Forem: The_resa

Agent Skills Explained: From Prompts to Structured AI Workflows

The_resa — Mon, 20 Apr 2026 02:38:50 +0000

Agent Skills are modular, reusable units of procedural knowledge that allow AI agents to perform specific tasks.

People hear “AI agent” and assume it’s already a complete system. Then they hear “agent skills” and things get fuzzy—are these tools? prompts? plugins?
The short answer: agent skills are structured capabilities that help AI agents do specific tasks.

According to a DataCamp article, agent skills are “portable, self-contained units of domain knowledge and procedural logic” that define how to perform workflows, not just what to know.
Similarly, Spring AI describes them as “modular folders of instructions, scripts, and resources that AI agents can discover and load on demand.”

Instead of giving an AI a long messy instruction every time, you package the instructions into a reusable “mini-workflow” it can run anytime.

So if we strip away the buzzwords, a consistent pattern appears:

👉 Agent Skills = structured workflows + reusable logic + optional code/tools

Why Agent Skills Exist?

Prompts are great… until they aren’t.
If you’ve ever written a long prompt and noticed the AI ignored half of it, you’ve already seen the limitation.

As prompts grow larger and more complex, important instructions can get diluted inside the model’s context window, leading to inconsistent behavior.

Agent skills address this by:

Keeping logic modular
Loading only when needed
Separating concerns (instead of one giant prompt blob)

According to Microsoft documentation, skills are:

Advertised to the agent
Loaded only when relevant
Expanded with additional resources as needed

This architectural idea is often called “progressive disclosure.”

How Agent Skills Actually Work？

Let’s walk through a simplified version of how they function:

The agent receives a task
It evaluates what kind of work is needed
It selects a matching skill
It loads that skill
It executes the workflow
It returns a structured result

The important idea here is selective loading. The AI is not guessing everything from scratch each time—it’s using prebuilt procedures. That’s why agent systems feel more stable than raw prompting.

Where to Find Agent Skills？

If you look around, you’ll notice there isn’t just one place. The ecosystem is still forming, and it’s a bit fragmented—somewhat like the early days of app stores. A lot of skills live on platforms like GitHub, where developers publish reusable workflows for others to try. However, many of agent skills collections are broad, not specialized. They cover many use cases—but not always with deep domain precision. So What if You Need Domain-Specific Agent Skills?

AIPOCH for Medical Research Agent Skills

If you’re specifically looking for medical research agent skills, that’s exactly where AIPOCH comes in.
As explained in this introduction to AIPOCH, AIPOCH offers a curated library of Medical Research Agent Skills built around medical research workflows—things like:

evidence Insights
protocol design
data analysis
academic writing

You can explore the complete agent skills library here:
👉 Medical Research Skills Github Repo

⭐ If you find this repository useful, consider giving it a star! It helps more researchers discover Medical Research Agent Skills and supports the continued development of this library.

Instead of asking an AI to figure everything out from scratch, you’re using predefined research processes that are already structured and consistent. That makes a difference.

FAQs

Are there agent skills for medical research?

Yes, there are specialized agent skills designed for medical research workflows. AIPOCH provides curated medical research agent skills for tasks such as evidence extraction, protocol design, data analysis, and academic writing. These skills are designed to support more structured and consistent research workflows, which can be helpful when working with complex scientific tasks.

What are ai agent skills?

Agent skills are modular, reusable workflows that allow AI agents to perform specific tasks in a structured and consistent way.

How do agent skills work in practice?

Agent skills work by being selected and loaded by an AI agent when needed. The agent evaluates a task, chooses the relevant skill, executes its predefined workflow, and returns structured results. This makes outputs more reliable compared to raw prompting.

What are some real-world agent skills examples?

Common agent skills examples include data analysis workflows, literature review summarization, content generation, and academic writing support.

Where can I find agent skills or an agent skills hub?

You can find agent skills in open-source repositories (such as GitHub), developer platforms, and curated libraries often referred to as agent skills hubs. These hubs provide reusable workflows for different domains, from general automation to specialized fields like medical research.

Disclaimer
This content is for informational purposes only. It does not constitute medical, clinical, or professional advice. AIPOCH Medical Research Agent Skills are designed to support research workflows. They are not intended to replace professional judgment, clinical decision-making, or peer-reviewed validation. AI-generated outputs may be incomplete or inaccurate and should be independently verified before use. References to third-party tools, platforms, or publications are provided for context only and do not imply endorsement or affiliation.

