗 https://github.com/affaan-m/ECC
📝 The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The ECC (harness-native operator system) is a complete system for agentic work, born out of an Anthropic hackathon win. It's not just about configurations, but rather a robust framework that includes skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. With 182K+ stars, 28K+ forks, and 170+ contributors, ECC supports 12+ language ecosystems and works across various AI agent harnesses like Claude Code, Codex, and Cursor.
The system is designed to be production-ready, with features like
To get started with ECC, users can follow the Shorthand Guide or the Longform Guide, which provide detailed information on setup, foundations, and philosophy. The Security Guide is also available for users who want to learn more about attack vectors, sandboxing, and sanitization.
ECC is suited for developers, researchers, and operators who want to build and deploy AI-powered agents and skills. With its comprehensive framework and wide range of features, ECC is an ideal choice for those looking to create robust and scalable agentic systems.
Takeaway: ECC is a powerful and flexible operator system that empowers users to build, deploy, and manage AI-powered agents and skills with ease, and its
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/affaan-m/ECC
📝 The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The ECC (harness-native operator system) is a complete system for agentic work, born out of an Anthropic hackathon win. It's not just about configurations, but rather a robust framework that includes skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. With 182K+ stars, 28K+ forks, and 170+ contributors, ECC supports 12+ language ecosystems and works across various AI agent harnesses like Claude Code, Codex, and Cursor.
The system is designed to be production-ready, with features like
agent and skill management, hook and rule configurations, and memory optimization techniques. ECC also includes a range of tools and features for security, research, and development, such as AgentShield and cross-harness architecture.To get started with ECC, users can follow the Shorthand Guide or the Longform Guide, which provide detailed information on setup, foundations, and philosophy. The Security Guide is also available for users who want to learn more about attack vectors, sandboxing, and sanitization.
ECC is suited for developers, researchers, and operators who want to build and deploy AI-powered agents and skills. With its comprehensive framework and wide range of features, ECC is an ideal choice for those looking to create robust and scalable agentic systems.
Takeaway: ECC is a powerful and flexible operator system that empowers users to build, deploy, and manage AI-powered agents and skills with ease, and its
harness-native approach makes it a game-changer in the world of agentic work.──────────────────────────────
🧠 Channel: https://t.me/GithubRe
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🚀 Meet anthropics/knowledge-work-plugins: a gem from today's GitHub trending list.
🔗 https://github.com/anthropics/knowledge-work-plugins
📝 Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork
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The anthropics/knowledge-work-plugins GitHub repository offers a collection of plugins designed to enhance the capabilities of Claude, a workplace assistant. These plugins cater to various roles and teams, providing a strong foundation for tasks such as productivity, sales, customer support, and more.
Key features include:
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-
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To get started, users can install plugins directly from Cowork or via
These plugins become more valuable when customized for your company's specific needs, allowing you to swap connectors, add company context, adjust workflows, and build new plugins.
The takeaway: with the anthropics/knowledge-work-plugins, you can transform Claude into a tailored specialist for your team, streamlining workflows and amplifying productivity.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/anthropics/knowledge-work-plugins
📝 Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork
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The anthropics/knowledge-work-plugins GitHub repository offers a collection of plugins designed to enhance the capabilities of Claude, a workplace assistant. These plugins cater to various roles and teams, providing a strong foundation for tasks such as productivity, sales, customer support, and more.
Key features include:
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Plugin Marketplace: 11 open-sourced plugins for different job functions-
Customization: Edit plugins to fit your company's tools and workflows-
Connectors: Integrate with various tools like Slack, Notion, and JiraTo get started, users can install plugins directly from Cowork or via
claude plugin installcommands. The structure of each plugin includes a
plugin.json manifest, .mcp.json for tool connections, commands for explicit actions, and skills for domain knowledge.These plugins become more valuable when customized for your company's specific needs, allowing you to swap connectors, add company context, adjust workflows, and build new plugins.
The takeaway: with the anthropics/knowledge-work-plugins, you can transform Claude into a tailored specialist for your team, streamlining workflows and amplifying productivity.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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🔥 Leonxlnx/taste-skill is trending — and it deserves your attention.
🔗 https://github.com/Leonxlnx/taste-skill
📝 Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop
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The Taste Skill project is an anti-slop frontend framework designed for AI agents, focusing on creating premium interfaces with strong layout, typography, motion, and spacing. This framework offers a range of
Key features of Taste Skill include:
- Adjustable dials for design variance, motion intensity, and visual density
- Support for multiple coding agents, including ChatGPT and Codex
- Framework-agnostic design rules
- Image-generation skills for creating reference images
To get started with Taste Skill, simply run the installation command:
Taste Skill is ideal for developers and designers looking to enhance their AI-built interfaces with a more premium and polished look.
