review-pr

name	review-pr
description	Review QuestDB documentation pull requests for placement, discoverability, dual-audience (human + LLM) quality, example coverage, and SQL syntax diagrams. Use when reviewing a docs PR, a new or changed page, or assessing whether documentation is findable, correct, and usable by both engineers and LLMs that build systems against QuestDB.
argument-hint	[PR number, URL, or branch] [--level=0..2]
allowed-tools	Bash, Read, Grep, Glob, Agent

QuestDB documentation PR reviewer

Use this as the operating procedure when reviewing a pull request against the questdb/documentation repo. The goal is not to rewrite the contribution but to judge whether it meets the bar for technical documentation that both engineers and LLMs rely on to build systems against QuestDB.

Guiding principle: users judge the product by the quality of its docs. The audience is technical, and a large share of "readers" are LLMs assembling systems, writing SQL, and one-shotting integration problems from these pages. A page that is correct but unfindable, or findable but vague, fails the bar.

This skill covers the review-specific judgment calls. For mechanical style and formatting rules (frontmatter, sentence case, admonitions, link paths, demo datasets, alphabetical ordering), defer to the questdb-docs-writer skill's review checklist rather than restating it here.

How to run a review

1. Resolve the target and get the diff. The skill reviews either a named PR or the current branch.

   • If invoked with a PR number or URL (after stripping any --level=N, -lN, or bare-digit level token), check it out so full-file reads and yarn build run against the PR branch, and capture its metadata and existing review comments:

     PR='<PR number or URL from the invocation, with the level token removed>'
     gh pr view "$PR" --json number,title,body,labels,state,headRefName
     gh pr checkout "$PR"
     gh pr view "$PR" --comments
     

   • If no PR is given, review the current branch as-is.

   Either way, identify the scope — list new pages, changed pages, and any sidebars.js change separately:

   git fetch origin
   git diff --stat origin/main...HEAD -- documentation/
   git diff origin/main...HEAD -- documentation/sidebars.js
   

   A new page with no sidebars.js change is an immediate red flag (see Pillar 1).

2. Read each new or changed page in full, not just the diff hunk. Placement and information-flow problems are invisible at the hunk level.

3. Build to catch broken links and render errors.

   yarn build
   

   A failing build is a blocking issue. Never approve over a broken build.

4. Score against the pillars below, assign a severity to each finding, and produce the review using the output format at the end.

5. For non-trivial PRs, escalate to the agent passes. The pillar review is a single-pass, checklist-anchored read — the right default for small edits, but checklists have a blind spot (a reviewer scoring fixed pillars tends to confirm them and miss failure modes the pillars don't name). For new pages, flagship/SQL-reference pages, or any change an LLM will build against, run the Adversarial review agents pass below (levels 1-2).

Severity levels

Tag every finding with one of these. The PR author needs to know what blocks merge versus what is a suggestion.

• Critical — blocks merge. The page is technically wrong, an example does not run, a top-tier feature is buried or unfindable, or the build is broken.
• Major — should be fixed before merge. Poor information flow, missing syntax diagram on a SQL page, no usage examples, inconsistent schema, weak or missing description that hurts SEO/LLM retrieval.
• Minor — fix if convenient. Suboptimal wording, a missing cross-link, an example that works but uses an off-convention schema.
• Nit — optional polish. Phrasing, ordering preferences.

A "top-tier feature documented 10 levels deep in the sidebar" is Critical, not a nit. Placement is a first-class quality dimension.

Pillar 0: Technical correctness

For a technical audience, a wrong example is worse than no example. Verify before anything else.

• Every SQL example must be runnable. Mark demoable queries with demo and confirm they execute against the demo instance or a local build.
• Function signatures, parameter names, types, and defaults must match the actual product behavior, not an aspirational design.
• Behavior claims (ordering, nullability, error conditions, edge cases) must be accurate. If you cannot verify a claim, flag it as Major with a request for confirmation rather than letting it pass.
• New config properties, keywords, functions, types, and operators must be cross-checked against the product and the questdb/sql-parser repo (see the questdb-docs-writer skill's parser-sync section).

Pillar 1: Sidebar placement and findability

New documentation must land in the sidebar where a user would look for it, without cluttering or confusing navigation.

Check:

• Depth matches importance. A top-tier or flagship feature must be reachable near the top of its section, not buried several levels deep. Map the path a user clicks to reach the page. If a marquee feature is 6+ clicks deep, that is Critical. Minor features can sit deeper.
• Correct section. The page belongs in the category that matches its type: concept vs. guide vs. SQL reference vs. function reference vs. configuration. A SQL keyword page under Guides is misfiled.
• Findable label. The sidebar_label is what a scanning user (or an LLM building a table of contents) sees. It must name the feature in the words a user would search for, not an internal codename. Avoid labels that collide with or are easily confused with a sibling entry.
• No clutter. Adding a page should not bloat a category past the point of scannability. If a category is growing unwieldy, flag whether the new page warrants a sub-category or whether an existing grouping should absorb it.
• Ordering. Respect the section's ordering rule (alphabetical for reference and config lists; logical/lifecycle order for conceptual flows).
• Sidebar entry exists at all. A new page absent from sidebars.js is only reachable by direct URL. Unless it is an intentional partial or redirect target, this is Critical — the page is effectively invisible.

