Skill collection · muratcankoylan
Agent-Skills-for-Context-Engineering agent skills by muratcankoylan
19 skills from muratcankoylan/Agent-Skills-for-Context-Engineering are listed in the directory, in AI agent and LLM engineering. 12 skills have a full page with install steps and a pre-install summary.
View muratcankoylan/Agent-Skills-for-Context-Engineering on GitHub
At a glance
| Publisher | muratcankoylan |
|---|---|
| Repository | github.com/muratcankoylan/Agent-Skills-for-Context-Engineering |
| Listed skills | 19 |
| Security scan | Not scanned |
| Categories | AI Agents and LLM Engineering |
| License | MIT |
| Last commit | October 11, 2026 |
| README badge | Get the badge for this listing |
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<a href="https://skills.suedeai.ai/directory/muratcankoylan/agent-skills-for-context-engineering/"><img src="https://skills.suedeai.ai/directory/muratcankoylan/agent-skills-for-context-engineering/badge.svg" alt="Suede Skills Directory: listed" height="20"></a>Limited-time feature offer: display either the numeric or letter badge in your repository or skill README (both also qualify), then request badge verification. Eligible skills scoring 80–99 can receive a featured placement after Suede verifies at least one valid badge linked to the skill’s listing. Suede has not announced an end date.
Skills in muratcankoylan/Agent-Skills-for-Context-Engineering
Linked names open the skill's directory page; the rest open the skill folder on GitHub. Descriptions on rows without a page are quoted from each SKILL.md.
| Skill | Category | What it does |
|---|---|---|
| advanced-evaluation 86/100 · B | AI agents | Guides advanced LLM evaluation, including LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment. |
| context-compression 86/100 · B | AI agents | Guides compression of long-running agent sessions through structured summarization, compaction, and durable handoff summaries that preserve decisions, files, risks, and next actions. |
| context-degradation 86/100 · B | AI agents | Diagnoses and mitigates context degradation, including lost-in-middle failures, context poisoning, context clash, context confusion, and performance problems caused by accumulated or conflicting context. |
| context-fundamentals 84/100 · B | AI agents | Explains the foundations of context engineering, including the anatomy of a context window, attention mechanics, and why context quality matters more than quantity. |
| context-optimization 86/100 · B | AI agents | Improves context efficiency through context budgeting, observation masking, KV-cache strategy, partitioning, and token-cost reduction, extending effective context capacity without lowering answer quality. |
| evaluation 86/100 · B | AI agents | Guides building agent evaluation systems with deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines. |
| filesystem-context 86/100 · B | AI agents | Guides file-backed context for agent work, including durable scratchpads, tool-output offloading, cross-agent handoff files, and cleanup policies for context stored outside the prompt. |
| hosted-agents 86/100 · B | AI agents | Guides design of hosted or background agent infrastructure, including sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, and self-spawning agents. |
| memory-systems 86/100 · B | AI agents | Supports persistent semantic memory in agent systems, including cross-session knowledge retention, entity tracking, graph or vector retrieval, memory consolidation, and memory benchmark selection. |
| multi-agent-patterns 86/100 · B | AI agents | Guides design of multi-agent systems with context isolation, supervisor or swarm coordination, explicit handoffs, and parallel execution, including whether multiple agents are justified. |
| project-development 86/100 · B | AI agents | Guides project-level decisions for LLM-powered systems, including whether an LLM fits the task, token and cost estimation, and single-agent versus multi-agent choices. |
| tool-design 86/100 · B | AI agents | Guides the tool-interface layer of agent systems, including tool descriptions, schemas, response formats, error recovery messages, MCP server design, and adding or removing tools. |
| bdi-mental-states 84/100 · B | AI agents | This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational…Get badges  Choose the numeric badge, the letter badge, or both. Each links to this skill’s Suede listing; either valid linked badge can qualify for the feature offer. Markdown HTML |
| context-engineering-collection 76/100 · C | AI agents | A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems… |
| harness-engineering 81/100 · B | AI agents | This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable…Get badges  Choose the numeric badge, the letter badge, or both. Each links to this skill’s Suede listing; either valid linked badge can qualify for the feature offer. Markdown HTML |
| latent-briefing 87/100 · B | AI agents | This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context"…Get badges  Choose the numeric badge, the letter badge, or both. Each links to this skill’s Suede listing; either valid linked badge can qualify for the feature offer. Markdown HTML |
| long-horizon-prompting 76/100 · C | AI agents | This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent… |
| self-improvement-loops 79/100 · C | AI agents | This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI)… |
| self-managed-context 79/100 · C | AI agents | This skill should be used when a model gets read-write control over its own live context window instead of a harness-scheduled compaction policy… |
Where this repository is cited
muratcankoylan/Agent-Skills-for-Context-Engineering appears in 3 curated lists: VoltAgent/awesome-agent-skills, heilcheng/awesome-agent-skills, skillmatic-ai/awesome-agent-skills.