Borrowing it
Nothing to install: this file belongs to brake71/1c-ssl-skills. 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/brake71/1c-ssl-skills/main/.agents/skills/context-optimization/SKILL.mdgit clone --depth 1 https://github.com/brake71/1c-ssl-skillsWrote 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/brake71/1c-ssl-skills/context-optimization)<a href="https://agentmods.dev/skills/brake71/1c-ssl-skills/context-optimization"><img src="https://agentmods.dev/badge/skills/brake71/1c-ssl-skills/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/brake71/1c-ssl-skills/context-optimization"><img src="https://agentmods.dev/badge/skills/brake71/1c-ssl-skills/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 | $0.00047 | $0.03115 |
| Opus 5.5 | $0.00019 | $0.01246 |
| Sonnet 5.5 | $0.00009 | $0.00623 |
| Haiku 4.5 | $0.00005 | $0.00312 |
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 17d 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
92% identical to context-optimization — 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Optimization Techniques
Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. Effective optimization increases useful capacity without requiring larger models or longer windows — but only when applied with measurement discipline. The techniques below are ordered by impact and risk.
When to Activate
Activate this skill when:
- Context budgets or token costs constrain task complexity
- Observation masking can replace verbose tool outputs with retrievable references
- Prefix or KV-cache hit rate needs improvement
- Retrieval scoping can reduce irrelevant loaded context
- Context partitioning can extend effective capacity across agents
- Budget triggers are needed for masking, compaction, or partitioning
Do not activate this skill for adjacent work owned by other skills:
- Explaining why attention or context windows behave this way:
context-fundamentals. - Diagnosing active lost-in-middle, poisoning, distraction, confusion, or clash:
context-degradation. - Designing a structured handoff summary for a long conversation:
context-compression. - Storing large outputs, plans, or logs as files:
filesystem-context. - Model-initiated rewrites of its own context and their re-prefill cost:
self-managed-context.
Core Concepts
Apply four primary strategies in this priority order:
-
KV-cache optimization — Reorder and stabilize prompt structure so the inference engine reuses cached Key/Value tensors. This is the cheapest optimization when the runtime supports prefix caching: low quality risk, immediate cost and latency savings. Apply it first when stable prefixes exist.
-
Observation masking — Replace verbose tool outputs with compact references once their purpose has been served. Tool outputs can dominate agent trajectories (claim-context-optimization-tool-output-dominance), so masking often yields the largest capacity gains. The original content remains retrievable if needed downstream.
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.
- 17d ago First seen · 222 lines · 47 tokens per session scan A 96fbd47c16d1
context-optimization is a skill published in the GitHub repository brake71/1c-ssl-skills (82 stars, last pushed 15d ago), licensed MIT. It adds 47 tokens to every session and 3,115 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to context-optimization, differing in 0 lines, and is treated as a copy.
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