Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add viktorbezdek/skillstack --skill context-fundamentalsgit clone --depth 1 https://github.com/viktorbezdek/skillstackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/viktorbezdek/skillstack/context-fundamentals)<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/context-fundamentals"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/context-fundamentals/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/context-fundamentals"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/context-fundamentals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00121 | $0.01766 |
| Opus 5.5 | $0.00048 | $0.00706 |
| Sonnet 5.5 | $0.00024 | $0.00353 |
| Haiku 4.5 | $0.00012 | $0.00177 |
Grade A, and why
context-fundamentals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering Fundamentals
Context is the complete state available to a language model at inference time: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to all other context engineering skills.
When to Use / Not Use
Use when:
- Designing new agent systems or modifying existing architectures
- Onboarding to context engineering concepts
- Reviewing context-related design decisions
- Debugging unexpected agent behavior that may relate to context structure
Do NOT use when:
- Diagnosing context failures or degradation -> use
context-degradation - Compressing or summarizing context -> use
context-compression - KV-cache optimization or context partitioning -> use
context-optimization - File-based context patterns or scratch pads -> use
filesystem-context
Decision Tree
What do you need to understand about context?
├── What goes INTO context
│ ├── System instructions / identity -> System Prompts (§Anatomy)
│ ├── Available actions -> Tool Definitions (§Anatomy)
│ ├── External knowledge -> Retrieved Documents (§Anatomy)
│ ├── Conversation so far -> Message History (§Anatomy)
│ └── Action results -> Tool Outputs (§Anatomy)
├── How context is CONSUMED
│ ├── Attention mechanics / budget -> Attention Budget (§Attention)
│ ├── Position encoding limits -> Position Encoding (§Attention)
│ └── Quality vs quantity trade-offs -> Quality vs Quantity (§Quality)
├── How to MANAGE context
│ ├── Load only when needed -> Progressive Disclosure (§Disclosure)
│ ├── Allocate token budget -> Context Budgeting (§Budgeting)
│ └── Mix pre-load + JIT -> Hybrid Strategies (§Hybrid)
└── Not about fundamentals? -> See related skills
The Anatomy of Context
Five components, each with different characteristics:
| Component | Persistence | Typical Token Share | Key Risk |
|---|---|---|---|
| System Prompts | Session-long | Low | Wrong altitude: too brittle or too vague |
| Tool Definitions | Session-long | Medium | Poor descriptions force agent guessing |
| Retrieved Documents | Dynamic | Medium | Pre-loading creates distraction |
| Message History | Growing | Medium-High | Dominates context in long sessions |
| Tool Outputs | Growing | 83.9% of total | Verbose outputs consume budget |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 162 lines · 121 tokens per session scan A 3b5bef823587
context-fundamentals is a skill published in the GitHub repository viktorbezdek/skillstack (14 stars, last pushed 4mo ago), licensed MIT. It adds 121 tokens to every session and 1,766 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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agent-memory
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accint-frames
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accint-solve
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agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.