Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill context-fundamentalsgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/context-fundamentals)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/context-fundamentals"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/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/foryourhealth111-pixel/vibe-skills/context-fundamentals"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/context-fundamentals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 47 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00069 | $0.02116 |
| Opus 5 | $0.00034 | $0.01058 |
| Sonnet 5 | $0.00014 | $0.00423 |
| Haiku 4.5 | $0.00007 | $0.00212 |
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 13d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- context-fundamentals — 91% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 186 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. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.
When to Activate
Activate this skill when:
- Designing new agent systems or modifying existing architectures
- Debugging unexpected agent behavior that may relate to context
- Optimizing context usage to reduce token costs or improve performance
- Onboarding new team members to context engineering concepts
- Reviewing context-related design decisions
Core Concepts
Context comprises several distinct components, each with different characteristics and constraints. The attention mechanism creates a finite budget that constrains effective context usage. Progressive disclosure manages this constraint by loading information only as needed. The engineering discipline is curating the smallest high-signal token set that achieves desired outcomes.
Detailed Topics
The Anatomy of Context
System Prompts System prompts establish the agent's core identity, constraints, and behavioral guidelines. They are loaded once at session start and typically persist throughout the conversation. System prompts should be extremely clear and use simple, direct language at the right altitude for the agent.
The right altitude balances two failure modes. At one extreme, engineers hardcode complex brittle logic that creates fragility and maintenance burden. At the other extreme, engineers provide vague high-level guidance that fails to give concrete signals for desired outputs or falsely assumes shared context. The optimal altitude strikes a balance: specific enough to guide behavior effectively, yet flexible enough to provide strong heuristics.
Organize prompts into distinct sections using XML tagging or Markdown headers to delineate background information, instructions, tool guidance, and output description. The exact formatting matters less as models become more capable, but structural clarity remains valuable.
What ships with it
2 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.
- 13d ago First seen · 186 lines · 69 tokens per session scan A 4f6b700280f6
context-fundamentals is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 69 tokens to every session and 2,116 once invoked, about $0.0003 per session on Opus 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
cs-onboard
Structured C-suite onboarding via a founder interview that captures company context into a persistent file used by all advisory skills. Use when setting up advisory context, refreshing stale context, or onboarding a new executive.
decision-logger
Two-layer memory for executive decisions, separating raw deliberation from approved decisions to prevent hallucinated consensus. Use when logging decisions, reviewing past decisions, or checking overdue action items.
honcho
Configure and use Honcho memory with Zorro -- cross-session user modeling, multi-profile peer isolation, observation config, and dialectic reasoning. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation and recall settings.
qmd
Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.
hive.note-taking
Maintain a free-form scratchpad of decisions, extracted values, and open questions so context pruning doesn't lose anything you still need.
ralph-reflect
View and manage agent loop reflections and episodic memory.