dev-guide-cache-hit-rate
dev-guide-cache-hit-rate is an LLM evaluation & observability skill for AI agents, published by Lingtai-AI in the lingtai repository on GitHub. Skillspace rates dev-guide-cache-hit-rate D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Nested lingtai-dev-guide reference for computing the recent prompt-cache hit rate from LingTai token ledgers over rolling windows (default 1h / 5h / 1d / 3d). Explains the provider-agnostic input/cached fields in logs/token_ledger.jsonl, the exact formula (sum(cached)/sum(input) per window), timestamp/timezone handling, the daemon double-count hazard, and ships a read-only stdlib script (scripts/cache_hit_rate.py) that aggregates an agent workdir, a project root, or a single ledger file. Use when asked how effective prompt caching has been recently, or to diagnose a cache-hit-rate drop after a refresh/affinity/cache-key change.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 701
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
- 84 / 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
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.