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 bcastelino/agent-skills-kit --skill context-optimizationgit clone --depth 1 https://github.com/bcastelino/agent-skills-kitWrote 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/bcastelino/agent-skills-kit/context-optimization)<a href="https://agentmods.dev/skills/bcastelino/agent-skills-kit/context-optimization"><img src="https://agentmods.dev/badge/skills/bcastelino/agent-skills-kit/context-optimization/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/bcastelino/agent-skills-kit/context-optimization"><img src="https://agentmods.dev/badge/skills/bcastelino/agent-skills-kit/context-optimization.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 | $0.00030 | $0.01557 |
| Opus 5.5 | $0.00012 | $0.00623 |
| Sonnet 5.5 | $0.00006 | $0.00311 |
| Haiku 4.5 | $0.00003 | $0.00156 |
Grade A, and why
context-optimization 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
98% identical to context-optimization — 10 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use This Skill
Apply compaction, masking, and caching strategies
Use this skill when working with apply compaction, masking, and caching strategies.
Context Optimization Techniques
Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.
When to Activate
Activate this skill when:
- Context limits constrain task complexity
- Optimizing for cost reduction (fewer tokens = lower costs)
- Reducing latency for long conversations
- Implementing long-running agent systems
- Needing to handle larger documents or conversations
- Building production systems at scale
Core Concepts
Context optimization extends effective capacity through four primary strategies: compaction (summarizing context near limits), observation masking (replacing verbose outputs with references), KV-cache optimization (reusing cached computations), and context partitioning (splitting work across isolated contexts).
The key insight is that context quality matters more than quantity. Optimization preserves signal while reducing noise. The art lies in selecting what to keep versus what to discard, and when to apply each technique.
Detailed Topics
Compaction Strategies
What is Compaction Compaction is the practice of summarizing context contents when approaching limits, then reinitializing a new context window with the summary. This distills the contents of a context window in a high-fidelity manner, enabling the agent to continue with minimal performance degradation.
Compaction typically serves as the first lever in context optimization. The art lies in selecting what to keep versus what to discard.
Compaction Implementation Compaction works by identifying sections that can be compressed, generating summaries that capture essential points, and replacing full content with summaries. Priority for compression goes to tool outputs (replace with summaries), old turns (summarize early conversation), retrieved docs (summarize if recent versions exist), and never compress system prompt.
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 · 185 lines · 30 tokens per session scan A 8e1444833ec6
context-optimization is a skill published in the GitHub repository bcastelino/agent-skills-kit (2 stars, last pushed 2mo ago), licensed MIT. It adds 30 tokens to every session and 1,557 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to context-optimization, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
agy-prompting
Internal helper — how to tighten a user request into a sharp prompt for the Antigravity CLI (agy / Gemini 3.x with native web search and agentic tools).
gemini-3-prompting
Guidance for writing effective prompts for Antigravity (agy) / Gemini 3 models.
gemini-proxy
A prompt-polishing mode that sends the next set of substantial requests to Gemini, which rewrites them into clearer, more detailed instructions before the agent responds. Gemini improves the wording but does not replace the conversation or perform the task itself.
ai-engineering-toolkit
Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
researcher
Delegate a deep research or survey task to Google's Antigravity CLI (agy staffer, fast Gemini). Use when the user says /agy:researcher, "ask agy to research", "have the agy staffer survey X", or wants a second, independent deep-dive on a topic or codebase without spending the host model's quota.