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 navendubrajesh/context-management-for-agents --skill context-optimizationgit clone --depth 1 https://github.com/navendubrajesh/context-management-for-agentsWrote 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/navendubrajesh/context-management-for-agents/context-optimization)<a href="https://agentmods.dev/skills/navendubrajesh/context-management-for-agents/context-optimization"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/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/navendubrajesh/context-management-for-agents/context-optimization"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/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 | $0.00072 | $0.01298 |
| Opus 5 | $0.00036 | $0.00649 |
| Sonnet 5 | $0.00014 | $0.00260 |
| Haiku 4.5 | $0.00007 | $0.00130 |
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 9d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Optimization Techniques
Apply tactical techniques to maximize useful information per token within the context window. Context optimization operates at the token level — it is the operational counterpart to the conceptual foundation in context-fundamentals and the failure diagnosis in context-degradation. Every technique trades implementation complexity for token savings.
When to Activate
Activate this skill when:
- Reducing token usage in production agent systems
- Implementing prefix caching for repeated context patterns
- Designing observation masking for tool outputs
- Partitioning context between static and dynamic sections
- Allocating token budgets across context components
- Optimizing retrieval precision to minimize irrelevant content
Do not activate this skill for adjacent work owned by other skills:
- Explaining why token efficiency matters:
context-fundamentals. - Diagnosing specific failure patterns:
context-degradation. - Designing compression strategies for long sessions:
context-compression. - Offloading content to filesystem:
filesystem-context. - Page load and Core Web Vitals regression: GStack
/benchmark— that measures performance, not token savings (see this repo's benchmarks for context metrics).
Core Concepts
Partition context into static and dynamic regions. Static content (system prompt, tool definitions, persistent instructions) benefits from prefix caching — it is computed once and reused across turns. Dynamic content (user messages, tool outputs, retrieved documents) changes every turn and cannot be cached.
Apply observation masking to tool outputs: after processing, replace verbose outputs with compact summaries. The full output served its purpose during the processing turn; subsequent turns need only the conclusions.
Budget tokens explicitly across components. A 128K context window might allocate: 10K for system prompt, 5K for tool schemas, 20K for retrieved documents, 50K for conversation history, and 43K headroom. Monitor actual usage against budget and trigger compaction when any component exceeds its allocation.
What ships with it
1 file 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.
- 9d ago First seen · 117 lines · 72 tokens per session scan A ab155d87af87
context-optimization is a skill published in the GitHub repository navendubrajesh/context-management-for-agents (2 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 1,298 once invoked, about $0.0004 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 2026-08-31.
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