Borrowing it
Nothing to install: this file belongs to docxology/template. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/docxology/template/main/.agents/skills/context-fundamentals/SKILL.mdgit clone --depth 1 https://github.com/docxology/templateWrote 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/docxology/template/context-fundamentals)<a href="https://agentmods.dev/skills/docxology/template/context-fundamentals"><img src="https://agentmods.dev/badge/skills/docxology/template/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/docxology/template/context-fundamentals"><img src="https://agentmods.dev/badge/skills/docxology/template/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 | $0.00125 | $0.03183 |
| Opus 5.5 | $0.00050 | $0.01273 |
| Sonnet 5.5 | $0.00025 | $0.00637 |
| Haiku 4.5 | $0.00013 | $0.00318 |
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.
This is a copy
100% identical to context-fundamentals — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 211 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. Context engineering is the discipline of curating the smallest high-signal token set that maximizes the likelihood of desired outcomes.
This skill is the conceptual foundation that every other skill in the collection builds on. It explains what context is, how attention mechanics work, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. It does not own operational work: debugging attention failures belongs to context-degradation, token-efficiency tactics belong to context-optimization, conversation summarization belongs to context-compression, file-based offloading belongs to filesystem-context, and project-shape decisions belong to project-development.
When to Activate
Activate this skill when the work is conceptual:
- Explaining what context is and how attention mechanics constrain agent behavior.
- Onboarding new contributors who need the mental models before diving into operational skills.
- Reasoning about a context-related design decision from first principles (what does this constraint mean, why does this trade-off exist) before picking a specific tactic.
- Writing or reviewing documentation that needs to ground operational guidance in the underlying mechanics.
Do not activate this skill for operational work. The specialized skills handle the doing:
- Diagnosing lost-in-middle, context poisoning, or attention failures:
context-degradation. - Reducing token cost via masking, partitioning, prefix caching, budgets:
context-optimization. - Compressing a long session into a handoff summary:
context-compression. - Offloading large tool outputs or maintaining a durable scratchpad:
filesystem-context. - Deciding the shape of an LLM project or pipeline:
project-development.
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.
- 1mo ago First seen · 211 lines · 125 tokens per session scan A 03b56e1c40ed
context-fundamentals is a skill published in the GitHub repository docxology/template (20 stars, last pushed today), licensed Apache-2.0. It adds 125 tokens to every session and 3,183 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to context-fundamentals, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
learn
Review the current conversation and update project knowledge artifacts - common-gotchas.md (bug patterns), conventions at the highest rung that holds them (code, check, reviewer standard, skill, then AGENTS.md), navigation pointers, agent memory (cross-session). Use when asked to '/learn', 'what did we learn'…
save-learning
Use when you learned something durable — a decision, a hard-won lesson, a useful reference. Captures it into the memory wiki so it compounds.
kiro-steering-custom
Create custom steering documents for specialized project contexts.
memory
Cross-project research memory. Deep-dive past projects' notes and record corrections across all Luxas research projects.
daily-journal
A passive daily work journal that Claude keeps FOR you so you never have to write it yourself. Append short entries after meaningful work (what was done, what you focused on, artifacts touched) to 01-daily/journal/YYYY-MM-DD.md. Run a guided reflection at night or in the morning. Use when you run /daily-journal, say…
calibration-technician
Use when a task needs the judgment of a Calibration Technologist/Technician — computing a Test Uncertainty Ratio (TUR) and deciding whether it meets the 4:1 target, building a measurement uncertainty budget (Type A/Type B, GUM-style) for a calibration, tracing a reference standard's chain of custody to NIST/SI…