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".
69
85%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Passed
No findings from the security scan
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.
| Kind | Name contract | Cardinality per user | Canonical example |
|---|---|---|---|
| Review perspective | review-hog-perspective-<slug> | Multi-enable, at least one stays on | review-hog-perspective-logic-correctness |
| Blind-spot check | review-hog-blind-spots-<slug> | Exactly one active; selecting swaps | review-hog-blind-spots-general |
| Validation criteria | review-hog-validation-<slug> | Exactly one active; selecting swaps | review-hog-validation-criteria |
| Resolution criteria | review-hog-resolution-<slug> | Exactly one active; selecting swaps | review-hog-resolution-criteria |
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).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.The body instructs one specialist review pass over one PR chunk. Match the canonical logic-correctness skill's shape:
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.
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).
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.
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.
57503b8
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.