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 siviter-xyz/dot-agent --skill context-engineeringgit clone --depth 1 https://github.com/siviter-xyz/dot-agentWrote 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/siviter-xyz/dot-agent/context-engineering)<a href="https://agentmods.dev/skills/siviter-xyz/dot-agent/context-engineering"><img src="https://agentmods.dev/badge/skills/siviter-xyz/dot-agent/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/siviter-xyz/dot-agent/context-engineering"><img src="https://agentmods.dev/badge/skills/siviter-xyz/dot-agent/context-engineering.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 | $0.00075 | $0.00723 |
| Opus 5.5 | $0.00030 | $0.00289 |
| Sonnet 5.5 | $0.00015 | $0.00145 |
| Haiku 4.5 | $0.00007 | $0.00072 |
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 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage.
When to Activate
- Designing/debugging agent systems
- Context limits constrain performance
- Optimizing cost/latency
- Building multi-agent coordination
- Implementing memory systems
- Evaluating agent performance
- Developing LLM-powered pipelines
Core Principles
- Context quality > quantity - High-signal tokens beat exhaustive content
- Attention is finite - U-shaped curve favors beginning/end positions
- Progressive disclosure - Load information just-in-time
- Isolation prevents degradation - Partition work across sub-agents
- Measure before optimizing - Know your baseline
Key Metrics
- Token utilization: Warning at 70%, trigger optimization at 80%
- Token variance: Explains 80% of agent performance variance
- Multi-agent cost: ~15x single agent baseline
- Compaction target: 50-70% reduction, <5% quality loss
- Cache hit target: 70%+ for stable workloads
Four-Bucket Strategy
- Write: Save context externally (scratchpads, files)
- Select: Pull only relevant context (retrieval, filtering)
- Compress: Reduce tokens while preserving info (summarization)
- Isolate: Split across sub-agents (partitioning)
Anti-Patterns
- Exhaustive context over curated context
- Critical info in middle positions
- No compaction triggers before limits
- Single agent for parallelizable tasks
- Tools without clear descriptions
Guidelines
- Place critical info at beginning/end of context
- Implement compaction at 70-80% utilization
- Use sub-agents for context isolation, not role-play
- Design tools with clear descriptions (what, when, inputs, returns)
- Optimize for tokens-per-task, not tokens-per-request
- Validate with probe-based evaluation
- Monitor token usage in production
- Start minimal, add complexity only when proven necessary
What ships with it
8 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 · 82 lines · 75 tokens per session scan A ffd61033c12c
context-engineering is a skill published in the GitHub repository siviter-xyz/dot-agent (22 stars, last pushed 7mo ago), licensed MIT. It adds 75 tokens to every session and 723 once invoked, about $0.0003 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.
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