context-engineering-collection

An agent skill by muratcankoylan, from muratcankoylan/Agent-Skills-for-Context-Engineering. Tags: api, debugging, developer-tools, strategy.

What it does

A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.

Install

With the skills CLI, which installs into Claude Code, Codex, Cursor and other agents:

npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-engineering-collection

Or copy the skill folder into Claude Code's skills directory by hand (~/.claude/skills for every project, or .claude/skills inside one):

git clone --depth 1 https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering
cp -r Agent-Skills-for-Context-Engineering ~/.claude/skills/context-engineering-collection

Safety box score

Not rated yet. A safety box score grades what a skill and its scripts can reach on the machine of whoever installs it, across eight categories from shell execution to secrets access. Anyone can request one from this page; it is saved for everyone. How the score works.

Source

Repository

muratcankoylan/Agent-Skills-for-Context-Engineering (all skills from this repository)

Path

SKILL.md

Branch

main

Updated

2026-09-19

Related skills

• tool-design — This skill should be used for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on.
• bdi-mental-states — This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations.
• context-degradation — This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion.
• harness-engineering — This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs.
• memory-systems — This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity.
• reasoning-trace-optimizer — Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures.