context-fundamentals
An AI agent skill from muratcankoylan/Agent-Skills-for-Context-Engineering, scored 67 out of 100 by code.
Code score by group
It loses the most points in Done criteria (15), the group that checks whether there is a checklist and a step that verifies the work.
What the author says it does
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 background reading. Route operational work to the specialized skills: debugging attention failures goes to context-degradation, token-efficiency work goes to context-optimization, conversation summariz…
This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.
- Folder
- skills/context-fundamentals
- License
- MIT
- GitHub stars
- 18,341
- SKILL.md lines
- 211
- Other files
- 2
Add context-fundamentals to your agent
npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-fundamentals
The command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored.
More skills from muratcankoylan/Agent-Skills-for-Context-Engineering
-
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 pro…
76B -
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,…
74B -
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 ag…
71B -
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-improv…
70B -
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 wor…
68C -
self-managed-context
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: the context exposed as an editabl…
66C -
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, roll…
65C -
hosted-agents
This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer c…
65C
All skills in muratcankoylan/Agent-Skills-for-Context-Engineering
Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.