Agent-Skills-for-Context-Engineering

Agent Skills for Context Engineering

A comprehensive, open collection of Agent Skills focused on context engineering and harness engineering principles for building production-grade AI agent systems. These skills teach the art and science of curating context, designing agent operating loops, and evaluating agent behavior across any agent platform.

What is Context Engineering?

Context engineering is the discipline of managing the language model's context window. Unlike prompt engineering, which focuses on crafting effective instructions, context engineering addresses the holistic curation of all information that enters the model's limited attention budget: system prompts, tool definitions, retrieved documents, message history, and tool outputs.

The fundamental challenge is that context windows are constrained not by raw token capacity but by attention mechanics. As context length increases, models exhibit predictable degradation patterns: the "lost-in-the-middle" phenomenon, U-shaped attention curves, and attention scarcity. Effective context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes.

Recognition

This repository is cited in academic research as foundational work on static skill architecture:

"While static skills are well-recognized [Anthropic, 2025b; Muratcan Koylan, 2025], MCE is among the first to dynamically evolve them, bridging manual skill engineering and autonomous self-improvement."

1. Meta Context Engineering via Agentic Skill Evolution, Peking University State Key Laboratory of General Artificial Intelligence (2025)
2. Agent Harness Engineering: A Survey, CMU, Yale, JHU, NEU, Tulane, UAB, OSU, Virginia Tech, and Amazon (2026)

Skills Overview

Foundational Skills

These skills establish the foundational understanding required for all subsequent context engineering work.

Skill Description context-fundamentals Understand what context is, why it matters, and the anatomy of context in agent systems context-degradation Recognize patterns of context failure: lost-in-middle, poisoning, distraction, and clash context-compression Design and evaluate compression strategies for long-running sessions

Architectural Skills

These skills cover the patterns and structures for building effective agent systems.

Skill Description multi-agent-patterns Master orchestrator, peer-to-peer, and hierarchical multi-agent architectures long-horizon-prompting NEW Write pseudo-formal task briefs for long-running autonomous agents and parallel orchestrations: exact success predicates, non-counting outcomes, audit-gated return conditions, effort floors, and diversity policies, modeled on the published GPT-5.6 Sol Ultra Cycle Double Cover prompt memory-systems Design short-term, long-term, and graph-based memory architectures tool-design Build tools that agents can use effectively filesystem-context Use filesystems for dynamic context discovery, tool output offloading, and plan persistence hosted-agents NEW Build background coding agents with sandboxed VMs, pre-built images, multiplayer support, and multi-client interfaces

Operational Skills

These skills address the ongoing operation and optimization of agent systems.

Skill Description context-optimization Apply compaction, masking, and caching strategies latent-briefing Share task-relevant orchestrator state with workers via task-guided KV cache compaction when the worker runtime is controllable evaluation Build evaluation frameworks for agent systems advanced-evaluation Master LLM-as-a-Judge techniques: direct scoring, pairwise comparison, rubric generation, and bias mitigation harness-engineering Design autonomous agent harnesses with locked metrics, durable logs, novelty gates, rollback, and human approval boundaries self-improvement-loops NEW Build loops where the harness itself is the optimization target: RSI, meta-harness search, failure-driven self-edits, evolutionary scaffold search, and acceptance gates for self-modifying systems

Development Methodology

These skills cover the meta-level practices for building LLM-powered projects.

Skill Description project-development Design and build LLM projects from ideation through deployment, including task-model fit analysis, pipeline architecture, and structured output design

Cognitive Architecture Skills

These skills cover formal cognitive modeling for rational agent systems.

Skill Description bdi-mental-states NEW Transform external RDF context into agent mental states (beliefs, desires, intentions) using formal BDI ontology patterns for deliberative reasoning and explainability

Design Philosophy

Progressive Disclosure

Each skill is structured for efficient context use. At startup, agents load only skill names and descriptions. Full content loads only when a skill is activated for relevant tasks.

Platform Agnosticism

These skills focus on transferable principles rather than vendor-specific implementations. The patterns work across Claude Code, Cursor, and any agent platform that supports skills or allows custom instructions.

Conceptual Foundation with Practical Examples

Scripts and examples demonstrate concepts using Python pseudocode that works across environments without requiring specific dependency installations.

Usage

Usage with Claude Code

This repository is a Claude Code Plugin Marketplace containing context engineering skills that Claude automatically discovers and activates based on your task context.

