context-optimization
context-optimization is a Context window & token budget skill for AI agents, published by muratcankoylan in the Agent-Skills-for-Context-Engineering repository on GitHub. Skillspace rates context-optimization C (70/100) and it passed the safety scan; a judge model has also reviewed it.
This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.
- Grade
- C
- Quality
- 70 / 100
- Safety
- Passed
- GitHub stars
- 17,968
Placed #1 of 6 in the judged category final for Context window & token budget.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 99 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
Judge review
Placed #1 of 6 in the judged category final for Context window & token budget.
- Trigger
88 88 - Instructions
90 90 - Specificity
92 92 - Safety
90 90 - Disclosure
88 88 - Maintenance
88 88
A rigorously quantified optimization playbook: priority-ordered strategies with measurable targets and an explicit rule to never compress the system prompt.
What the scanner flagged
- mentions secrets in text
Not checked yet
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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