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 nimadorostkar/Claude-Skills-collection --skill context-engineeringgit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/context-engineering)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/context-engineering"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/context-engineering/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/nimadorostkar/claude-skills-collection/context-engineering"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/context-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.01321 |
| Opus 5 | $0.00023 | $0.00660 |
| Sonnet 5 | $0.00009 | $0.00264 |
| Haiku 4.5 | $0.00005 | $0.00132 |
Grade A, and why
context-engineering 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 11d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
Purpose
Decide what an LLM sees, and what it does not. Context is a finite budget with a nonlinear cost curve: more context is not more capability, and past a point it is actively less.
When to Use
- Building an agent or assistant that runs over many turns.
- A model that performs worse as the conversation gets longer.
- Managing tool results, retrieved documents, or long files.
- Reducing token cost in a long-running system.
Capabilities
- Context budgeting and allocation.
- Retrieval: putting the right thing in, not everything.
- Compaction and summarization of history.
- Memory: what persists across sessions, and in what form.
- Tool-result pruning and progressive disclosure.
Inputs
- The task, and what information it genuinely requires.
- The context window and its cost.
- The failure symptom, if the system is degrading over long runs.
Outputs
- A context budget with an allocation per component.
- A compaction strategy that preserves what matters.
- Measurably better performance on long tasks.
Workflow
- Budget the window explicitly — System prompt, tools, retrieved context, history, and the response all compete for the same space. Decide the allocation rather than letting history consume everything.
- Retrieve narrowly — Ten highly relevant chunks outperform a hundred marginally relevant ones. Irrelevant context does not sit inertly; it distracts.
- Compact, do not truncate — Dropping the oldest turns loses the decisions that explain the current state. Summarize the history into the facts and decisions that are still live.
- Prune tool results — A tool returning 50 KB of JSON when the agent needs three fields is spending the budget on noise. Filter at the tool boundary.
- Disclose progressively — Provide a file listing, not the files. Let the model request what it actually needs. This is how a large codebase fits in a small window.
- Externalize memory — Long-lived state belongs in a file or a store the model can read and write, not in a conversation history that grows without bound.
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.
- 11d ago First seen · 123 lines · 46 tokens per session scan A 4d4ba3859330
context-engineering is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 23d ago), licensed MIT. It adds 46 tokens to every session and 1,321 once invoked, about $0.0002 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.
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