Installation

Step 1: Add the Marketplace

Run this command in Claude Code to register this repository as a plugin source:

/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering

Step 2: Install the Plugin

Option A - Browse and install:

1. Select Browse and install plugins
2. Select context-engineering-marketplace
3. Select context-engineering
4. Select Install now

Option B - Direct install via command:

/plugin install context-engineering@context-engineering-marketplace

This installs all published skills in a single plugin. Skills are activated automatically based on your task context.

Skill Activation Scenarios

Skill Activate When context-fundamentals Establishing context-window mental models, planning agent architecture, or explaining how context components affect model behavior context-degradation Diagnosing attention failures, context poisoning, lost-in-middle behavior, or degraded agent performance across long sessions context-compression Preserving useful state while reducing conversation, tool-output, or trajectory size under context pressure context-optimization Improving token efficiency, retrieval precision, prefix reuse, masking, partitioning, or budget allocation for agent systems latent-briefing Sharing orchestrator trajectory with workers via task-guided KV cache compaction when the worker runtime is controllable and the models are compatible multi-agent-patterns Choosing coordination patterns, isolating context across agents, designing handoffs, or evaluating whether parallel agents are justified long-horizon-prompting Writing or evaluating the launch prompt for a long-running autonomous agent or parallel orchestration: success predicates, non-counting outcomes, persistence and stop rules, adversarial audit gates, and portfolio diversity policies memory-systems Persisting cross-session knowledge, tracking entities over time, choosing memory frameworks, or designing retrieval and update semantics tool-design Defining agent-tool contracts, consolidating tool surfaces, improving descriptions, or making tool errors actionable filesystem-context Moving large or durable context into files, creating scratchpads, supporting just-in-time discovery, or coordinating agents through shared artifacts hosted-agents Running coding agents in remote sandboxes, background environments, warm pools, or multiplayer agent infrastructure evaluation Creating deterministic checks, rubrics, regression suites, production monitoring, or quality gates for agent behavior advanced-evaluation Using LLM judges, pairwise comparison, calibration, bias mitigation, or human-aligned quality assessment harness-engineering Designing autonomous loops with locked evaluators, editable surfaces, durable logs, novelty gates, rollback, and approval boundaries self-improvement-loops Building loops that modify themselves: failure-driven harness self-edits, meta-harness search, evolutionary scaffold search, context mechanism evolution, and acceptance gates for self-modification project-development Deciding whether an LLM is appropriate, shaping batch pipelines, creating staged artifacts, or estimating operational cost bdi-mental-states Modeling beliefs, desires, intentions, rational action traces, or neuro-symbolic state transformations for agents

For Cursor, Codex, and Open Plugins

This repository ships as an Open Plugins plugin. Hosts discover skills from the repo-root skills/ directory (each subdirectory contains a SKILL.md file). The manifest lives at .plugin/plugin.json.

Cursor (recommended):

1. Install from the Cursor Plugin Directory, or clone this repo and point Cursor at the plugin root.
2. Cursor reads .plugin/plugin.json and discovers the repo-root skills/ directory through the Open Plugins manifest.
3. For project-local manual installs, copy skill directories into .cursor/skills/. Do not rely on repository symlinks; they are fragile on Windows and in plugin packaging.

Codex / GitHub Copilot CLI / other Open Plugins hosts:

1. Clone or add this repository as a plugin directory.
2. The host reads .plugin/plugin.json and discovers all published skills under skills/.
3. For project-local manual installs, copy skill directories into .codex/skills/ or the host's documented Agent Skills directory.

Using Individual Skills

Agent Skills require a directory layout, not a flat markdown file. Copy the skill folder into your project's skills directory:

# Example: add just the context-fundamentals skill to a Cursor project
mkdir -p .cursor/skills
cp -R skills/context-fundamentals .cursor/skills/

# Claude Code project-scoped install (same directory layout)
mkdir -p .claude/skills
cp -R skills/context-fundamentals .claude/skills/

# Codex project-scoped install
mkdir -p .codex/skills
cp -R skills/context-fundamentals .codex/skills/

# Generic Agent Skills repo-scoped install (Codex/OpenAI, Copilot CLI, Open Plugins hosts)
mkdir -p .agents/skills
cp -R skills/context-fundamentals .agents/skills/

Do not flatten SKILL.md into a single file at .claude/skills/context-fundamentals.md. That breaks relative references/ paths and violates the Agent Skills directory spec used by Cursor, Claude Code, and Codex.

Available skills