Pillar 2: Written for humans and LLMs in equal measure

Pages serve two audiences at once: engineers scanning for an answer, and LLMs using the page as context to one-shot a problem (for example, writing the correct SQL for a specific task). Both need the same thing: a clean information flow from intent to answer.

Check:

• Intro states what and why. The opening paragraph (before the first H2) must say what the feature does and why someone would use it, in concrete terms. An LLM should be able to decide from the intro alone whether this page answers the user's problem.
• Progressive information flow. intro (what/why) → syntax (formal grammar) → reference material (valid values, types, parameters) → examples (usage). Concepts stated once, in the right place. An LLM should not have to stitch an answer together from fragments scattered across the page.
• Self-contained sections. Each section should make sense without requiring the reader to have read three other pages. Link out for depth, but state the immediate context inline.
• One-shot test. Ask: "If an LLM had only this page as context, could it produce a correct, runnable solution to the problem this page addresses?" If the answer is no — because a parameter is undocumented, an example is missing, or an edge case is hand-waved — that is at least Major.
• No filler. Throat-clearing intros, vague summaries, and approving restatements ("this is exactly what you want") add tokens without adding information and degrade both human and LLM use. Flag them.

Pillar 3: SQL documentation needs plentiful, consistent examples

SQL-related pages live or die on their examples. An engineer or LLM copies an example and adapts it; sparse or inconsistent examples force guesswork.

Check:

• Coverage. Every documented form, clause, and important parameter has at least one example. The common case is shown first; edge cases and advanced forms follow.
• Consistent schema. Examples must use the same tables and columns as the rest of the docs. Prefer the demo datasets (fx_trades, core_price, market_data, trades) and match their exact column names (for example, trades uses amount, fx_trades uses quantity). A page that invents a one-off schema when an existing one fits is Minor to Major depending on how much it confuses cross-page learning.
• Realistic, finance-friendly naming. No Alice/Bob/sensor_1, no meme coins. Use the conventions in the questdb-docs-writer skill.
• Demoable where possible. Queries that can run on the demo instance carry the demo tag and use TICK time filters ('$now-1h..$now') instead of hardcoded dates or prices that rot.
• Predictable output only. Result tables appear only where output is stable and illustrative; omit them for dynamic demo queries.

Pillar 4: SEO friendly and LLM friendly

The page must be discoverable by search engines and easy for an LLM to surface and recommend to a user.

Check:

• Description frontmatter. A single, specific sentence (~150 chars) that names the feature and what it does. This is the snippet both Google and an LLM retrieval layer key on. Generic descriptions ("Learn about this feature") are a Major SEO/LLM miss.
• Title and label use search terms. The title should contain the words a user actually types. Match real terminology over clever phrasing.
• Headings are descriptive and keyword-bearing. Section headings double as anchors an LLM can deep-link to. "Examples" is fine; "How ASOF JOIN matches rows" is better when it captures a real query intent.
• Inbound links. A new page should be linked from related pages (sibling reference pages, the relevant concept or overview page), not only reachable via the sidebar. Internal links drive SEO and give an LLM multiple retrieval paths to the page. A page with zero inbound links is Minor to Major.
• Recommendability. Ask: "Would an LLM, given a user's problem, be able to confidently point to this page by name and URL?" That requires a clear title, a precise description, and a scope the page actually delivers on.

Pillar 5: Text-based syntax diagrams for SQL

Every SQL reference page must carry a text-based syntax diagram that an LLM can parse directly. QuestDB uses fenced questdb-sql syntax blocks, not railroad SVGs, precisely because text grammar is machine-readable.

Check:

• Syntax section exists and is first. The ## Syntax section is the first H2, immediately after the intro. Missing syntax block on a SQL reference page is Major.
• Text grammar, not an image. The diagram is a fenced questdb-sql block using the convention notation: [optional], { A | B } for required alternatives, [A | B] for optional alternatives, [, item ...] for repeatable elements, camelCase placeholders. An SVG railroad diagram in place of a text block is a regression — flag it.
• Multiple forms get multiple labeled blocks. When a statement has distinct forms (for example CREATE TABLE vs. CREATE TABLE AS SELECT), each gets its own labeled block.
• Completeness. The grammar covers every clause the page documents. An example that uses a clause absent from the syntax block is a defect in one of the two.
• Both operator spellings. Where inequality is shown, both != and <> appear ({ != | <> }).
• No horizontal scroll. Break long grammar lines with indentation so the block stays readable and fully visible to both humans and LLMs.

