Agent skill

LoopX PR Review

by loopx-project in loopx-project/loopx

Runs an evidence-backed pull request review through the loopx CLI and posts bilingual reviews: a full Chinese review plus one concise English verdict.

Apache-2.0Auto-check passedDevelopment

Install LoopX PR Review

skills CLI
$ npx skills add loopx-project/loopx --skill loopx-pr-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install loopx-project/loopx loopx-pr-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/loopx-project/loopx.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/loopx-pr-review .claude/skills/loopx-pr-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
loopx-pr-review
GitHub stars
6.2k
Token cost
~3.3k tokens
SKILL.md length
1,605 words
Files
3
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

Runs an evidence-backed pull request review through the loopx CLI and posts bilingual reviews: a full Chinese review plus one concise English verdict.

  • Works in 5 steps: Record the packet's exact head, then… → Fill review_plan.result_template;… → Apply completion_gate literally: save… → …
  • Reviewing a queue of open pull requests with evidence
  • SKILL.md covers Route, Preserve The Packet, Read The Review Frame First and Execute One Review Plan, plus 5 more sections
  • Calls gh and rg

What it does

This skill is a thin adapter. The built-in pull-request-review capability decides review depth, evidence requirements and verdict policy through a packet from the CLI, and the agent runs loopx pr-review first, carries out the review plan for each selected exact head, then publishes reviews that match the verified findings. Filters include repo, since, state, limit, a repeatable exact-head target and a review priority that favors other developers first by default.

The agent must save the complete first JSON packet, including the execution contract, completeness data, scheduling policy, review groups and each PR's plan, template and evidence commands, and never keep only a jq-trimmed copy. If an exhaustive queue result is incomplete it reruns with the recommended limit. Approval and merge actions belong to the separate loopx-pr-merge skill. The excerpt is truncated.

When your agent uses it

  • Reviewing a queue of open pull requests with evidence
  • Reviewing named PRs at an exact head commit
  • Publishing bilingual reviews that match verified findings

Example prompts

  • “/loopx-pr-review for the open PRs in acme/api since yesterday.”
  • “Review PR number 7 at its current head and publish the review.”
  • “Review the merged PRs in my repo as a post-merge audit.”

Requirements

  • The `loopx` CLI
  • Access to the repository whose pull requests are reviewed

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Record the packet's exact head, then follow review_execution_contract.decision_procedure
  2. Fill review_plan.result_template; preserve missing evidence as unverified and never
  3. Apply completion_gate literally: save final Markdown in review_body, then check
  4. Publish the checked review_body; recheck after edits. Replace result.review_body
  5. Re-read the remote head immediately before verdict and publication; restart evidence if it changed.

What it can do on your machine

Read from SKILL.md and the folder at commit 8205c8b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh
    • rg

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

LoopX PR Review loads about 3.3k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 1,605 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from loopx-project/loopx at commit 8205c8b, republished under its Apache-2.0 licence (© loopx-project). 1,605 words, ~3,302 tokens.

Download SKILL.mdSave it as .claude/skills/loopx-pr-review/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
loopx-pr-review
description
Use for `/loopx-pr-review` or evidence-backed PR queue review. Run `loopx pr-review` first, execute the capability-owned review plan for each selected exact head, then publish full bilingual PR reviews (complete Chinese five-block review plus one concise English verdict) that match the verified findings. Use `loopx-pr-merge` for approval or merge actions.

LoopX PR Review

This skill is a thin host adapter. The built-in pull-request-review capability owns review depth, evidence requirements, completeness, and verdict policy through the CLI packet. Do not copy those rules into this skill or replace them with a host-specific checklist.

Route

Use this skill for /loopx-pr-review, explicit PR reviews, or review queues by state or time window. Route approval, merge, self-merge, and admin bypass to loopx-pr-merge (optional repo-kept workflow, not installed by default) after the evidence review is complete; it never replaces this skill's exact-head gate.

For named PRs, resolve heads and run repeatable --target-exact-head NUMBER@HEAD_OID. An omitted --state keeps ordinary queue discovery open-only while exact targets remain lifecycle-neutral; use explicit --state merged|all only for deliberate history or post-merge audit.

