Creating Description For Gh PR
redis/jedis
Generate a clear, concise GitHub PR title and description from the diff between two local git branches, and save it to prDescription.md in the repo root.
Review a GitHub issue or pull request URL as a node-redis maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality…
$ npx skills add redis/node-redis --skill maintainer-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install redis/node-redis maintainer-review --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/redis/node-redis.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/maintainer-review .claude/skills/maintainer-review && rm -rf skills-srcUse ~/.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/
Install the "maintainer-review" agent skill from https://github.com/redis/node-redis/tree/master/.agents/skills/maintainer-review into .claude/skills/maintainer-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "maintainer-review", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/redis/node-redis/tree/master/.agents/skills/maintainer-reviewType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add redis/node-redis --skill maintainer-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install redis/node-redis maintainer-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/redis/node-redis.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/maintainer-review .agents/skills/maintainer-review && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "maintainer-review" agent skill from https://github.com/redis/node-redis/tree/master/.agents/skills/maintainer-review into .agents/skills/maintainer-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "maintainer-review", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add redis/node-redis --skill maintainer-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install redis/node-redis maintainer-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/redis/node-redis.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/maintainer-review .cursor/skills/maintainer-review && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "maintainer-review" agent skill from https://github.com/redis/node-redis/tree/master/.agents/skills/maintainer-review into .cursor/skills/maintainer-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "maintainer-review", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/redis/node-redis.git --path .agents/skills/maintainer-review--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add redis/node-redis --skill maintainer-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install redis/node-redis maintainer-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/redis/node-redis.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/maintainer-review .gemini/skills/maintainer-review && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "maintainer-review" agent skill from https://github.com/redis/node-redis/tree/master/.agents/skills/maintainer-review into .gemini/skills/maintainer-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "maintainer-review", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install redis/node-redis maintainer-reviewInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add redis/node-redis --skill maintainer-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/redis/node-redis.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/maintainer-review .github/skills/maintainer-review && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "maintainer-review" agent skill from https://github.com/redis/node-redis/tree/master/.agents/skills/maintainer-review into .github/skills/maintainer-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "maintainer-review", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add redis/node-redis --skill maintainer-review -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install redis/node-redis maintainer-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/redis/node-redis.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/maintainer-review .opencode/skills/maintainer-review && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "maintainer-review" agent skill from https://github.com/redis/node-redis/tree/master/.agents/skills/maintainer-review into .opencode/skills/maintainer-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "maintainer-review", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
maintainer-reviewReview a GitHub issue or pull request URL as a node-redis maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality…
Maintainer Review is an agent skill from redis/node-redis, published by the product's own GitHub organization. Review a GitHub issue or pull request URL as a node-redis maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort. Use when assessing issue validity or severity, deciding whether an issue should be prioritized or closed, determining whether a requested feature represents an unmet need rather than a discoverability or usage gap, judging…
Its SKILL.md is about 5.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/evaluation-framework.md`).
It sits in Development, covering Code quality and Pull requests. It works with Redis and GitHub. The licence is MIT.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit bba9381. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Maintainer Review loads about 5.5k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 219 tokens; SKILL.md has 3,008 words of instructions outside code blocks.
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.
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.
The full file from redis/node-redis at commit bba9381, republished under its MIT licence (© redis). 3,008 words, ~5,514 tokens.
.claude/skills/maintainer-review/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Make a maintainer decision, not a generic diff summary. Separate these questions:
Treat an issue's requested field, callback, flag, class, or implementation strategy as a proposed mechanism, not as the accepted requirement. Do not begin by asking how to implement it. First establish either a concrete unmet user outcome or a violated supported contract, then prove that the proposed mechanism is better than the available alternatives.
Lead with the current review state. Use Preliminary assessment while runtime approval or evidence is pending, and Maintainer decision only when the review can be concluded. Use the diff, issue narrative, and contributor effort as evidence, not as proxies for impact.
Related to does not transfer evidence of need to an adjacent extension. If the reported scenario has already been fixed, treat additional variants as new needs requiring their own evidence.Use read-only GitHub access. On this laptop, do not run gh unless the user explicitly asks in the same turn. A review never authorizes comments, labels, branch changes, pushes, merges, or other remote writes.
