review-queue
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Fan out isolated, one-per-PR reviews across your queue (own context + worktree per PR, no cross-pollution), then synthesize one report. Pulls the queue from a review inbox (zen) or gh; posts nothing without per-PR authorization.
5 other projects use a version of this.
Adopted by
Most recently taken up by
- zahidakhyar/skillstheir own version, first seen Nov 8, 2026
- fedemelo/agent-configtheir own version, first seen Oct 17, 2026
- microsoft/aibast-agents-librarytheir own version, first seen Oct 13, 2026
- sarthakguptadev/feedsheriff-mcptheir own version, first seen Oct 2, 2026
- tokarix/memory-servertheir own version, first seen Mar 12, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- microsoft/aibast-agents-library★ 8same name
- fedemelo/agent-config★ 4same name
- sarthakguptadev/feedsheriff-mcp★ 1same name
- tokarix/memory-server★ 1same name
- zahidakhyar/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository43 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- break-glassSkill
- claim-ledgerSkill
- contract-parity-auditSkill
- counterfactual-auditSkill
- design-doc-reviewSkill
- diagram-gen-emergentSkill
- diagram-gen-specSkill
- doc-triageSkill
- explain-workSkill
- feature-implSkill
- finding-triageSkill
- first-party-auditSkill
- gh-token-rotateSkill
- handoffSkill
- implementSkill
- jj-stackSkill
- linear-escalation-triageSkill
- mache-usageSkill
- park-workSkill
- pr-boardSkill
- pr-bucketsSkill
- prior-art-cartographerSkill
- problem-decomposerSkill
- pr-review-kitSkill
- pr-reviewSkill
- pr-suggestionSkill
- readme-restructureSkill
- repo-description-polishSkill
- repo-homepage-setSkill
- repo-social-previewSkill
- repo-topic-taggerSkill
- review-prepSkill
- reviewSkill
- self-auditSkill
- structural-pr-reviewSkill
- surveySkill
- taskfile-ci-paritySkill
- test-fidelitySkill
- workflow-auditSkill
- work-scopeSkill
- writing-pr-descriptionsSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 1 is distinctive, and 2 are very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 23
args: if the user passed PR refs, use those verbatim — skip discovery.
- Very specific: line 25
Confirm the list with the user: before dispatching — show repo#number — title for each, note any that are drafts or already-approved, and …
- Very specific: line 53
Verify before relaying: the sub-agents are competent but not infallible (misidentified targets, stale framing, phantom citations).
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Review Queue — isolated per-PR reviews, fanned out and synthesized
The problem this solves: reviewing N peer PRs in one long session pollutes context — findings, file reads, and design intent from PR A bleed into PR B. This skill keeps each review in its own sub-agent + own git worktree, then consolidates. One command, N clean reviews, one report.
Model: the orchestrator (you, the main session) builds the worklist, dispatches one pr-reviewer sub-agent per PR, and synthesizes. The sub-agent carries the discipline (pr-review-kit) and isolation: worktree; the orchestrator never reviews a PR itself (that would re-pollute the main context).
Arguments
A list of PR numbers/URLs to review (e.g. 42088 42090 39311, or full owner/repo#42090 refs). If empty, the skill builds the queue from your review inbox (Phase 0).
Phase 0 — Build the worklist + catch up
- Get the queue (first source that's available):
- zen (if the
zenMCP is connected): callzen_inbox— PRs awaiting you (configured authors + explicit review requests). This is the intended source. - gh fallback:
gh search prs --review-requested=@me --state=open --json number,title,repository(and/or the repos you care about). - args: if the user passed PR refs, use those verbatim — skip discovery.
- zen (if the
- Catch up via an observational memory layer (optional — only if your harness provides one, e.g. a memory/timeline MCP) — for each PR you're about to review, a quick timeline / text search to surface what changed and who reviewed since you last looked (especially if it's a re-review). This is the "stay current" layer — query it, don't carry it. Skip silently if no such source is connected.
- Confirm the list with the user before dispatching — show
repo#number — titlefor each, note any that are drafts or already-approved, and let them prune. Don't fan out a dozen agents on an unconfirmed list.
Phase 1 — Fan out (one isolated reviewer per PR)
Dispatch one pr-reviewer sub-agent per confirmed PR, in parallel (independent work, no shared state). Each pr-reviewer already declares isolation: worktree + read-only tools, so it gets its own repo copy and clean context.
In each dispatch prompt, give the agent exactly what it needs (it can't ask you):
OWNER,REPO,N(PR number)- the canonical remote name for fetching (
upstreamon a fork checkout, elseorigin) - intent: review-only — return a structured summary, post nothing
- any design-doc pointer you have (path/URL), so it doesn't have to hunt blind
Label each agent by PR (e.g. review:repo#1234) so progress is legible. Sub-agents can't spawn sub-agents — that's fine; the worker is flat by design.
Scale note: ~5 PRs fan out cleanly as parallel sub-agents. If you're ever past ~10–15, or you want the reviewers to adversarially cross-check each other before reporting, that's the Workflow tool's territory (codified orchestration, dozens of agents) — switch to it rather than hand-dispatching.
Phase 2 — Synthesize
Collect each sub-agent's structured summary (verdict + falsifiable matrix + blockers/nits/questions). Then produce one consolidated report:
- Per-PR line:
repo#N — VERDICT — one-line why(sortable: BLOCKED/CHANGES first, SHIP last). - Cross-PR themes: anything that recurs (same nit across PRs, a shared dependency two PRs both touch, a design question that spans them). This is the value the orchestrator adds over reading N separate reports.
- What needs you: the short list of PRs where a human decision is required (a design call, an approve, a "reply to reviewer").
Verify before relaying: the sub-agents are competent but not infallible (misidentified targets, stale framing, phantom citations). For any blocker a sub-agent reports, spot-check its evidence command against the actual code before you present it as fact.
Phase 3 — Post (only on explicit, per-PR authorization)
Default posture: post nothing. The report is local until the user says, per PR, "post the review on #N" or "approve #N." Posting is the one side-effect that's visible to others; never batch-post or infer authorization. When authorized, follow pr-review-kit Phase 8 (body-only review, inline threads, or atomic batch) and verify it landed.
Hygiene
- The
pr-reviewerworktrees auto-clean when the agent makes no changes — and a review makes none — so they shouldn't accumulate. Still,git worktree listperiodically and prune anything stale. - This skill orchestrates; the pr-reviewer agent and pr-review-kit skill carry the per-PR discipline. Keep the division: orchestrator builds the list + synthesizes; worker reviews one PR in isolation.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