Translate only explicit filters:

  • --repo owner/repo
  • --since ISO
  • --state open|merged|all
  • --limit N; --target-exact-head NUMBER@HEAD_OID (repeatable direct read)
  • --review-priority other-developers-first|owner-first (default other-developers-first; use owner-first to opt into owner priority) When omitted, the CLI resolves pull_request_review from the standard machine capability editor; an absent namespace keeps the default other-developers-first. Words such as today, open, or merged are filters, not permission to return a table only; stats-only output needs an explicit opt-out such as 只统计 or stats only.

Preserve The Packet

Save the full first JSON packet before printing a compact projection. Keep all paths named by agent_response_contract.required_packet_fields_to_preserve:

  • agent_response_contract.review_execution_contract
  • result_completeness, scheduling_policy, review_groups
  • pull_requests[review_action_kind!=null].review_plan
  • pull_requests[review_action_kind!=null].review_template
  • pull_requests[review_action_kind!=null].evidence_commands

Do not pipe the only copy through jq. When an exhaustive queue request has result_completeness.complete=false, rerun with its recommended_limit before reviewing; limit_scope=exact_targets is already complete for the named targets.

Read review_execution_contract.policy_revision from the packet; require the result's review_policy_revision to equal it, never a literal this file pins. If missing or unequal, do not publish APPROVE; a conservative REQUEST_CHANGES is allowed only when it names the incompatible-policy gap. Do not retain expired temporary worktree overrides. Report incompatible policy rather than downgrading the review.

Read The Review Frame First

Read the PR's existing comments and cited documents first: a maintainer comment names the contract the change is judged against.

  • Review inside that frame; cite the document and check its rows, gates, or exit criteria one by one at the exact head, attaching each finding to its row.
  • Name unsatisfied rows as missing frame items with their own evidence; keep the declared boundary (preview versus promotion, milestone entry versus merge).
  • Publish the frame-aligned conclusion as a PR comment citing that guidance, since a published review body cannot be edited.

Execute One Review Plan

Receive pending current-session requests through review_execution_contract.decision_procedure.establish_goal before generic queue selection, including other agents sharing a GitHub account; task intake and claimed action authority are separate judgments. Resolve the selected requests with repeatable --target-exact-head NUMBER@HEAD_OID. Follow scheduling_policy and its ranked actionable review_sequence; explicit current-request PR selection may override ordering only, never pull_requests[].review_action_kind or exact-head idempotency. Generic re-review, 重新review, and 复审 wording selects the named PR; it is not a force-refresh token. Todo/monitor prose may not select work. When review_action_kind is null, the row stays in pull_requests inventory but must not appear in review_sequence; its review_plan and review_template are null and evidence_commands is empty. Do one compact exact-head conclusion readback and report the existing verdict or bounded invalid/missing reason. Run a fresh audit only when the user explicitly requests fresh evidence despite that result, or supplies a concrete new concern; regenerate with --fresh-audit-exact-head NUMBER@HEAD_OID, then execute the complete current plan and never inherit the earlier approval. For every actionable PR:

  1. Record the packet's exact head, then follow review_execution_contract.decision_procedure: the current goal and problem_context delivery judgment first, including on re-review, then evidence_commands and repository-native validation.

  2. Fill review_plan.result_template; preserve missing evidence as unverified and never infer verified from metadata or CI. Execute its repository-reuse, default-off, authority and real-path counterfactuals rather than repeating them as prose. Fill result.reviewer per review_execution_contract.reviewer_declaration, open the body with its body_marker line, and read problem_context.spec_basis's specification before the diff.

  3. Apply completion_gate literally: save final Markdown in review_body, then check evidence and that exact body. Follow capability-owned floors and scope counterfactuals; prose cannot replace missing execution:

    bash
    loopx --format json pr-review --check-result review-result.json --packet review-packet.json

    Fix contradictory verdicts, not evidence labels. It cannot verify evidence truth, architecture, freshness or a relabeled old result.

  4. Publish the checked review_body; recheck after edits. Replace result.review_body with the remote readback and rerun --check-result; matching does not certify reasoning.

  5. Re-read the remote head immediately before verdict and publication; restart evidence if it changed.

Each PR needs independent evidence and a standalone card; a queue table is a preface only.