In short, Taste Skill is a game-changer for anyone looking to take their AI-designed interfaces to the next level - it's time to add some taste to your skill.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/Leonxlnx/taste-skill
📝 Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop
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The Taste Skill project is an anti-slop frontend framework designed for AI agents, focusing on creating premium interfaces with strong layout, typography, motion, and spacing. This framework offers a range of
agent skills that can be easily installed using the npx skills add command. The skills include various design and image-generation capabilities, such as design-taste-frontend, imagegen-frontend-web, and brandkit. Key features of Taste Skill include:
- Adjustable dials for design variance, motion intensity, and visual density
- Support for multiple coding agents, including ChatGPT and Codex
- Framework-agnostic design rules
- Image-generation skills for creating reference images
To get started with Taste Skill, simply run the installation command:
npm install https://github.com/Leonxlnx/taste-skill
Taste Skill is ideal for developers and designers looking to enhance their AI-built interfaces with a more premium and polished look.
In short, Taste Skill is a game-changer for anyone looking to take their AI-designed interfaces to the next level - it's time to add some taste to your skill.
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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📌 Spotted on GitHub Trending: p-e-w/heretic — let's break it down.
🔗 https://github.com/p-e-w/heretic
📝 Fully automatic censorship removal for language models
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The Heretic project is a game-changer in the world of language models, offering fully automatic censorship removal without the need for expensive post-training. By combining
The process is completely automatic and doesn't require configuration, although Heretic provides various parameters for greater control. Users can run Heretic using a simple command, replacing the model name with their desired model. For example:
Heretic supports most dense models, including multimodal and MoE architectures, and has been used to create over 3000 models. The community has well-received models generated with Heretic, praising their ability to provide uncensored responses without destroying the model's intelligence.
In addition to its primary function, Heretic offers research features for exploring model internals, including generating plots of residual vectors and printing details about residual geometry. These features can be accessed by installing Heretic with the optional
In short, Heretic is a powerful tool that's democratizing access to uncensored language models - and that's a big deal!
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/p-e-w/heretic
📝 Fully automatic censorship removal for language models
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The Heretic project is a game-changer in the world of language models, offering fully automatic censorship removal without the need for expensive post-training. By combining
directional ablation with a TPE-based parameter optimizer powered by Optuna, Heretic enables users to decensor language models with ease. The process is completely automatic and doesn't require configuration, although Heretic provides various parameters for greater control. Users can run Heretic using a simple command, replacing the model name with their desired model. For example:
heretic Qwen/Qwen3-4B-Instruct-2507
Heretic supports most dense models, including multimodal and MoE architectures, and has been used to create over 3000 models. The community has well-received models generated with Heretic, praising their ability to provide uncensored responses without destroying the model's intelligence.
In addition to its primary function, Heretic offers research features for exploring model internals, including generating plots of residual vectors and printing details about residual geometry. These features can be accessed by installing Heretic with the optional
research extra.In short, Heretic is a powerful tool that's democratizing access to uncensored language models - and that's a big deal!
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🧠 Channel: https://t.me/GithubRe
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Github Top Repositories
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🔥 shiyu-coder/Kronos is trending — and it deserves your attention.
🔗 https://github.com/shiyu-coder/Kronos
📝 Kronos: A Foundation Model for the Language of Financial Markets
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Kronos is the first open-source foundation model for financial candlesticks, trained on data from over 45 global exchanges. It's a decoder-only model, pre-trained to handle the unique, high-noise characteristics of financial data. The model uses a novel two-stage framework, first quantizing continuous, multi-dimensional K-line data into hierarchical discrete tokens, and then pre-training a large, autoregressive Transformer on these tokens.
Key Features:
- Pre-trained on data from 45 global exchanges
- Novel two-stage framework for handling financial data
- Decoder-only model for autoregressive forecasting
- Supports multiple model sizes for different computational needs
The model can be used for forecasting by loading a pre-trained Kronos model and its corresponding tokenizer from the Hugging Face Hub, and then using the `KronosPredictor` class to handle data preprocessing, normalization, prediction, and inverse normalization.
The model supports batch prediction for efficient processing of multiple time series, and also provides a pipeline for fine-tuning on your own datasets.