Complete this pass before deeply evaluating a proposed implementation and before any positive issue or PR assessment.
First assign one Need evidence status:
Only Demonstrated need may receive Merge-worthy as-is or Merge-worthy after focused changes. For Plausible but unproven, prefer Needs evidence or Not worth completing; for Already covered or Unsupported, prefer closure or the relevant simpler alternative.
Do not treat a test proving that new code can work as evidence that the feature is needed. A sinon spy or stub, a fake socket, a synthetic fixture built with @redis/test-utils, or a new regression test can establish code-path reachability and implementation correctness; it does not by itself establish realistic server behavior, user reach, frequency, practical consequence, or demand.
API symmetry, naming consistency, and parity with an adjacent command, reply type, or output type are design arguments, not evidence of need. Parity may justify work when it removes existing complexity or enforces a broad demonstrated invariant, but adding branches, tests, documentation, or public behavior requires independent practical justification.
If the need is not Demonstrated, inspect the patch only far enough to understand its contract, risk, and maintenance cost. Do not turn implementation defects, missing tests, or documentation gaps into a request-changes recommendation, because those questions become merge-blocking only after the need gate passes. If the report provides no concrete scenario, the existing functionality appears sufficient, or the requested mechanism solves only a hypothetical convenience problem, prefer Needs evidence, Close, Supersede with a simpler alternative, or Not worth completing over designing the requested feature on the reporter's behalf.
An existing workaround may change priority or solution shape, but it does not by itself erase a demonstrated correctness, security, compatibility, or lifecycle defect in supported behavior. Evaluate both the unmet outcome and the violated contract.
Do this before deeply evaluating a specified PR. A PR URL selects the starting point, not necessarily the entire comparison set.
Compare candidates on need coverage, runtime correctness, placement, tests, compatibility, complexity, readiness, remaining maintainer work, and reusable pieces. Prefer the best maintainable solution, not the first or smallest diff by default.
Always begin with a desk review. Inspect the real runtime path before judging a change as trivial or meaningful. Check callers, public exports, equivalent streaming/non-streaming or provider/runtime paths, persistence, cleanup, and focused tests. Inspecting test code is part of the desk review; executing tests, imports, examples, reproductions, benchmarks, or service calls is a runtime probe.
Consult AGENTS.md and the docs/ guides (for example client-configuration, clustering, sentinel, pool, RESP, and transactions) for architecture and background, and treat the source under packages/*/lib as the source of truth. Verify every current claim against the remote change, current source, tests, docs, release boundary, and runtime evidence. Do not infer issue status or PR correctness from a guide.
Use this evidence order across the two stages:
Produce an initial result from static evidence before running code:
Before a positive assessment, complete the pass in step 2 and be able to state all of the following from concrete evidence:
If any answer is missing and could change whether code should exist at all, do not call the issue actionable or the PR merge-worthy. Request only the evidence needed to distinguish a genuine capability gap or contract violation from a usage, discoverability, or solution-design problem. This is a product and architecture evidence gap, not a runtime-probe trigger by itself.
Run this pass before any positive PR assessment when a patch adds, removes, or reorders cleanup, retry, reconnect, cancellation, listeners, shared promises or tasks, sockets or streams, state flags, or mutable state across an await, callback, event, or deferred completion.
A and B, across every suspension or re-entry point. Check A pending -> B starts -> A fails -> B succeeds, A pending -> B starts -> B fails -> A succeeds, close or cancellation between setup and completion, and a stale completion arriving after newer work.Do not mark a concurrency-sensitive patch Merge-worthy as-is merely because sequential reconnect, retry, failure, and close tests pass. If the code trace proves an unsafe interleaving, conclude from static evidence and request a focused fix and regression test. If ownership remains ambiguous, keep the result preliminary and request approval for the smallest decisive runtime probe.
Preliminary assessment, name the concern, propose the smallest decisive probe and control, and ask the user for approval to run it.Do not issue a definitive positive maintainer decision while a decision-relevant runtime concern remains unresolved. If the user declines the probe, keep the result preliminary and state the exact confidence limitation.
After explicit approval, run only the smallest probe needed to resolve the stated concern. Exercise the real public or internal path and include a base, release, or known-good control when relevant. Do not stop at a happy-path smoke check when failure behavior determines the decision. Return to the user for separate approval before expanding materially beyond the approved probe.