For managed review, pass --goal-id GOAL and follow the packet’s resolved wait_for_ci: false means never fetch, poll, or wait for CI; true retains available CI observation and the merge gate's CI policy. It does not require every CI job to finish or succeed before APPROVE when independent current evidence covers the changed invariants. Follow validation_matrix.validation_source: required marks decisive review evidence, not branch protection; record merely pending remote jobs as separate diagnostic rows, and keep an invariant unverified when CI is its only decisive coverage. Pending CI alone is never a REQUEST_CHANGES reason. Apply validation_matrix.failure_attribution, including its evidence_scope, to current required checks. An independently attributed unchanged baseline failure or external outage can hold merge readiness without forcing REQUEST_CHANGES on an unrelated PR. Preserve earlier failures and the current evidence that supersedes them in existing evidence fields; historical root-cause completeness alone is not an approval gate. Current unattributed failures, material instability and missing affected-invariant coverage still block; selecting one successful rerun does not resolve them. Configure one Goal with configure-goal --goal-id GOAL --no-pr-review-wait-for-ci --execute; clear with --clear-pr-review-configuration --execute.

Show full SKILL.md (696 more words)Show less

Publish And Read Back

For an open PR, publish validated actionable findings by default unless the user requested local-only/dry-run output or the finding is private or security-sensitive.

  • Remaining blocker: formal REQUEST_CHANGES; for an author-owned PR, use a COMMENTED review titled Request changes conclusion (author-owned PR; GitHub blocks formal self-review).
  • Non-blocking finding with no blockers: formal APPROVE, not a bare comment; when the account is the author and self-approval is rejected, record the same conclusion as a COMMENTED review titled Approval conclusion (author-owned PR; GitHub blocks formal self-approval).
  • Non-blocking P2 suggestions: still APPROVE; keep them in the body.
  • Merged PR: publish a post-merge audit comment only for a new actionable finding; avoid duplicating an equivalent exact-head result.

Build public text from the exact reviewed head; remove local paths, private context, raw logs, credentials and internal-only links. Read the published review back, verify state/body and return its URL. After APPROVE (incl. existing approval), execute review_execution_contract.approval_closeout; reconcile only verified obsolete blockers with owner/GitHub authority, preserving discussion and unresolved reviews. Merge routes through loopx-pr-merge; APPROVE is not merge authority; a public blocker belongs on the PR.

Immediately before every merge, run loopx --format json pr-review --goal-id GOAL --repo OWNER/REPO --check-merge-readiness NUMBER@HEAD_OID; require ready=true. Its compact Goal observation suppresses only unchanged requalification; material change reopens it, admin bypass never overrides this gate, and author fallback needs user authority.

Full PR Review And Bilingual Format

Every review must cover the whole PR, not only the top finding: read the full diff/local validation, then explain motivation, architecture, changed symbols, both paths, whole-diff risk, validation, and judgment. A findings-only or blocker-only body is incomplete.

Publish two artifacts:

  1. 详细中文评审 - a standalone Chinese full-PR review with the exact head and five sections: 动机, 改动思路, 具体改动, 对主干的风险, 我的整体评价. Cover every changed surface and key symbols, not only the main finding.
  2. 审查者来源 - one visible body_marker line naming the actor kind and, for an agent, the model and provider. It must agree with result.reviewer; a reviewer from another operator reads this body, not your local result.
  3. 英文简短结论 - a line starting with exactly English verdict: followed by the bare token APPROVE or REQUEST_CHANGES, then the head, key finding, and validation. The parser matches that token as a keyword, so English verdict: Approved at ... is invalid and leaves the head without a merge-ready approval; write English verdict: APPROVE - ....

Do not publish before the Chinese section covers the entire PR. Follow the packet's problem_context publication rule for an understandable opening. Read both artifacts back.

Example / Walkthrough / Smoke-Only PRs

When the review plan marks smoke_or_example_only, the durable_smoke_value evidence is mandatory before approval. The essence is real, durable value to the repository and product: running, deterministic, and public-safe are necessary but not enough.

  1. Name the shipped behavior, boundary, or maintenance cost this artifact guards. "Demonstrates something that already works" is not durable value.
  2. Scan existing coverage (rg -l '<behavior|module>' examples tests) and the same-author batch (gh pr list ... --author <author> / gh search prs); flag same-shape batches opened within minutes as PR farming.
  3. Apply the repo smoke policy: thin + durable, guard shipped behavior or a real boundary, compress rather than append, consolidate same-shape walkthroughs into one PR or focused tests.
  4. Verdict: REQUEST_CHANGES for duplicative, oversized, or value-less scaffolding; name the consolidation or thinning repair in the body.
  5. Repeat offenders: after a REQUEST_CHANGES warning, further low-value same-shape PRs from the same author escalate to a contribution-restriction recommendation (owner blocks the account from further PR submissions); the warning must name this consequence.

