analyzing-experiment-query-performance
analyzing-experiment-query-performance is a Backend performance & caching skill for AI agents, published by PostHog in the posthog repository on GitHub. Skillspace rates analyzing-experiment-query-performance D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Pull and interpret production experiment query-performance data from the staff-only `/api/debug_ch_queries` endpoints backing the `/instance/query_performance` scene: slowest experiment queries, precompute read/build health, and preaggregation cache footprint. Covers prod-US and prod-EU via a `query_performance:read` personal API key, all query params, and response field semantics (exception codes, exposure paths, precompute skip reasons, job states). Use when investigating slow or failing experiment queries, precompute regressions, 307/159/241 errors, preaggregation table growth, or when asked how experiment query performance or the precompute rollout is doing in production.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 35,560
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
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 1 other repositories, in 1 collections
- Scope
- Repo-internal: written for the repository it lives in. Staff-only PostHog endpoints, prod clusters and internal tables
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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