For latency, timeout, buffering, backpressure, or cleanup, measure an observable elapsed-time or state transition where feasible. Do not assume that a mocked unit test exercises real scheduling or provider behavior. Prefer a local probe first; use an approval-gated live-service probe only when local evidence cannot settle the decision.
Use $runtime-behavior-probe only when the user explicitly invokes it or approves using it for the proposed runtime work. Preserve its environment-variable, live-service, cost, cleanup, and reporting gates. Ordinary maintainer review must not depend on that skill.
For validation, cleanup, retries, interruption, background work, or concurrency:
Stop when additional evidence is unlikely to change validity, severity, or maintainer action.
Read the evaluation framework when validity, severity, or merge value is not immediately clear.
Assess claim validity, realistic reach, consequence, breadth, frequency, recoverability, compatibility, and severity. Keep observed facts separate from inference and name missing evidence that could change the result.
Report the Need evidence status before classifying the need as a capability gap, ergonomics or discoverability gap, unsupported use case, no demonstrated gap, or a defect in supported behavior. Do not assign practical impact to the absence of the requested mechanism when an existing supported workflow already produces the requested outcome. Do not infer practical importance merely from reachability, API asymmetry, or a technically successful patch.
For a PR, make Severity describe the underlying issue or user need. Report patch-induced regression, compatibility, lifecycle, or maintenance risk separately as Patch risk.
Do not speculate about AI authorship or contributor intent. Identify weak reports through objective evidence: no reproduction, unsupported input, impossible path, duplicated handling, a test that does not exercise the claim, or a fix that is a runtime no-op.
Use one code recommendation:
Merge-worthy as-is and Merge-worthy after focused changes are invalid unless Need evidence is Demonstrated. A bounded set of implementation fixes cannot promote a Plausible but unproven need into a merge-worthy recommendation.
For merge-worthy recommendations, use one repository-readiness status when useful:
Omit readiness for supersede/not-worth-completing recommendations; CI does not change those code decisions. Do not downgrade sound code only because CI is pending, and do not call a PR ready when semantic changes remain.
For competing PRs, make one portfolio recommendation: choose one, choose one after focused changes, combine exact pieces into one destination, replace all with a simpler approach, or merge none. State what should happen to every active candidate.
Always compare the proposed patch with the strongest existing supported approach and at least one alternative: no code change, validation or documentation, a narrower fix, reuse of an existing helper, or a different layer that enforces the invariant consistently. A review is incomplete if it establishes only that the patch works without establishing why the current product cannot meet the underlying need or violates a supported contract, and why this design is preferable.
Choose the assessment language from the current user request and governing repository instructions. Maintainer comment drafts remain English.
Use the matching compact report in the evaluation framework. While runtime approval is pending, use its preliminary-assessment variant and end with the approval request instead of presenting a final recommendation. Keep the report decision-oriented, put unexpected/negative evidence first, and use no more than five evidence bullets by default.
For PRs, put Need evidence before the code recommendation. When the need is not Demonstrated, lead with that result, omit repository readiness, and avoid presenting patch fixes as the primary maintainer action.
When existing functionality or a better alternative materially affects the decision, state it explicitly in the evidence and recommendation. Name the exact supported path, what it does and does not cover, and why it is preferable. Do not bury a Not worth completing or Supersede with a simpler alternative conclusion beneath praise for implementation quality.
When recommending closure, more evidence, focused changes, or superseding a PR, append a polite, complete, copy-paste-ready English maintainer comment. Write the comment as plain markdown without blockquote (>) prefixes or other wrapper markers, so it can be pasted verbatim. Include only merge-blocking work in its required-action paragraph. Do not produce a line-by-line review unless requested, equate passing tests with merge-worthiness, or equate a logically correct patch with practical value.
references/evaluation-framework.md contains the severity rubric, evidence checks, lifecycle review, issue dispositions, PR value checks, documentation threshold, competing-PR framework, maintainer-comment guidance, and compact report variants.© redis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (references) in .agents/skills/maintainer-review of redis/node-redis.