Autonomous Queue

For recurring observation, keep one ignored checkpoint and use loopx --format json pr-review --repo owner/repo --state open --autonomous-observation --observation-state-file .local/pr-review-monitor.json with the projected or handled exact-head flags when those durable receipts exist.

Treat candidate as a preview, not a durable projection. Follow this order: durable Todo target-key readback -> --projected-exact-head -> exact-head review/comment readback -> --handled-exact-head. Never send the projection ACK before the Todo exists, or the handled ACK before readback at that head. Observation states remain literal; the checkpoint grants no authority. Stateless callers may use --previous-observation-json instead.

Failure

If loopx pr-review is unavailable, repair the LoopX install or use the intended checked-out CLI. Do not reconstruct the queue manually and call it a successful /loopx-pr-review run.

© loopx-project, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/loopx-pr-review of loopx-project/loopx.

  • SKILL.md
  • .loopx-skill-scope
  • agents/openai.yaml

Open the folder on GitHubat commit 8205c8b

Compare with similar skills

LoopX PR Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

LoopX PR Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
LoopX PR Review this skillloopx-project/loopx6.2k—~3.3kAutomated safety check: PassApache-2.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Understand Diff AnalysisEgonex-AI/Understand-Anything85k1 repos~1.4kAutomated safety check: PassMIT
WooCommerce Code Reviewwoocommerce/woocommerce11k3 repos~1.1kAutomated safety check: PassCustom licence
Open Code Review CLIalibaba/open-code-review44k—~3.1kAutomated safety check: PassApache-2.0
GitHub Review Iterationprisma/orm48k—~2.2kAutomated safety check: PassApache-2.0

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Categories

Questions about LoopX PR Review

What does LoopX PR Review do?

Runs an evidence-backed pull request review through the loopx CLI and posts bilingual reviews: a full Chinese review plus one concise English verdict. This skill is a thin adapter. The built-in pull-request-review capability decides review depth, evidence requirements and verdict policy through a packet from the CLI, and the agent runs loopx pr-review first, carries out the review plan for each selected exact head, then publishes reviews that match the verified findings.

When should I use LoopX PR Review?

LoopX PR Review fits situations like: reviewing a queue of open pull requests with evidence; reviewing named PRs at an exact head commit; publishing bilingual reviews that match verified findings.

How do I install LoopX PR Review in Claude Code?

Run `npx skills add loopx-project/loopx --skill loopx-pr-review -a claude-code`. Or copy the skill folder (skills/loopx-pr-review in loopx-project/loopx) into .claude/skills/loopx-pr-review in your project. Claude Code loads it when a task matches its description.

How do I install LoopX PR Review in Codex?

Run `npx skills add loopx-project/loopx --skill loopx-pr-review -a codex`. Or copy the skill folder (skills/loopx-pr-review in loopx-project/loopx) into .agents/skills/loopx-pr-review in your project. Codex loads it when a task matches its description.

Can I use LoopX PR Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add loopx-project/loopx --skill loopx-pr-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/loopx-pr-review, .gemini/skills/loopx-pr-review, .github/skills/loopx-pr-review and .opencode/skills/loopx-pr-review in your project.

What does LoopX PR Review need to run?

Going by SKILL.md and its folder, LoopX PR Review needs the command-line tools its instructions call (gh and rg). Our summary lists: The `loopx` CLI; Access to the repository whose pull requests are reviewed.

Does LoopX PR Review access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is LoopX PR Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does LoopX PR Review use?

LoopX PR Review is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does LoopX PR Review use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to LoopX PR Review?

Skills that share tags, products or a category with LoopX PR Review: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Understand Diff Analysis (Egonex-AI/Understand-Anything, 85k stars), WooCommerce Code Review (woocommerce/woocommerce, 11k stars) and Open Code Review CLI (alibaba/open-code-review, 44k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains LoopX PR Review?

loopx-project (a GitHub organization) maintains it in loopx-project/loopx, which has 6,167 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 7, 2026.

Source: loopx-project/loopx on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.