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diagnosing-endpoint-performance

Diagnose why a PostHog endpoint is slow or expensive and propose a concrete fix — bump the cache TTL, enable materialisation, restructure variables, or rewrite the query. Use when the user says "this endpoint is slow", "my endpoint times out", "we're hitting the cost cap on this one", or asks "should I materialise this?". Focuses on a single named endpoint, not a project-wide audit.

76

Quality

94%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

88%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 high-quality, actionable skill body with a well-sequenced decision tree, executable tool calls and SQL, and explicit validation gates around the destructive rewrite step. The main improvement is trimming redundancy between the decision tree and the Important notes summary.

Suggestions

Trim the "Important notes" section: points about cache-first, materialisation costs, and SLA-dependence already appear in the decision tree — keep only the notes that add new information (the three usage signals, the agent-feedback nudge).

Consider moving the Step 3 rejection-pattern bullets (cohort breakdown, JOIN+variables, missing variables, HogQL *) into a short reference table or a references file, leaving the main decision tree leaner.

The Example interaction block is somewhat narrative; condensing it to the key diagnostic outputs and the final recommendation would reduce tokens without losing the teaching value.

DimensionReasoningScore

Conciseness

Mostly efficient and dense with product-specific knowledge Claude would not already have (TTL enum values, materialisation eligibility rules), but the "Important notes" section restates several decision-tree points (cache first, materialisation costs) that could be trimmed.

4 / 5

Actionability

Fully executable guidance throughout: named tool calls (endpoint-get, endpoints-materialization-preview, etc.), a copy-paste-ready query_log SQL query, concrete enum values, and a decision tree that prescribes specific actions per branch.

5 / 5

Workflow Clarity

Clear sequenced decision tree ("walk these in order — the first one that applies is the cheapest fix") plus a 6-step Workflow, with explicit validation gates for the destructive rewrite flow (run original and rewrite, compare results, only then apply) and confirmation before applying fixes.

5 / 5

Progressive Disclosure

Well-organized into clearly headed sections (When to use, Available tools, Decision tree, Workflow, Example, Important notes) with no nested references, but it is a single ~190-line file with no external references; some detail (e.g. the Step 3 rejection-pattern bullets) could optionally be split out.

4 / 5

Total

18

/

20

Passed

Description

100%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 strong description: it names concrete actions, quotes natural user trigger phrases, answers both what and when, and scopes a distinct niche. No changes needed.

DimensionReasoningScore

Specificity

Lists multiple concrete fix actions — "bump the cache TTL, enable materialisation, restructure variables, or rewrite the query" — giving comprehensive coverage of the fix space rather than vague language.

5 / 5

Completeness

Explicitly answers both what (diagnose and propose a concrete fix, with the four fix types named) and when (a "Use when..." clause with concrete trigger phrases), matching the anchor example closely.

5 / 5

Trigger Term Quality

Quotes verbatim natural phrases users would actually say — "this endpoint is slow", "my endpoint times out", "we're hitting the cost cap on this one", "should I materialise this?" — covering the main symptom variants.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche — "Focuses on a single named endpoint, not a project-wide audit" — and is explicitly contrasted with auditing-endpoints, giving it distinct triggers and minimal conflict risk.

5 / 5

Total

20

/

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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