Review Hog Authoring

by PostHog469d1773e9cbNo licenseListed Oct 8, 2026Updated Oct 8, 2026

How to author custom PostHog Review skills: the review perspectives, blind-spot checks, validation criteria, and resolution criteria that drive PostHog Review's automated PR reviews. Use when a user wants a new review perspective (a specialist lens on their PRs), a custom blind-spot sweep, their own validation bar for which findings get published, or their own bar for which review comments get implemented. Trigger on "create a PostHog Review perspective", "custom review perspective", "my own blind-spot check", "custom validation criteria", "custom resolution criteria", "tune what PostHog Review publishes", "tune what PostHog Review implements".

Instructions onlyAI & Agents
AI-generated overview

Guides authoring custom PostHog Review skills: review perspectives, blind-spot checks, validation and resolution criteria.

What it does
This skill is an authoring guide for creating custom PostHog Review skills, the LLMSkill rows that drive PostHog's automated pull-request reviews. It explains the four kinds of review skills, their naming contracts and cardinality, and gives per-kind drafting guidance for review perspectives, blind-spot checks, validation criteria and resolution criteria. It also describes an authoring flow: inspect existing review-hog skills over MCP, interview the user, draft the body, create the skill with the skill-create tool, and tell the user how to activate it. It produces new team-level skill rows rather than a document or file.
When to use it
Use it when a user wants a new specialist review perspective for their pull requests, a custom blind-spot sweep, or their own bar for which findings get published or which review comments get implemented. It is also relevant when tuning what PostHog Review publishes or implements.
Requirements
Requires access to the PostHog MCP skill tools (skill-list, skill-get, posthog:skill-create, posthog:skill-update) and a PostHog team context. No scripts ship with the skill; it is instructions only.

Authoring PostHog Review skills

PostHog Review is PostHog's automated PR reviewer. A review splits the PR into chunks, then for each chunk runs every enabled perspective in parallel (independent specialist lenses), a single blind-spot check afterwards (a final sweep conditioned on what the perspectives found), and finally judges every surviving candidate finding against one validation criteria skill — only findings that pass get published to the pull request. After a published review, the resolution stage goes back over the PR's unresolved review threads and judges each against one resolution criteria skill — worth-and-safe asks get implemented on the PR branch, every thread gets a reply.

All four kinds are team LLMSkill rows the review agents pull over MCP at run time. PostHog ships canonicals; this skill is the guide for authoring custom ones. The skill itself is team-level; whether it runs is a per-user setting in Inbox → Code review.

KindName contractCardinality per userCanonical example
Review perspectivereview-hog-perspective-<slug>Multi-enable, at least one stays onreview-hog-perspective-logic-correctness
Blind-spot checkreview-hog-blind-spots-<slug>Exactly one active; selecting swapsreview-hog-blind-spots-general
Validation criteriareview-hog-validation-<slug>Exactly one active; selecting swapsreview-hog-validation-criteria
Resolution criteriareview-hog-resolution-<slug>Exactly one active; selecting swapsreview-hog-resolution-criteria

Authoring flow

  1. Ground yourself. Using the PostHog MCP skill tools, skill-list the team's review-hog-* skills and skill-get the canonical of the kind you're authoring (see the table above) — it is the reference for structure and tone. For a perspective, skim the descriptions of every existing review-hog-perspective-* so the new lens doesn't re-cover ground an enabled one already owns (overlap gets deduplicated later, but it wastes review passes).
  2. Interview the user. Ask what the skill should focus on, and offer a few concrete directions the current set doesn't cover — grounded in what you saw in step 1 and, when useful, in the project itself. Don't start writing until the direction is picked.
  3. Draft the body following the per-kind guidance below. Keep it a focused instruction set the review agent can apply to one chunk in one pass — not an essay.
  4. Create the skill yourself with posthog:skill-create — actually create the team LLMSkill row; never hand the user a body to copy-paste. Pass the exact name per the contract above (lowercase slug), a one-paragraph description of what the lens/sweep/bar is, and the body. The name prefix is the whole identity — it is how the Code review tab and the review runs discover the skill. There is no category parameter on the skill tools and you don't need one: the backend stamps the review_hog grouping category itself (it only affects grouping on the Skills page) — do not spend turns trying to set or verify it. Iterate with posthog:skill-update if the user wants changes. Author fresh — don't skill-duplicate a canonical to edit: seeded metadata rides along with the copy, and the canonical sync may overwrite or prune it.
  5. Tell the user how to activate it. A custom skill starts inactive for them:
    • Perspective — toggle it on under Inbox → Code review → Perspectives (it appears disabled until they enable it; at least one perspective must stay on).
    • Blind-spot check / validation criteria / resolution criteria — select it under the matching section; exactly one runs at a time, so selecting it swaps out the current one for their reviews only. Reviews pin skill versions when a run starts, so an edit mid-review applies from the next run.

Writing a review perspective

The body instructs one specialist review pass over one PR chunk. Match the canonical logic-correctness skill's shape:

  • The lane — one sentence on what this lens is responsible for; report everything in lane and leave the rest to the other perspectives.
  • Hunting grounds — a numbered handful of concrete places to look, each a specific check the agent can walk against the chunk ("transaction boundaries that split writes that must land together"), not an abstract virtue ("ensure correctness").
  • Lane boundary — which perspective owns each adjacent concern this lens must leave alone.
  • The finding bar — a publishable finding names the concrete trigger and the concrete consequence; close with a completion criterion ("done when every changed file is flagged or cleared against every hunting ground").

The review harness already tells the agent the pipeline mechanics — parallel perspectives, later deduplication, severity levels, the non-test-files rule — so the skill carries only the lens; restating harness rules dilutes it.

Writing a blind-spot check

The body instructs the final sweep that runs after every enabled perspective finished a chunk. It is conditioned on the covered findings (the prompt lists which perspectives ran and what they found), so the body should say how to use that: the covered findings map where attention already went, and the sweep's value is the negative space — error paths, unhandled inputs, cross-file interactions, assumptions. It is not scoped to one specialty, and an empty result beats padding. A custom sweep narrows or re-weights this hunt (e.g. toward a domain the team keeps getting burned by).

Writing validation criteria

The body defines the keep/drop bar every candidate finding is judged against before publishing. Precision over recall is the house default — a reviewer that raises noise gets muted — so define: what makes a finding real and worth an author's attention (user-affecting correctness, security, data loss, contract breaks, performance), what gets dropped (overengineering, speculation, defensive paranoia, unreachable edges, style), and how to treat genuine uncertainty (default: drop). A custom bar shifts strictness or re-weights concerns; it should still demand evidence from the live codebase, not vibes.

Writing resolution criteria

The body defines the bar the resolution stage applies to each unresolved review thread on a PR: worth implementing (a real improvement the PR should carry, in scope for what it changes) and safe to implement unattended (small blast radius, no contract or API changes, no cross-cutting rewrites, verifiable locally). Define what gets implemented, what gets a reasoned decline (overengineering asks, scope creep, style-only churn, requests better served by a follow-up), and what escalates to a human. The harness owns the hard floors — human-authored threads are never resolved by the bot, escalations never resolve a thread, replies always explain the decision — so a custom skill may tighten the bar or re-weight what counts as worth it, never loosen those floors.

Source and attribution

Source:PostHog/ai-plugininskills/review-hog-authoringat commit469d177

License: No license

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Review Hog Authoring Agent Skill | SourceWeft