Open the folder on GitHubat commit bba9381
Maintainer 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Maintainer Review this skillredis/node-redis | 18k | — | ~5.5k | Automated safety check: Pass | MIT | |
| Creating Description For Gh PRredis/jedis | 12k | — | ~838 | Automated safety check: Pass | MIT | |
| Mariadb Operator PR Reviewmariadb-operator/mariadb-operator | 1k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| PR Auto ReviewFontWoW/FontWoW.github.io | 158 | — | ~1.3k | Automated safety check: Pass | GPL-3.0 | |
| Code Reviewsortie-ai/sortie | 197 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Databricks CLI PR Checklistdatabricks/cli | 404 | — | ~1.5k | Automated safety check: Pass | Custom licence |
redis/jedis
Generate a clear, concise GitHub PR title and description from the diff between two local git branches, and save it to prDescription.md in the repo root.
mariadb-operator/mariadb-operator
Perform a structured maintainer-style PR review for the mariadb-operator repository.
FontWoW/FontWoW.github.io
Automatically review open GitHub PRs on this repo (FontWoW.github.io) — inspect the diff for correctness, security, scope, and code-quality issues, then approve clean PRs or leave a Persian, Rick…
sortie-ai/sortie
Reviews pull requests in this repository for the defect classes a mechanical checklist misses: documentation that outlived the code it describes, reaction and retry state that leaks or clobbers a…
databricks/cli
Pre-PR checklist for the Databricks CLI repository: run the same format and lint checks as CI, scrub the diff, fill the PR template concisely and add a changelog entry.
bitwarden/ios
Performs comprehensive code reviews for Bitwarden iOS projects, verifying architecture compliance, style guidelines, compilation safety, test coverage, and security requirements.
redis/node-redis
Plan and execute runtime-behavior investigations with temporary TypeScript probe scripts, validation matrices, state controls, and findings-first reports.
redis/node-redis
Add a new Redis command (or command variant) to node-redis end-to-end — the <NAME.ts Command file, its registration with JSDoc in the package commands/index.ts, and a co-located <NAME.spec.ts with…
redis/node-redis
Batch-triage and act on a set of node-redis PRs (or issues) by filter — fan out the maintainer-review methodology across them, present a one-word verdict plus a tldr to the user one at a time for…
redis/node-redis
Analyze master branch implementation and configuration to find missing, incorrect, or outdated documentation in docs/, README.md, and per-package READMEs.
redis/node-redis
Bump the default Redis docker test image (redislabs/client-libs-test) in the shared DEFAULTDOCKERCONFIG and the CI matrix, then force-push the bump-test-image branch and open a PR against upstream.
redis/node-redis
Create the required PR-ready summary block, branch suggestion, title, and draft description for node-redis.
Categories
Review a GitHub issue or pull request URL as a node-redis maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality…. Maintainer Review is an agent skill from redis/node-redis, published by the product's own GitHub organization. Review a GitHub issue or pull request URL as a node-redis maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort.
Maintainer Review fits situations like: assessing issue validity; deciding whether an issue should be prioritized; determining whether a requested feature represents an unmet need rather than a discoverability; judging whether a PR is worth bringing to mergeable quality.
Run `npx skills add redis/node-redis --skill maintainer-review -a claude-code`. Or copy the skill folder (.agents/skills/maintainer-review in redis/node-redis) into .claude/skills/maintainer-review in your project. Claude Code loads it when a task matches its description.
Run `npx skills add redis/node-redis --skill maintainer-review -a codex`. Or copy the skill folder (.agents/skills/maintainer-review in redis/node-redis) into .agents/skills/maintainer-review in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add redis/node-redis --skill maintainer-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/maintainer-review, .gemini/skills/maintainer-review, .github/skills/maintainer-review and .opencode/skills/maintainer-review in your project.
SKILL.md names no scripts, command-line tools or credentials: Maintainer Review is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
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.
Maintainer Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.5k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Maintainer Review: Creating Description For Gh PR (redis/jedis, 12k stars), Mariadb Operator PR Review (mariadb-operator/mariadb-operator, 1k stars), PR Auto Review (FontWoW/FontWoW.github.io, 158 stars) and Code Review (sortie-ai/sortie, 197 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
redis (a GitHub organization, an official publisher) maintains it in redis/node-redis, which has 17,585 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 8, 2026.
Source: redis/node-redis on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.