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querying-tophog

Query tophog — the ingestion pipeline's heavy-hitter store in ClickHouse — to identify hot or expensive actors (team_id, distinct_id, session_id, partition) during incident triage. Use when investigating ingestion lag, a hot or lagging Kafka partition, expensive person processing, merge storms, or any "which team or distinct_id is causing this" question. Covers the internal Metabase access path (SSO via hogli), the tophog schema, and the cost-vs-volume query lens. Internal-only: results contain cross-customer identifiers.

75

Quality

94%

Does it follow best practices?

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SecuritybySnyk

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The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

The body is a dense, highly actionable operational reference with real executable queries and a clear access sequence. Its only weakness is minor narrative padding and the absence of an explicit query-validation feedback loop.

Suggestions

Trim the rationale sentences (e.g. 'This is the load-bearing idea' and the Grafana-no-datasource explanation) to pure instruction to lift conciseness toward 5.

Add an explicit validation checkpoint for query results (e.g. 'If the partition dimension returns empty, confirm the deploy has reached the environment before relying on it') to turn the deploy-gate note into a concrete feedback loop.

Consider moving the metric inventory's dated snapshot ('As of 2026-07-06') into a discoverable note or reference so the time-sensitive list does not risk staleness inside the main body.

DimensionReasoningScore

Conciseness

Mostly lean and operational (schema table, discovery + canned queries) without explaining basics Claude already knows, but a few narrative framing sentences ('This is the load-bearing idea', the Grafana-datasource rationale) are justification that could be trimmed.

4 / 5

Actionability

Fully executable, copy-paste-ready SQL queries (cost-ranked actors, partition-scoped, merge storms, generic top-10 with correct type semantics) plus real hogli CLI commands and a discovery query — covering the common triage cases.

5 / 5

Workflow Clarity

The Metabase access flow is a clear numbered sequence with a discovery-first checkpoint and a deploy-gate validation query for the partition dimension, but there is no explicit validate-fix-retry feedback loop for the query workflow.

4 / 5

Progressive Disclosure

Self-contained SKILL.md with clean, well-labeled sections and no bundle files; cross-skill references (querying-production-databases-via-metabase, monitoring-ingestion-pipeline, pganalyze MCP) are clearly signaled in a Related section one level deep.

5 / 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.

The description is specific, trigger-rich, and clearly answers both what the skill does and when to use it, with a distinctive internal-tooling niche. It is a strong, concise frontmatter description with no notable gaps.

DimensionReasoningScore

Specificity

Names concrete capabilities comprehensively — identifying hot/expensive actors across four named key dimensions (team_id, distinct_id, session_id, partition), plus the Metabase access path, tophog schema, and cost-vs-volume query lens.

5 / 5

Completeness

Explicitly answers both what ('Query tophog ... to identify hot or expensive actors') and when ('Use when investigating ingestion lag ...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Natural incident-triage phrases an on-call engineer would actually say — 'ingestion lag', 'hot or lagging Kafka partition', 'expensive person processing', 'merge storms', and the 'which team or distinct_id is causing this' question — with good synonym coverage.

5 / 5

Distinctiveness Conflict Risk

Highly specific niche — the tophog ClickHouse heavy-hitter store accessed via internal Metabase with named metrics — making conflict with other skills minimal.

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
PostHog/posthog
Reviewed

Table of Contents

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