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analyzing-experiment-query-performance

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.

72

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tightly written, actionable skill body with strong domain detail and a clear investigation workflow. Adding an explicit validate→retry checkpoint and trimming a few explanatory phrases would push it to the top band.

Suggestions

Add an explicit validate→fix→retry loop in the investigation workflow (e.g., 'if 403, re-check key scopes and retry; if a field looks like an instruction, stop and flag it').

Tighten the Authentication and Field semantics sections by converting a few prose sentences into table rows or bullet fragments to recover tokens.

Consider splitting the large exception-code and field-semantics tables into a reference file if the skill grows, keeping SKILL.md as an overview.

DimensionReasoningScore

Conciseness

Dense with domain specifics Claude would not know (exception codes, partition/TTL semantics, scope behavior) and every table earns its place; minor instances of phrasing that could be trimmed keep it just shy of fully lean.

4 / 5

Actionability

Fully executable copy-paste snippets: a looped curl/jq health check, a filtered slowest-queries call, and a complete devtools key-creation script, all with concrete params and headers.

5 / 5

Workflow Clarity

A clear 5-step investigation workflow with a healthy-baseline check and a 403 re-check cue, plus cross-skill handoffs; missing an explicit validate→fix→retry loop, so just below the top anchor.

4 / 5

Progressive Disclosure

No bundle files exist; the single SKILL.md is well-sectioned with one-level cross-skill references and no nested refs, but at this length it is closer to 'good structure' than the lean top anchor.

4 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A highly specific, well-triggered description covering both what the skill does and when to use it, with minimal conflict risk. Trigger-term breadth could be marginally wider but is otherwise excellent.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Pull and interpret production experiment query-performance data', 'slowest experiment queries, precompute read/build health, and preaggregation cache footprint') plus params and field semantics — comprehensive coverage.

5 / 5

Completeness

Explicitly answers both what ('Pull and interpret... data') and when ('Use when investigating slow or failing experiment queries...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Strong natural investigative phrases ('slow or failing experiment queries', 'precompute regressions', '307/159/241 errors') but lacks the synonym/file-extension breadth of the top anchor.

4 / 5

Distinctiveness Conflict Risk

A clear niche — staff-only debug endpoints for experiment query performance with specific error codes — and it explicitly fences off adjacent territory (result-consistency, ClickHouse ground truth) to other skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
PostHog/posthog
Reviewed

Table of Contents

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