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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.

80

Quality

100%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

100%

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

A dense, well-structured operational skill: executable examples, a sequenced investigation workflow with baselines and error recovery, and clean single-file organization with no padding of general knowledge. It earns its length with product-specific detail Claude would not already know.

DimensionReasoningScore

Conciseness

Every section carries non-obvious product-specific detail (endpoint params, exception-code table, job states, skip-reason semantics) rather than concepts Claude already knows; no padded prose, matching the lean/efficient level-3 anchor.

3 / 3

Actionability

Provides copy-paste-ready bash with exact endpoints, headers (`Authorization: Bearer $POSTHOG_QUERY_PERF_PAT_US`), env-var names, and jq projections, plus a 403 error-recovery note — fully executable, not pseudocode.

3 / 3

Workflow Clarity

The numbered "Investigation workflow" sequences headline → localize → drill-to-ground-truth, with explicit "Healthy looks like" baselines and 403/env-var checkpoints; this read-only investigation does not hit the destructive-ops cap.

3 / 3

Progressive Disclosure

No bundle files exist (references/scripts/assets are empty), and the single SKILL.md is organized into clearly-headed sections (Environment, Authentication, Endpoints, Field semantics, Example calls, Investigation workflow, Known limitations, Maintenance) with one-level pointers to other skills/MCPs and no nested references.

3 / 3

Total

12

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12

Passed

Description

100%

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 high-quality description: specific actions, explicit trigger guidance, third-person voice, and a tightly scoped niche that minimizes conflict risk. It concisely conveys both capability and when-to-use without padding.

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 specific auth scope and param/field coverage, matching the level-3 anchor of several specific concrete actions.

3 / 3

Completeness

It explicitly answers both what (pull/interpret query-performance data across three endpoints, params, and field semantics) and when (an explicit "Use when investigating..." clause), meeting the level-3 anchor for both what AND when.

3 / 3

Trigger Term Quality

The "Use when..." clause surfaces natural staff phrasing — "slow or failing experiment queries", "precompute regressions", "307/159/241 errors", "experiment query performance", "precompute rollout" — giving good coverage of terms users would actually say for this niche.

3 / 3

Distinctiveness Conflict Risk

The niche is sharply bounded — staff-only `/api/debug_ch_queries` endpoints, prod-US/EU, specific error codes 307/159/241 — so it is unlikely to trigger for the wrong skill; not the level-2 case that could overlap with similar skills.

3 / 3

Total

12

/

12

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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