Updated 4d ago
by muratcankoylan
Agent-Skills-for-Context-Engineering
A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
18.1k GitHub stars (18,146)
+111 in 20 days
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- 22 skills (22 skills found in its SKILL.md files)
- 1.5k forks (1,499 forks)
- 4d ago last update (Last updated 4d ago)
Install
Skills CLI
npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering
Asks which skills and agents to install. Just one: npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-engineering-collection — add -g for every project.
Claude Code
git clone https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineeringcp -r "Agent-Skills-for-Context-Engineering" ~/.claude/skills/Agent-Skills-for-Context-Engineering
Copies the context-engineering-collection skill into ~/.claude/skills/ for every project (use .claude/skills/ for one project). Repeat for the other skills listed below.
Plugin
/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering
Run inside Claude Code if the repository ships a plugin marketplace, then pick plugins from /plugin.
Skills can run code on your machine — read the SKILL.md first.
What it does
In the author's own words, from each skill's SKILL.md.
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context-engineering-collection
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.
SKILL.md on GitHub (opens in a new tab) -
book-sft-pipeline
This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.
examples/book-sft-pipeline/SKILL.md on GitHub (opens in a new tab) -
digital-brain
This skill should be used for personal operating-system workflows: content creation, voice consistency, relationship lookup, meeting preparation, weekly review, goal tracking, personal brand management, and network management.
examples/digital-brain-skill/SKILL.md on GitHub (opens in a new tab) -
reasoning-trace-optimizer
Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.
examples/interleaved-thinking/SKILL.md on GitHub (opens in a new tab) -
advanced-evaluation
This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
skills/advanced-evaluation/SKILL.md on GitHub (opens in a new tab) -
bdi-mental-states
This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.
skills/bdi-mental-states/SKILL.md on GitHub (opens in a new tab) -
context-compression
This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.
skills/context-compression/SKILL.md on GitHub (opens in a new tab) -
context-degradation
This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.
skills/context-degradation/SKILL.md on GitHub (opens in a new tab) -
context-fundamentals
This skill should be used to explain or reason about the foundational concepts of context engineering: what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and b…
skills/context-fundamentals/SKILL.md on GitHub (opens in a new tab) -
context-optimization
This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.
skills/context-optimization/SKILL.md on GitHub (opens in a new tab) -
evaluation
This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.
skills/evaluation/SKILL.md on GitHub (opens in a new tab) -
filesystem-context
This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.
skills/filesystem-context/SKILL.md on GitHub (opens in a new tab) -
harness-engineering
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
skills/harness-engineering/SKILL.md on GitHub (opens in a new tab) -
hosted-agents
This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.
skills/hosted-agents/SKILL.md on GitHub (opens in a new tab) -
latent-briefing
This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.
skills/latent-briefing/SKILL.md on GitHub (opens in a new tab) -
long-horizon-prompting
This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a…
skills/long-horizon-prompting/SKILL.md on GitHub (opens in a new tab) -
memory-systems
This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.
skills/memory-systems/SKILL.md on GitHub (opens in a new tab) -
multi-agent-patterns
This skill should be used when designing multi-agent systems that need context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, or a decision on whether multiple agents are justified.
skills/multi-agent-patterns/SKILL.md on GitHub (opens in a new tab) -
project-development
This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the un…
skills/project-development/SKILL.md on GitHub (opens in a new tab) -
self-improvement-loops
This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mec…
skills/self-improvement-loops/SKILL.md on GitHub (opens in a new tab) -
tool-design
This skill should be used for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on, designing tool schemas and response formats, naming conventions, actionable error recovery messages, MCP server design, tool-set consolidation, and deciding when to add or remove an individual tool. Use this when the unit of work is a single tool or a set of tools…
skills/tool-design/SKILL.md on GitHub (opens in a new tab) -
comprehensive-research-agent
Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls
examples/interleaved-thinking/generated_skills/comprehensive-research-agent/SKILL.md on GitHub (opens in a new tab)
No skill matches that filter.
Review before you install
A skill is a set of instructions and scripts your agent will follow, and it can run code on your machine. Read the SKILL.md and any scripts first — this listing is not a security review.