Cross-cutting checks

• Build passes with no broken links (yarn build).
• Defer to questdb-docs-writer for the full style/formatting checklist: frontmatter, sentence case, no H1 in body, admonitions over bold, current /docs/... link paths, alphabetical ordering of reference sections, no em-dashes, no LLM mannerisms.
• Scope discipline. Review the impact on neighbors too: a new sidebar entry shifts its siblings; a renamed page may orphan inbound links. Check that the PR did not silently break adjacent pages.
• Changelog. If the change is user-facing, confirm the docs-changelog skill's criteria are met (or flag that a changelog entry is owed).

Adversarial review agents (deeper pass)

For marquee pages, large PRs, or any change an LLM will build against, add a deeper pass that spawns fresh-context adversarial agents — agents whose only job is to attack the page, with no checklist to anchor them. This mirrors the multi-agent review in the questdb/questdb repo, adapted to documentation. Because docs are a graph that humans and LLMs traverse, the deeper pass also maps the change surface and walks the reader's cross-page journey, not just each page in isolation.

Review depth (levels)

Parse the invocation for a level token (--level=N, -lN, or a bare 0-2). Default to 0. State the chosen level in one line at the start of the review (e.g. "Reviewing at level 1"); if defaulted, mention that level 2 exists.

Level	What runs
0 (default)	Single-pass inline pillar review (the procedure above). No agents. Use for typo fixes, small edits, single-page tweaks.
1	Build the change surface map, then spawn structured agents 1-4 (Agent 4 = cross-page navigation) and the fresh-context adversarial agent (5) in parallel; verify findings. Use for new pages, multi-page changes, and link/sidebar changes.
2	Full change surface map and all six agents (adds the LLM one-shot consumer, 6) with per-finding verification. Use for flagship/SQL-reference pages and large PRs.

Map the change surface (levels 1-2)

Before spawning agents, build a change surface map — the docs analog of a code reviewer's blast-radius analysis. A page change ripples to its neighbors; produce this inventory, don't guess it:

• Inbound links. For every changed, renamed, or moved page, search the repo for links pointing at it (old path and new path). A renamed or moved page whose old path still has inbound links is an orphan-link defect.

  rg -n "query/sql/foo" documentation/   # repeat per changed page path
  

• Outbound links & indirection chains. Where each changed page sends the reader ("see X"). Flag chains longer than two hops, circular "see X → see Y → see X" loops, and links that dead-end on a page that doesn't answer the question.
• Sidebar neighbors. The siblings immediately before/after each changed entry in sidebars.js. Note label collisions and whether an insertion shifted or duplicated a neighbor.
• Shared schema & vocabulary. The demo datasets, column names, and key terms the changed pages use, versus what their neighbors and linked pages use — the raw material for spotting terminology and schema drift across pages.
• Reading path(s). The realistic journeys a reader takes through the changed set (concept → guide → SQL reference). These are the scripts Agent 4 walks.

This map is required input for Agent 4 and sharpens agents 1-3. Skip it only at level 0.

Spawning review agents in Claude Code

Harness note: this skill runs inside Claude Code. Launch parallel agents with the Agent tool — one agent per role, dispatched in a single message so they run concurrently. Search the repo with Grep/Glob and read files with Read.

Each agent task must be self-contained — the child does not inherit this conversation. Give every agent: the diff (or have the child re-run the git diff from step 1), the change surface map (for the structured agents), the list of new/changed pages with their sidebars.js context, its role instructions below, and an explicit "review only — do not edit any files" constraint. Dispatch them like:

Agent("Agent 1 — Technical correctness. <diff + changed pages + Agent 1 instructions>. Review only; do not edit.")
Agent("Agent 2 — Placement & retrieval. <...>. Review only; do not edit.")
Agent("Agent 3 — Information flow, examples & syntax. <...>. Review only; do not edit.")
Agent("Agent 4 — Cross-page navigation & coherence. <change surface map + reading paths + linked pages>. Review only; do not edit.")
Agent("Agent 5 — Fresh-context adversarial. <ONLY the changed page text + file paths; NO pillars>. Review only; do not edit.")

The adversarial agents (5 and 6) must NOT receive the pillars, the severity table, or any checklist — give them only the page content (and, for Agent 6, the user task). The parent session owns synthesis, deduplication, verification, and the final report; children only return findings.

Structured agents (pillars and change surface)

Agent 1 — Technical correctness & runnable examples (Pillar 0). Build the site (yarn build). Run every demo-tagged and runnable SQL example against the demo instance or a local build. Cross-check every function signature, parameter name, type, default, and behavior claim against the product and the questdb/sql-parser repo. A broken build, an example that does not run, or a signature that does not match the product is Critical.