Hermes Agent: Why does it feels different from other agents

The_resa — Thu, 16 Apr 2026 08:10:07 +0000

Originally published on AIPOCH

If you’ve been browsing GitHub, AI forums, or even a few niche newsletters lately, you’ve probably seen the name Hermes Agent pop up more than once.

What Is Hermes Agent?

According to the official documentation, Hermes Agent is:

“The self-improving AI agent built by Nous Research. The agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, and builds a deepening model of who you are across sessions.”
(Source: Nous Research Hermes Agent docs)

Let 's translate that into something more grounded:

Unlike traditional AI tools, it includes a built-in learning loop that allows it to turn past tasks into reusable skills, refine those skills over time, and retain knowledge across sessions. As it continues to operate, Hermes gradually builds a more personalized understanding of the user, making its behavior more context-aware and efficient.

It keeps memory
It learns by doing
It accumulates context and experience

What's The Core Idea Of Hermes Agent?

The Core Idea of Hermes Agent is "A Self-Improving Loop".

This is where Hermes starts to separate itself.

The official concept is simple but powerful:

When Hermes completes a task, it can convert that solution into a reusable “skill.”
(Source: Hermes documentation + feature overview)

So instead of repeating work, it builds a library of experience.

Think of it like this:

First time → slow, exploratory
Second time → faster
Third time → almost automatic

That loop looks like:

Receive a task
Execute using tools
Save solution as a skill
Reuse skill later

This “skill system” is explicitly documented as a core mechanism of Hermes Agent.

Why It Feels Different From Other AI Agents

The memory system of Hermes Agent is one of its standout features.
Hermes doesn’t just “remember conversations”—it builds a long-term understanding of your work, preferences, and past tasks.

And yes, that changes everything.

According to the Hermes Agent documentation, Hermes uses structured files like:

MEMORY.md → agent's personal notes
USER.md → user-specific information

These files act as the foundation of long-term knowledge. Agent's memory files have Char Limit

Why Hermes Agent Memory Matters

Hermes remembers:

Preferences
Workflows
Past decisions

So you don’t have to explain things again.

It gets better with use

The more you use Hermes:

The more it learns
The more it adapts
The more efficient it becomes

It enables real automation

Without memory, automation breaks easily.

With memory, Hermes can:

Maintain consistency
Reuse past solutions
Improve task execution

It supports personalization at scale

Hermes Agent + AIPOCH Medical Agent Skills

AIPOCH Medical Agent Skills can work with Hermes Agent.

Hermes Agent brings persistence, memory, and a built-in learning loop—it can execute tasks, refine its approach over time, and retain what it learns across sessions. On the other side, AIPOCH provides Medical Research Agent Skills designed for medical research workflows, like Evidence Insights, Protocol Design, Data Analysis, and Academic Writing.

Hermes gives you a learning agent.
AIPOCH gives it medical research agent skills.

These capabilities are designed to support research workflows, and outputs should always be reviewed before real-world use.

Explore AIPOCH Medical Research Agent Skills

Researchers and AI agents can explore the growing library of medical research agent skills through multiple resources:

Open-Source Repository on GitHub
AIPOCH Medical Research Agent Skills List – Browse all skills organized by category, from Evidence Insights to Academic Writing.
Full Agent Skills Overview – Learn about the purpose, workflow integration, and capabilities of each skill in detail.

⭐ If you find this repository useful, consider giving it a star! It helps more researchers discover Medical Research Agent Skills and supports the continued development of this library. Open-Source Repository on GitHub.

FAQs About Hermes Agent

What is Hermes Agent in simple terms?

In simple terms, Hermes Agent is an AI agent that learns from experience, improves itself over time, and gets better at understanding you.

Is Hermes Agent open source?

Yes, Hermes Agent is an open-source project developed by Nous Research.

Why Hermes Agent Feels Different From Other AI Agents?

Hermes focuses more on persistent memory and reusable skills.

Does Hermes Agent use its own AI model?

Hermes is an agent framework and can connect to different LLMs.

What are Hermes Agent skills?

