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exploring-llm-traces

ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.

75

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

The body is highly actionable with executable tool calls, scripts, and SQL, a clear sequenced debug workflow, and well-signaled one-level-deep references. The main weakness is conciseness: the parsing scripts and key warnings are restated multiple times and could be consolidated.

Suggestions

Consolidate the parsing-script coverage: keep the inline bash block in Step 3 OR the full table under "Parsing large trace results", and cross-reference the other rather than maintaining both.

State the posthog.ai_events heavy-content rule once in the body (e.g. in the Tips or event reference link) and link out for detail instead of repeating it across the description, Step 2, and the Tips section.

Reduce repeated _posthogUrl reminders to a single authoritative callout (e.g. one Tip bullet) and reference it where needed.

DimensionReasoningScore

Conciseness

Mostly efficient and free of concept-explanation fluff, but the parsing scripts are duplicated (inline bash block in Step 3 and again as a full table under "Parsing large trace results"), and the posthog.ai_events and _posthogUrl points are repeated several times, so it could be tightened.

2 / 3

Actionability

Provides fully executable JSON tool-call payloads, concrete bash invocations with env vars, and complete HogQL/SQL examples that are copy-paste ready, matching the anchor for executable, specific guidance.

3 / 3

Workflow Clarity

The URL-to-debug workflow is a clearly sequenced three-step process, and the schema-discovery-first pattern acts as an explicit pre-filter checkpoint; operations are read-only so the destructive-operation feedback-loop cap does not apply.

3 / 3

Progressive Disclosure

SKILL.md is a clear overview pointing one level deep to real bundle files (events-and-properties.md, example-llm-trace.md.j2, example-llm-traces-list.md, scripts/*) with a navigable scripts table; reference cross-links are sibling-level rather than deeply nested.

3 / 3

Total

11

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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.

The description is specific, trigger-rich, and complete: it names concrete debugging/inspection actions, enumerates natural user triggers including URL-paste and SQL-fallback scenarios, and carves out a distinct niche. Third-person voice is maintained throughout.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ("debug and inspect…traces", "check if an agent used a tool correctly", "inspect subagent behavior", "analyze token usage and costs") rather than vague language, matching the anchor for multiple specific concrete actions.

3 / 3

Completeness

Explicitly states both what ("debug and inspect LLM/AI agent traces using PostHog's MCP tools") and when ("Use when the user pastes a trace or session URL…asks to debug a trace…") with explicit triggers.

3 / 3

Trigger Term Quality

Covers natural phrasing a user would actually say ("pastes a trace or session URL", "debug a trace", "figure out what went wrong", "token usage and costs") with good breadth of variations.

3 / 3

Distinctiveness Conflict Risk

A clear niche (PostHog LLM/AI agent traces via MCP tools, plus the dedicated posthog.ai_events table) with distinct triggers that are unlikely to fire for unrelated 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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