Agent 2 — Placement, findability & retrieval (Pillars 1, 4). Map the click path to the page. Verify section, depth-vs-importance, sidebar_label, ordering, and that the page exists in sidebars.js at all. Check the description frontmatter is a specific ~150-char sentence, that the title and headings carry real search terms, and that the page has inbound links from sibling/concept pages. A buried marquee feature or a page missing from the sidebar is Critical.

Agent 3 — Information flow, examples & syntax grammar (Pillars 2, 3, 5). Check the intro states what/why, the flow is intro → syntax → reference → examples, and sections are self-contained. Verify example coverage per documented form/clause, consistent demo schema and finance-friendly naming, and that SQL reference pages carry a text-based questdb-sql syntax block (not a railroad SVG) as the first H2, with both !=/<> spellings and no horizontal scroll.

Agent 4 — Cross-page navigation & coherence (human + LLM). Consumes the change surface map. Walk each reading path and judge every page-to-page transition twice:

• As a human reader: does each transition flow? Are prerequisites established before the page that needs them? Is each concept explained once, in one canonical place — not duplicated or, worse, contradicted across pages? Do "see X" links land on a page that actually answers the question instead of bouncing the reader around?
• As an LLM doing retrieval: if these pages are retrieved independently and out of order, does each still stand on its own? Do terminology and schema stay consistent so an LLM stitching two pages together doesn't hit contradictory column names or definitions? Do cross-links give a path to the missing context, or dead-end? Is there one canonical page an LLM would cite for this topic, or is the answer smeared across several with no clear home?

Output a verdict per transition — COHERENT / BROKEN / DRIFT — each BROKEN or DRIFT naming the exact link, orphaned inbound link, terminology/schema mismatch, or circular chain. Orphaned inbound links and cross-page contradictions are at least Major. Skip this agent only when the PR touches a single page and changes no links or sidebar entries.

Adversarial agents (no checklist — escape pillar anchoring)

Agent 5 — Fresh-context adversarial reader. Give this agent ONLY the raw text of the changed pages and their file paths. Do NOT give it the pillars, the severity levels, or any checklist. Its sole instruction:

You are a skeptical staff engineer who was told this page answers your problem. Find every way it misleads you, omits something you need, states something that is wrong or unverifiable, buries the answer, or wastes your time. Quote the exact passage for each problem.

It may use Read, Grep, and Glob to explore the repo and the product to confirm a claim is wrong. Findings are not pre-classified — each states what's wrong, where, and why. A finding here that none of agents 1-4 produced is high signal: the structured review's frame missed it.

Agent 6 — LLM one-shot consumer (the docs-specific adversarial test). This agent operationalizes Pillar 2's one-shot test. Give it ONLY the changed page as if it were the single retrieved context, plus one realistic user task the page claims to solve (for example, "write the SQL to X using this feature"). Instruct it to actually produce the runnable solution from the page alone:

Wherever you must guess a parameter, invent a column or table, assume a default, or reach for knowledge not on this page, stop and record it as a gap. Every guess is a documentation defect.

The output is the attempted solution plus a list of every point the page forced a guess. Each gap is at least Major — it is concrete proof of a one-shot failure that the prose-level pillar check only asserts in the abstract.

Verify before reporting

Adversarial agents over-fire. Before any finding reaches the report, verify it:

• "Example doesn't run" / "claim is wrong": re-run the example or re-check the signature against the product yourself. Never relay an agent's claim unverified.
• "Buried / missing from sidebar": open sidebars.js and confirm the path.
• "Orphaned inbound link" / "broken or circular chain" (Agent 4): resolve the link target yourself; a valid relative path the build accepts is not a defect.
• "Forced a guess" (Agent 6): confirm the needed fact is genuinely absent from the page, not merely one section away behind a clear inline link.

Classify each finding as CONFIRMED, FALSE POSITIVE, or CONFIRMED-with-nuance. Move false positives to the Downgraded section of the output with a one-line dismissal each, so the author can audit the reasoning.

Producing the review

Structure the review so the author can act on it immediately:

1. Verdict — one line: approve, approve-with-nits, or request-changes.
2. Blocking issues — every Critical and Major finding, each with the file path, what is wrong, and the concrete fix. Group by file.
3. Non-blocking — Minor and Nit findings, briefly.
4. What works — call out what the PR does well, so good patterns get reinforced.
5. Downgraded (false positives) — only when agent passes ran (levels 1-2): findings raised by an agent but dismissed on verification, one line each stating the original claim and why it was dropped. Also state the verified vs dropped count (e.g. "6 findings verified, 3 false positives removed").

Reference exact paths (documentation/query/sql/foo.md) and, where useful, line numbers. Propose the fix, don't just name the problem. Be direct and concise; this is a technical review for a technical author.