Skills are reusable task workflows generated by the agent after completing tasks, allowing future automation. (Source: Hermes documentation)

Disclaimer: This AI-assisted content is for informational purposes only. The content may be incomplete or inaccurate, and should be independently verified. The AI systems and agent workflows discussed (including Hermes Agent and AIPOCH Medical Research Agent Skills) are intended to support research processes, not to replace professional judgment. AI-generated outputs may be incomplete or inaccurate and should be independently reviewed. References to third-party projects do not imply official endorsement or affiliation.

Medical Research Agent Skills: Blind Review Sanitizer

The_resa — Tue, 14 Apr 2026 01:54:30 +0000

You can explore a growing collection of Medical Research Agent Skills on AIPOCH Github.

If you find it useful, consider giving it a ⭐ to support the project!

If you want to explore more about this skill—including Complete Workflow Example, Common Patterns, Quality Checklist, Common Pitfalls, Troubleshooting, References, and more—please visit this page: Blind Review Sanitizer.

What is Blind Review Sanitizer?

Automatically anonymize academic manuscripts for double-blind peer review by removing author identifiers, institutional affiliations, acknowledgments, and excessive self-citations while preserving document formatting and scholarly content integrity.

What Are The Key Capabilities of This Agent Skill?

Author Identity Removal: Automatically detect and redact author names, institutional affiliations, and contact information using pattern matching and customizable rules
Acknowledgment Section Sanitization: Identify and remove or flag acknowledgment sections that may reveal author identity through funding sources or personal thanks
Self-Citation Detection and Neutralization: Identify first-person citations and excessive self-references that could deanonymize the submission
Multi-Format Document Support: Process DOCX, Markdown, and plain text files with format-aware sanitization strategies
Audit Trail Generation: Create detailed logs of all redactions made for verification and transparency

Limitations and Considerations

Important Limitations:

Not Foolproof: Automated sanitization cannot guarantee complete anonymity. Always perform manual verification.
Context Blindness: Pattern matching may miss context-dependent identifiers or incorrectly flag legitimate content.
Image Processing: This tool processes text only. Images, figures, and embedded objects may contain identifying information not detected.
LaTeX Support: Limited support for LaTeX source files. Consider using LaTeX-specific tools for LaTeX manuscripts.
Language Support: Optimized for English and Chinese. Other languages may have reduced accuracy.

Ethical and Legal Considerations:

Author Consent: Ensure all authors consent to anonymization before submission
Copyright: Anonymization does not change copyright ownership
Data Availability: Some journals require non-anonymized versions for data/code availability statements
Post-Acceptance: Plan for deanonymization process after paper acceptance

Explore AIPOCH Agent Skills

Researchers and AI agents can explore the growing library of medical research agent skills through multiple resources:

Open-Source Repository on GitHub
AIPOCH Medical Research Agent Skills List – Browse all skills organized by category.
Full Agent Skills Overview – Learn about the purpose and capabilities of each skill in detail.

These resources make it easy to explore, validate, and experiment with AIPOCH’s growing library.

AIPOCH Medical Skill Auditor: How We Evaluates Agent Skills?

The_resa — Thu, 09 Apr 2026 06:48:57 +0000

You can explore a growing collection of Medical Research Agent Skills on the AIPOCH Github.

If you find it useful, consider giving it a ⭐ to support the project!

What is Medical Skill Auditor?

AIPOCH Medical Skill Evaluator is a framework for assessing the quality of AIPOCH's Agent Skills. Its core function is to perform a comprehensive quality check on a Skill before it is released to users.

How does Medical Skill Auditor Work?

Veto Gates

To enforce strict quality control, Skill Auditor is designed with two layers of veto mechanisms. Any failure in these checks may lead to immediate rejection of a skill.

Skill Veto

Take the agent skill “medical-research-literature-reader-pro” as an example：

Operational Stability
Structural Consistency
Result Determinism
System Security

Research Veto

Take the agent skill “medical-research-literature-reader-pro” as an example：

Scientific Integrity
Practice Boundaries
Methodological Ground
Code Usability

Core Capability

Take the agent skill “medical-research-literature-reader-pro” as an example：

Evaluates a skill’s design and contract against key dimensions such as Functional Suitability, Reliability, Performance & Context, Agent Usability, Human Usability, Security, Agent-Specific and Maintainability.

Medical Task
Take the agent skill “medical-research-literature-reader-pro” as an example：

Assesses actual outputs of a skill with layered criteria.

For skill testing, the AI automatically generates inputs. The number of inputs in specific categories will increase or decrease depending on the complexity of the skill. The following 7 inputs represent the most comprehensive version.

