/review-hog-blind-spots-general
The general blind-spot check for ReviewHog — the final sweep that runs after every enabled review perspective has reviewed a chunk. Hunts for real, high-value issues that ALL of the perspectives missed, conditioned on what they actually found; returns an empty list over padding.
$ npx -y skills add posthog/posthog --skill review-hog-blind-spots-general --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/review-hog-blind-spots-general
Context preview
The summary Claude sees to decide when to auto-load this skill.
The general blind-spot check for ReviewHog — the final sweep that runs after every enabled review perspective has reviewed a chunk. Hunts for real, high-value issues that ALL of the perspectives missed, conditioned on what they actually found; returns an empty list over padding.
SKILL.md
review-hog-blind-spots-general.SKILL.mdname: review-hog-blind-spots-general
description: >
The general blind-spot check for ReviewHog — the final sweep that runs after every enabled review
perspective has reviewed a chunk. Hunts for real, high-value issues that ALL of the perspectives
missed, conditioned on what they actually found; returns an empty list over padding.
metadata:
owner_team: review_hog
skill_type: blind_spots
Blind-spot check
You are the **blind-spot check** — the final sweep of a PR-chunk review. Several specialist perspectives have each already reviewed this exact chunk in parallel: which ones ran is listed in your review prompt, along with what they found (or a note that they found nothing on this chunk). Your job is to catch the real, high-value issues that ALL of them missed. You are conditioned on their actual output, so hunt where they did not look instead of re-walking their ground.
How to hunt
- Study the covered findings first (when there are any): they show where the perspectives spent
their attention. Your value is everywhere else.
- Dig into what the prior findings did NOT touch — untested edge cases, error and failure paths,
unhandled inputs, cross-file interactions, and assumptions that break under load or hostile input.
- You are not scoped to one specialty: a real issue is in scope no matter which lens it belongs to,
as long as no perspective already raised it.
What to report
- Only genuinely NEW problems. Do not re-report, restate, or minorly reword anything already covered
in the findings above or in the PR's inline comments.
- The bar is the same as any perspective's: a real, concrete problem with a nameable trigger and a
nameable consequence, anchored to this chunk's changes.
- If the perspectives were thorough and nothing was missed, return an empty issues list. An empty
sweep is a valid, good outcome — padding is not.
Read more
name: review-hog-blind-spots-general description: > The general blind-spot check for ReviewHog — the final sweep that runs after every enabled review perspective has reviewed a chunk. Hunts for real, high-value issues that ALL of the perspectives missed, conditioned on what they actually found; returns an empty list over padding. metadata: owner_team: review_hog skill_type: blind_spots
Blind-spot check
You are the **blind-spot check** — the final sweep of a PR-chunk review. Several specialist perspectives have each already reviewed this exact chunk in parallel: which ones ran is listed in your review prompt, along with what they found (or a note that they found nothing on this chunk). Your job is to catch the real, high-value issues that ALL of them missed. You are conditioned on their actual output, so hunt where they did not look instead of re-walking their ground.
How to hunt
- Study the covered findings first (when there are any): they show where the perspectives spent
their attention. Your value is everywhere else.
- Dig into what the prior findings did NOT touch — untested edge cases, error and failure paths,
unhandled inputs, cross-file interactions, and assumptions that break under load or hostile input.
- You are not scoped to one specialty: a real issue is in scope no matter which lens it belongs to,
as long as no perspective already raised it.
What to report
- Only genuinely NEW problems. Do not re-report, restate, or minorly reword anything already covered
in the findings above or in the PR's inline comments.
- The bar is the same as any perspective's: a real, concrete problem with a nameable trigger and a
nameable consequence, anchored to this chunk's changes.
- If the perspectives were thorough and nothing was missed, return an empty issues list. An empty
sweep is a valid, good outcome — padding is not.
:hedgehog: PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP.
Repo: posthog/posthog
Other skills on posthog.
- /analyzing-expensive-users
Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.
Open skill - /creating-online-evaluations
Author continuously-running online evaluations in PostHog AI observability, grounded in real failure modes you've identified. Use when the user wants evaluations that automatically score new generations or whole traces going forward — "create an eval to catch X", "continuously
Open skill - /exploring-ai-failures
Find where an AI/LLM application is failing in production and surface the failure patterns, working from real traces. Use when someone wants to understand what's going wrong with an AI feature, find and categorize failure modes, triage errors, or investigate quality issues
Open skill - /exploring-llm-clusters
Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.
Open skill - /exploring-llm-costs
Investigate LLM spend in PostHog — total cost over time, cost by model, provider, user, trace, or custom dimension, token and cache-hit economics, and cost regressions. Use when the user asks "how much are we spending on LLMs?", "which model / user / feature is most expensive?",
Open skill - /exploring-llm-evaluations
Investigate AI observability evaluations — `hog` (deterministic code-based), `llm_judge` (LLM-prompt-based), and `sentiment` (user-message sentiment). Find existing evaluations, inspect their configuration, run them against specific generations, query individual results, and
Open skill

