context-engineering
context-engineering is a Context window & token budget skill for AI agents, published by liza-mas in the liza repository on GitHub. Skillspace rates context-engineering D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Analyze §BRAND_NAME_TITLE§ `§BRAND_PROJECT_DIRNAME§/agent-prompts/` and `§BRAND_PROJECT_DIRNAME§/agent-outputs/` from a context-engineering perspective: prompt payload shape, context budget use, cacheability, duplicated or missing context, instruction hierarchy, tool-output pressure, role-specific context fit, and prompt-output feedback loops. Use when diagnosing agent context bloat, prompt drift, poor agent handoffs, repeated misunderstandings, excessive tool output, or whether §BRAND_NAME_TITLE§ agents received the right information at the right time.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 322
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 96 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- 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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