Canonical
Variant A
Edge
Variant B
Stress
Scope Boundary
Adversarial

Skill Complexity Classification

Label	Code/Rank	Definition
Simple	S	Narrow task scope
Moderate	M	Moderate branching or multiple task types
Complex	C	Broad or multi-step specialized skill

Simple (S): 3 inputs

Moderate (M): 5 inputs

Complex (C): 7 inputs

Final Score

Take the agent skill “medical-research-literature-reader-pro” as an example：

The Skill Evaluator uses a two-stage scoring system: static evaluation (design quality, accounting for 40%) and dynamic evaluation (runtime performance, accounting for 60%). The final overall score is derived by combining both.

Static (40%)
Dynamic (60%)

Final Score = Static Score × 40% + Dynamic Score × 60%

You can view evaluation results for selected AIPOCH skills here.

Feedback and possible future directions

This framework is still under active development.Right now it is only applied to a subset of AIPOCH’s skills, but we’re considering expanding it more broadly.

😎AIPOCH – 450+ Modular Agent Skills for Medical Research

The_resa — Wed, 25 Mar 2026 09:10:00 +0000

Hi! I’m part of the team building AIPOCH, an open-source library of 450+ executable Agent Skills designed specifically for medical research workflows.

AIPOCH GitHub Repository
see our Website here

Why we built AIPOCH?

Most medical research AI tools today are essentially a bundle of prompt engineering + fixed toolchains + a UI. They handle "published knowledge" well (like summarizing a paper), but they fall apart the moment you say: "Now validate this hypothesis using my own cohort data." Existing tools often lack a persistent research context. There is no version-controlled hypothesis tracking, no seamless link between literature evidence and actual data execution. We wanted to move beyond point-solutions to a modular, extensible protocol.

What is AIPOCH？

AIPOCH is a curated library of 450+ Medical Research Agent Skills, built to work with OpenClaw and other AI agent platforms, including OpenCode and Claude. To achieve this, we have encoded specialized medical research logic directly into our Skills.

Scientific Integrity Constraints
Study type identification
Medically Specialized Prompt Logic

A Skill is a structured capability package consisting of:

skill.md: A "contract" containing YAML metadata (trigger logic) and specific operational steps.
Python Scripts: Executable engines called directly via bash under the guidance of the skill.md.

In the context of AIPOCH, we define our developed skills as structured capability packages designed for professional medical research tasks, utilizing skill.md as the trigger contract and Python scripts as the execution engine. We have embedded medical research constraints directly into our skill.md, references, and Python scripts.

AIPOCH Medical Skill Auditor (in development)

What is Medical Skill Auditor?

Skill Auditor is AIPOCH’s evaluation framework under active development for scoring Medical Research Agent Skills with rigorous, multi‑dimensional quality metrics. It’s intended to go beyond static descriptions by measuring both core capability and real execution performance—giving users and developers a clearer, data‑driven understanding of skill quality.

How does it work?

🧰 Core Capability
Evaluates a skill’s design and contract against key dimensions such as Functional Suitability, reliability, performance & context, Agent Usability, human usability, Security, Agent-Specific and maintainability.

📊 Medical Task
Assesses actual outputs of a skill with layered criteria, weighting general competence and category‑specific behaviors to reflect real‑world execution quality.

🚫Veto Gates
To enforce strict quality control, Skill Auditor is designed with two layers of veto mechanisms. Any failure in these checks may lead to immediate rejection of a skill.

Skill Veto
Operational Stability
Structural Consistency
Result Determinism
System Security
Research Veto
Scientific Integrity
Practice Boundaries
Methodological Ground
Code Usability

The Most Frustrating Moment

One of our biggest early mistakes was using a cheaper LLM to "vibe coding" the initial batch of scripts.
On the surface, it worked. The scripts ran, and the logic seemed okay. The nightmare only surfaced during our audit: we realized the executing agent was silently correcting the script's logic on the fly. Because the agent read the intent in skill.md, it would "patch" the sloppy edge cases and vague error branches in the Python code during execution.
The result? We were burning massive amounts of extra tokens just to fix errors that shouldn't have existed. It didn't throw an error; it just showed up on the API bill.
We eventually scrapped the lot. We learned the hard way: Quantity isn't a moat; high-quality scripts are.

All questions/feedback welcome！😎😎😎