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Exploring Endpoint Execution Logs

Free

Diagnose issues with PostHog endpoint logs efficiently.

by posthog37.6k stars on posthog/posthog
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Updated Aug 11, 2026
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What Exploring Endpoint Execution Logs does

The Exploring Endpoint Execution Logs skill provides a focused approach to diagnosing issues with specific PostHog endpoints by analyzing their execution logs. Each invocation of an endpoint generates a log entry that captures critical information, including success or failure status, execution duration, cache usage, and the number of rows returned. This skill allows users to query these logs to understand what happened during the execution of an endpoint, making it invaluable for troubleshooting errors and performance issues.

When users encounter problems such as an endpoint failing or returning unexpected results, they can utilize this skill to retrieve relevant log entries. The skill supports filtering logs by various parameters, including log level (INFO or ERROR), search terms, timestamps, and instance IDs. This enables users to pinpoint specific issues, such as identifying error messages or determining whether the endpoint utilized cached results. The structured log entries, which include key-value pairs, provide detailed insights into the execution path and any errors encountered, allowing for effective diagnosis of the root cause.

This skill is particularly useful for developers and data engineers who work with PostHog and need to quickly assess the health and performance of their endpoints. By focusing on individual endpoint logs rather than broader project audits, users can efficiently troubleshoot specific problems without being overwhelmed by extraneous information. Whether it's investigating a recent failure or analyzing trends in execution performance, this skill offers the necessary tools to enhance endpoint reliability and performance.

In summary, the Exploring Endpoint Execution Logs skill is a targeted solution for diagnosing endpoint issues within PostHog, providing users with the ability to quickly access and analyze execution logs to resolve problems effectively.

When to use it

Use this skill when you need to investigate specific errors or performance issues related to a single PostHog endpoint.

When not to use it

Avoid using this skill for project-wide audits or performance profiling, as it is designed for individual endpoint analysis only.

What you can build with it

Diagnosing a Failing Endpoint

When an endpoint starts failing, use this skill to retrieve recent logs and identify the error messages.

Analyzing Cache Performance

Check if an endpoint is hitting the cache by searching for cache-related tokens in the logs.

Investigating Unexpected Results

Compare the number of rows returned across different runs to identify potential issues with data consistency.

How to install Exploring Endpoint Execution Logs

View source

1. Install with the skills CLI

npx skills add posthog/posthog/exploring-endpoint-execution-logs --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by posthog

Exploring endpoint execution logs

Every endpoint run emits one execution log entry to PostHog's log_entries store. This skill reads those entries for a specific endpoint to answer "what happened when it ran?". It is the log-level counterpart to diagnosing-endpoint-performance (which reasons about cache/materialisation strategy from config and query_log).

When to use this skill

  • "Why is my endpoint failing / erroring?"
  • "Show me the logs / recent runs for endpoint X"
  • "Did the last run hit cache? How many rows did it return?"
  • "What happened the last time endpoint Y ran?"

If the question is "this endpoint is slow, what should I change?", use diagnosing-endpoint-performance. If it's project-wide ("what can I clean up?"), use auditing-endpoints.

What an execution log entry looks like

Each run produces exactly one entry. The level is INFO on success and ERROR on failure, and the message carries the extra data as searchable key=value tokens:

Endpoint executed · path=materialized cache=hit duration_ms=142 rows=1024 version=3
Endpoint execution failed · path=inline error=ResolutionError version=3

Token meanings:

TokenValuesMeaning
pathmaterialized / inline / ducklake / ducklake_fallbackWhich execution path ran
cachehit / missWhether the query result cache was used (omitted for ducklake)
duration_msintegerWall-clock execution time
rowsintegerNumber of result rows returned
versionintegerWhich endpoint version ran
errore.g. ResolutionError, HogVMExceptionError class / HogQL code name (failures only)

Each run gets a distinct instance_id, so logs group one-per-execution in the viewer.

Available tools

ToolPurpose
endpoint-logsPrimary. Execution log entries for one endpoint by name. Filter by level, search, time range, instance_id; limit up to 500.
endpoint-getEndpoint config for context (current version, materialisation, query kind)
execute-sqlFallback / aggregation directly against log_entries (log_source='endpoints')

Filtering

endpoint-logs exposes the standard log filters:

  • level — comma-separated, e.g. ERROR to see only failed runs, or INFO,ERROR for all.
  • search — case-insensitive substring over the message. Because the extra data is in key=value tokens, you can search cache=miss, path=inline, error=ResolutionError, or a specific version=3.
  • after / before — ISO timestamps to bound the time range.
  • instance_id — pin a single execution.
  • limit — 1–500 (default 50).

Workflow

  1. Identify the endpoint by name. If given a URL, parse it from /api/projects/{team_id}/endpoints/{name}/run.

  2. Start broad: endpoint-logs for the endpoint with a recent time range. Skim levels and tokens.

  3. Narrow to the symptom:

    • Failures → level=ERROR; read the error= token and path= to see where it broke.
    • Cache concerns → search=cache=miss to see how often runs miss cache.
    • Wrong results → compare rows= across runs, and version= to spot a regression after a version bump.
  4. For counts/trends across many runs (e.g. error rate over a week), drop to execute-sql against log_entries:

    SELECT toDate(timestamp) AS day, upper(level) AS level, count() AS runs
    FROM log_entries
    WHERE log_source = 'endpoints' AND log_source_id = '<endpoint_uuid>'
    GROUP BY day, level ORDER BY day DESC
    

    Get the endpoint UUID from endpoint-get (the log_source_id is the endpoint id, not its name).

  5. Summarize: what's failing, since when, on which version/path, and whether it's a config issue (hand off to diagnosing-endpoint-performance) or a query bug.

Example interaction

User: "weekly_signups started erroring this morning"

Agent steps:
- endpoint-logs weekly_signups, level=ERROR, after=<this morning>
  → several "Endpoint execution failed · path=inline error=ResolutionError version=5"
- endpoint-get weekly_signups → current version is v5 (bumped today)
- endpoint-logs weekly_signups, level=INFO, before=<this morning>
  → prior runs: "path=inline cache=hit ... version=4" succeeded

- "v5 (created this morning) is failing with a ResolutionError on the inline path — it can't
   resolve a table or field reference. v4 ran fine. This looks like a bad query in the new
   version. Want me to pull the v5 query (endpoint-versions) so we can fix it, or roll back to v4?"

Important notes

  • One entry per run. Don't expect step-by-step traces — endpoints log a single completion line. The detail lives in the tokens, not in multiple lines.
  • log_source_id is the endpoint UUID, not the name. For execute-sql, fetch it via endpoint-get first.
  • Logs are retained ~90 days (the log_entries TTL). Older runs won't appear.
  • Execution logs ≠ query performance. endpoint-logs tells you what happened and why a run failed; for "should I materialise / bump cache TTL?" use diagnosing-endpoint-performance, which reasons over config and query_log cost metrics.
  • Best-effort emission. A log line is emitted after each run but never blocks it — if a run succeeded for the caller but no log shows, the emit was dropped, not the query.

Frequently asked questions about Exploring Endpoint Execution Logs

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