Technical Highlights:
- Novel two-stage framework for handling financial data
- Supports multiple model sizes for different computational needs
- Batch prediction for efficient processing of multiple time series
The model is suitable for anyone interested in financial forecasting, including quantitative traders, researchers, and students.
Audience:
- Quantitative traders
- Researchers
- Students
In summary, Kronos is a powerful tool for financial forecasting, and its open-source nature makes it accessible to anyone. With its novel framework and support for batch prediction, it's an ideal choice for anyone looking to improve their financial forecasting capabilities. Get ready to forecast like a pro with Kronos - the future of financial forecasting is here!
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/shiyu-coder/Kronos
📝 Kronos: A Foundation Model for the Language of Financial Markets
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Kronos is the first open-source foundation model for financial candlesticks, trained on data from over 45 global exchanges. It's a decoder-only model, pre-trained to handle the unique, high-noise characteristics of financial data. The model uses a novel two-stage framework, first quantizing continuous, multi-dimensional K-line data into hierarchical discrete tokens, and then pre-training a large, autoregressive Transformer on these tokens.
Key Features:
- Pre-trained on data from 45 global exchanges
- Novel two-stage framework for handling financial data
- Decoder-only model for autoregressive forecasting
- Supports multiple model sizes for different computational needs
The model can be used for forecasting by loading a pre-trained Kronos model and its corresponding tokenizer from the Hugging Face Hub, and then using the `KronosPredictor` class to handle data preprocessing, normalization, prediction, and inverse normalization.
from model import Kronos, KronosTokenizer, KronosPredictorThe model supports batch prediction for efficient processing of multiple time series, and also provides a pipeline for fine-tuning on your own datasets.
Technical Highlights:
- Novel two-stage framework for handling financial data
- Supports multiple model sizes for different computational needs
- Batch prediction for efficient processing of multiple time series
The model is suitable for anyone interested in financial forecasting, including quantitative traders, researchers, and students.
Audience:
- Quantitative traders
- Researchers
- Students
In summary, Kronos is a powerful tool for financial forecasting, and its open-source nature makes it accessible to anyone. With its novel framework and support for batch prediction, it's an ideal choice for anyone looking to improve their financial forecasting capabilities. Get ready to forecast like a pro with Kronos - the future of financial forecasting is here!
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🧠 Channel: https://t.me/GithubRe
Github Top Repositories
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🎯 mukul975/Anthropic-Cybersecurity-Skills landed on trending. Worth a proper look.
🔗 https://github.com/mukul975/Anthropic-Cybersecurity-Skills
📝 754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0
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Anthropic Cybersecurity Skills is an open-source library of 754 production-grade cybersecurity skills, covering 26 security domains and mapped to five industry frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF. This library is designed to give AI agents the security skills of a senior analyst, enabling them to execute structured decision-making workflows and follow practitioner playbooks.
The skills are encoded in a consistent directory structure, with each skill having a
Key features of this library include:
*
*
*
*
To get started, users can clone the repository or use
Takeaway: With Anthropic Cybersecurity Skills, AI agents can now think like senior security analysts, and that changes everything.
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🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/mukul975/Anthropic-Cybersecurity-Skills
📝 754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0
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Anthropic Cybersecurity Skills is an open-source library of 754 production-grade cybersecurity skills, covering 26 security domains and mapped to five industry frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, MITRE D3FEND, and NIST AI RMF. This library is designed to give AI agents the security skills of a senior analyst, enabling them to execute structured decision-making workflows and follow practitioner playbooks.
The skills are encoded in a consistent directory structure, with each skill having a
SKILL.md file containing YAML frontmatter and Markdown body sections. The YAML frontmatter provides metadata, such as the skill name, description, and tags, while the Markdown body sections outline when to use the skill, prerequisites, step-by-step workflow, and verification procedures.Key features of this library include:
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754 production-grade cybersecurity skills*
26 security domains covered, including cloud security, threat hunting, and digital forensics*
5 framework mappings for unified cross-framework coverage*
Compatible with 26+ AI platforms, including Claude Code, GitHub Copilot, and OpenAI Codex CLITo get started, users can clone the repository or use
npx skills add mukul975/Anthropic-Cybersecurity-Skills. The library is designed for AI agents to use, but security professionals and developers can also benefit from it.Takeaway: With Anthropic Cybersecurity Skills, AI agents can now think like senior security analysts, and that changes everything.
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🧠 Channel: https://t.me/GithubRe